International Series in
Operations Research & Management Science
Sharan Srinivas
Suchithra Rajendran
Hans Ziegler Editors
Supply Chain
Management in
Manufacturing and
Service Systems
Advanced Analytics for Smarter
Decisions
International Series in Operations Research
& Management Science
Founding Editor
Frederick S. Hillier
Stanford University, Stanford, CA, USA
Volume 304
Series Editor
Camille C. Price
Department of Computer Science, Stephen F. Austin State University,
Nacogdoches, TX, USA
Associate Editor
Joe Zhu
Foisie Business School, Worcester Polytechnic Institute, Worcester, MA, USA
More information about this series at http://www.springer.com/series/6161
Sharan Srinivas • Suchithra Rajendran •
Hans Ziegler
Editors
Supply Chain Management
in Manufacturing and Service
Systems
Advanced Analytics for Smarter Decisions
Editors
Sharan Srinivas
Department of Industrial and Manufacturing
Systems Engineering, College
of Engineering, Department of Marketing,
Trulaske College of Business
University of Missouri
Columbia, Missouri, USA
Suchithra Rajendran
Department of Marketing, Trulaske College
of Business, Department of Industrial
and Manufacturing Systems Engineering,
College of Engineering
University of Missouri
Columbia, Missouri, USA
Hans Ziegler
School of Business, Economics and
Information Systems
University of Passau
Passau, Germany
ISSN 0884-8289
ISSN 2214-7934 (electronic)
International Series in Operations Research & Management Science
ISBN 978-3-030-69264-3
ISBN 978-3-030-69265-0 (eBook)
https://doi.org/10.1007/978-3-030-69265-0
© Springer Nature Switzerland AG 2021
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To the memory of my father, B. Srinivasan,
who always believed in me and supported all
my choices in life.
—Sharan Srinivas
To my husband, parents and my late
grandmother (Dr. A.B Vasanthalakshmi)
—Suchithra Rajendran
To Sarah Madeleine and Ulrike
—Hans Ziegler
Preface
Management of supply chains has evolved rapidly over the last few years owing
to the inception of Industry 4.0, where digitization using Internet of things (IoT),
advanced robotics, and sensors have taken precedence. The traditional supply chain
systems are no longer efficient as these transformations have led to dynamic and
interconnected systems that require robust management capabilities. Moreover,
today’s customers are being exposed to numerous products and services through
the Internet, leading to high expectations requiring superior supply chain systems
with cost-effective, responsive, and sustainable capabilities. Nevertheless, digital
transformation, both in manufacturing and service industries, has led to large
volumes of real-time data which can be leveraged to obtain actionable insights and
drive profitable supply chain decisions. This book focuses on providing an overview
of current trends in supply chain as well as publishing state-of-the-art original
research work dealing with advanced analytical models (predictive and prescriptive
analytic models) for the design, planning, and operation of supply chains in the era
of digitization and Industry 4.0. It intends to empower supply chains with smarter
decisions at all levels and stages.
The key characteristics of this book are as follows:
1. Covers recent trends, developments, and applications in supply chain management
2. Includes a wide collection of analytical methodologies for optimizing key supply
chain decisions
3. Bridges the theory–practice gap in supply chain management
4. Designed, organized, and edited considering non-experts
5. Holistic with contributions from leading academicians and industry practitioners
6. Unified single-source guide
7. Versatile reference book for students, researchers, educators, and practitioners,
alike.
vii
viii
Preface
The vital areas of supply chain management empowered with smart decisions
include:
1.
2.
3.
4.
5.
6.
7.
8.
Supply chain network design
Logistics and distribution
Process optimization
Product life cycle management
Visibility
Risk management
Sustainability
Customer feedback
Book Overview
This book presents chapters from well-recognized international authors across
academia and industry to facilitate holistic and up-to-date knowledge in supply
chain management. The chapter entitled “An Overview of Decisions, Performance
and Analytics in Supply Chain Management” introduces the readers to the concept
of supply chain management and its various facets. Further, it aims to familiarize the
readers with the frequently used terminologies to help them navigate the remaining
chapters. The chapter entitled “Intelligent Digital Supply Chains” covers the current
trends in digital supply chain management such as intelligent visibility, digitization,
and blockchain technology. It sets the context for the research works presented in
the following chapters. Further, to address concerns in both product and service
supply chains, the book provides novel research work in both the sectors. While
chapters entitled “Product Life Cycle Optimization Model for Closed Loop Supply
Chain Network Design,” “Supply Chain Risk Management in Indian Manufacturing
Industries: An Empirical Study and a Fuzzy Approach,” and “Prescriptive Analytics
for Dynamic Real Time Scheduling of Diffusion Furnaces” are related to the
manufacturing industry, the remaining chapters focus on the service sector.
Supply chain management involves every decision taken to optimize the flow of
products, funds, and information in an organization. These decisions can be better
perfected by understanding them at one of the three decision levels – strategic,
tactical, and operational. Strategic decisions are long-term decisions usually taken
by the senior management and mainly include decisions on supply chain network
design, production and sourcing, and information technology. These decisions need
to be carefully accounted for their long-term impact and future uncertainties. The
chapters entitled “Product Life Cycle Optimization Model for Closed Loop Supply
Chain Network Design,” “Supply Chain Risk Management in Indian Manufacturing
Industries: An Empirical Study and a Fuzzy Approach,” “Improving Service
Supply Chain of Internet Services by Analyzing Online Customer Reviews,” and
“Prescriptive Analytics for Dynamic Real Time Scheduling of Diffusion Furnaces”
address some of the critical strategic issues in a supply chain. On the other hand,
Preface
ix
tactical decisions are made every month or quarter and are related to supply chain
planning activities like purchasing, production and planning, transportation, inventory management, and distribution. The chapters entitled “An Integrated Problem
of Production Scheduling and Transportation in a Two-Stage Supply Chain with
Carbon Emission Consideration” and “A Simulation-Based Evaluation of Drone
Integrated Delivery Strategies for Improving Pharmaceutical Service” prescribe the
best course of action for tactical decision-making. Finally, operational decisions are
related to determining the day-to-day tasks needed to satisfy individual customer
orders. They involve activities like allocating inventory, assigning logistics, updating
delivery dates, and placing replenishment orders in response to customer orders.
The chapters entitled “Pro-active Strategies in Online Routing” and “Prescriptive
Analytics for Dynamic Real Time Scheduling of Diffusion Furnaces” provide
insights to make effective real-time operational decisions.
While this book is organized based on the decision level each chapter addresses,
the chapters can also be understood by the methods they employ. The chapters
entitled “Product Life Cycle Optimization Model for Closed Loop Supply Chain
Network Design,” “An Integrated Problem of Production Scheduling and Transportation in a two-Stage Supply Chain with Carbon Emission Consideration,”
Pro-active Strategies in Online Routing,” and “Prescriptive Analytics for Dynamic
Real Time Scheduling of Diffusion Furnaces” leverage mixed-integer linear programming (MILP) techniques to obtain the optimal values for the decision variables
in a supply chain, given practical constraints. Further, the chapters entitled “Proactive Strategies in Online Routing” and “Prescriptive Analytics for Dynamic
Real Time Scheduling of Diffusion Furnaces” integrate heuristics into their MILP
formulations to quality solutions more quickly. On the other hand, the chapter
entitled “A Simulation-Based Evaluation of Drone Integrated Delivery Strategies
for Improving Pharmaceutical Service” evaluates the effectiveness of integrating
new strategies to improve last-mile delivery in supply chain using discrete event
simulation modeling. The chapter entitled “Supply Chain Risk Management in
Indian Manufacturing Industries: An Empirical Study and a Fuzzy Approach”
employs fuzzy cognitive map (FCM) to foresee and manage SC risks. Finally,
the chapter entitled “Improving Service Supply Chain of Internet Services by
Analyzing Online Customer Reviews” uses text analytics techniques like bigram
and trigram analysis on online customer reviews to gain insights to make better
strategic decisions.
Chapter Summaries
The chapter entitled “An Overview of Decisions, Performance and Analytics in
Supply Chain Management” introduces the readers to the concept of supply chain
management and further provides numerous case studies to help them understand
its various facets. Particularly, this chapter focuses on decision levels, enablers,
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Preface
drivers, and varied goals associated with supply chains. Moreover, this chapter also
familiarizes the readers with analytics, setting the stage for the book.
The chapter entitled “Intelligent Digital Supply Chains” reports on how modern
supply chains are leveraging Industry 4.0 technologies to gain end-to-end visibility
and intelligence in their supply chains. Further, it discusses planning, execution,
evaluation, monitoring, risk management, and opportunities from the perspective
of digitized supply chains. This chapter is vital for both academic professionals
who want to understand the tools employed by industry practitioners and industry
personnel who need to catch up with the latest trends.
The chapter entitled “Product Life Cycle Optimization Model for Closed Loop
Supply Chain Network Design” proposes a product life cycle optimization model
for a closed-loop supply chain network design. In environmentally concerning
times, a closed-loop supply chain capable of reclaiming and reusing post-consumer
materials to reduce wastes and dependence on raw materials is crucial. Further,
remanufacturing is efficient and profitable. This chapter proposes an integrated
multi-period optimization model to design the closed-loop supply chain for OEMs.
The authors explicitly model the optimal collection of remanufactured products
through suppliers in a dynamic manner across multiple time periods over the product
life, accounting for demand, quality, and remanufacturability of returned products.
The model is applied to a realistic case study of Apple’s iPhone 7 for a product life
cycle of 8 years.
The chapter entitled “Supply Chain Risk Management in Indian Manufacturing
Industries: An Empirical Study and a Fuzzy Approach” seeks to predict supply chain
risks via early warning signals and implement appropriate mitigation strategies.
With many organizations solely focusing on efficiency to win today’s fierce
competition, supply chains have become more vulnerable to risks. The authors strive
to address this issue using a twofold method. In the first part, the critical factors
of supply chain risk management are understood by developing a framework, and
the views of the practicing managers about risks perceived in their organization
are captured using an empirical study. Second, this information is used to develop a
fuzzy cognitive map to identify all plausible risks in the future, given a risk observed
from a point in time, and suggest proactive mitigation strategies for practicing
managers.
The chapter entitled “Improving Service Supply Chain of Internet Services by
Analyzing Online Customer Reviews” proposes a methodology to leverage online
customer reviews using text analytics to improve service quality and customer
satisfaction. It specifically focuses on Internet service providers (ISPs), as the
service sector is gaining more attention, and the wireless communications industry
has become highly competitive. The chapter seeks to extract the current strengths,
weaknesses, opportunities, and threats (SWOT), along with their corresponding
root causes for several leading ISPs by exploring consumer reviews using text
analytics. The proposed approach consists of four different stages, bigram and
trigram analyses, topic identification, SWOT analysis, and root cause analysis
(RCA), and provides compelling managerial insights. This chapter is critical, as
all businesses can benefit from learning to leverage their online customer reviews.
Preface
xi
The chapter entitled “An Integrated Problem of Production Scheduling and
Transportation in a Two-Stage Supply Chain with Carbon Emission Consideration”
provides an integrated logistics and scheduling problem with the consideration for
carbon emissions in a supply chain system. In today’s highly competitive market,
companies strive to capitalize on every opportunity across supply chain stages.
Further, while doing so, sometimes they ignore the resultant carbon emissions,
leading to growing concerns that mandate focus on carbon emissions as well. This
chapter addresses this challenge by providing a solution that integrates three issues:
selection of subcontractors, scheduling of jobs, and scheduling of logistics with
carbon emission consideration using mixed-integer linear programming (MILP).
The chapter entitled “A Simulation-Based Evaluation of Drone Integrated
Delivery Strategies for Improving Pharmaceutical Service” reports on the effects
of employing drone integrated delivery service at the pharmacy. Drones have
the potential to decrease labor costs, maintenance costs, and delivery times, and
pharmacy offers a unique opportunity for possible drone delivery as quick delivery
can better serve the community. This chapter uses discrete-event simulation to
compare drone-only, truck-tandem, and truck-only delivery methods on a variety
of scenarios to provide the information needed for municipalities to determine the
validity of using drones for pharmaceutical deliveries.
The chapter entitled “Pro-active Strategies in Online Routing” summarizes the
ideas behind recent approaches to efficiently control urgent delivery processes
in real time. With growing customer expectations, businesses periodically face
urgent requests that need to be serviced the same day. Further, since these requests
are usually unknown and will occur dynamically, the transportation plan that is
already in execution must be continuously adapted in real time by applying suitable
optimization approaches. This chapter reports on these approaches that control
urgent deliveries by minimizing the total weighted request response times in order
to minimize resulting customer inconveniences. This chapter is specifically vital for
logistics service providers as more businesses seek to improve their supply chain’s
responsiveness to accommodate express deliveries.
The chapter entitled “Prescriptive Analytics for Dynamic Real Time Scheduling of Diffusion Furnaces” presents prescriptive analytics for dynamic real-time
scheduling (DRTS) of the diffusion furnace. In the semiconductor industry, the
capability to meet delivery commitment and have shorter cycle time is the most
critical challenge in facing global competition. Particularly, wafer fabrication processes involve complex operations, and effectively scheduling them is crucial. This
chapter addresses scheduling in diffusion operation, the lengthiest wafer fabrication
process involved, by proposing a mathematical model. The chapter proposes seven
different apparent tardiness cost (ATC)-based greedy heuristic algorithms (GHA)
for the same.
Columbia, Missouri, USA
Passau, Germany
Sharan Srinivas
Suchithra Rajendran
Hans Ziegler
Acknowledgments
First and foremost, we would like to thank the authors as this book would not have
been possible without their timely and novel contributions. We also acknowledge
their efforts in preparing concise chapters for addressing recent and practically
relevant supply chain issues using advanced analytical methodologies. Besides, we
would also like to thank the authors for promptly revising the chapters based on the
reviewer’s comments.
We want to express our sincere appreciation to the editorial assistant, Surya
Ramachandiran, for his outstanding assistance in several preparatory aspects of this
edited book, especially for typing the first chapter, reviewing the language style,
and suggesting insightful edits to improve the overall quality of the chapters. We
want to thank Christian Rauscher, senior editor of Business, Operations Research
& Information Systems at Springer, for his guidance from the conception to
the completion of this edited book. We also acknowledge the timely help and
information provided by Sayani Dey, production editor at Springer, during different
book preparation stages. Finally, we would like to thank our families for their
encouragement and support in our research endeavors.
xiii
Contents
An Overview of Decisions, Performance and Analytics in Supply
Chain Management . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . .
Sharan Srinivas, Suchithra Rajendran, and Hans Ziegler
Intelligent Digital Supply Chains . . . . . . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . .
Raghav Jandhyala
Product Life Cycle Optimization Model for Closed Loop Supply
Chain Network Design. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . .
Aswin Dhamodharan and A. Ravi Ravindran
1
19
65
Supply Chain Risk Management in Indian Manufacturing
Industries: An Empirical Study and a Fuzzy Approach .. . . . . . . . . . . . . . . . . . . . 107
V. Viswanath Shenoi, T. N. Srikantha Dath,
and Chandrasekharan Rajendran
Improving Service Supply Chain of Internet Services
by Analyzing Online Customer Reviews . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . . 147
Suchithra Rajendran and John Fennewald
An Integrated Problem of Production Scheduling
and Transportation in a Two-Stage Supply Chain with Carbon
Emission Consideration . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . . 165
Bobin Cherian Jos, Chandrasekharan Rajendran, and Hans Ziegler
A Simulation-Based Evaluation of Drone Integrated Delivery
Strategies for Improving Pharmaceutical Service . . . . . . . .. . . . . . . . . . . . . . . . . . . . 185
Alexander Jackson and Sharan Srinivas
Pro-Active Strategies in Online Routing.. . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . . 205
Stefan Bock
Prescriptive Analytics for Dynamic Real Time Scheduling
of Diffusion Furnaces . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . . 241
M. Vimala Rani and M. Mathirajan
xv
Contributors
Stefan Bock Business Computing and Operations Research, Schumpeter School
of Business and Economics, University of Wuppertal, Wuppertal, Germany
Aswin Dhamodharan Tesla Motors, San Carlos, CA, USA
John Fennewald Department of Industrial and Manufacturing Systems Engineering, University of Missouri, Columbia, MO, USA
Department of Marketing, University of Missouri, Columbia, MO, USA
Alexander Jackson Department of Industrial and Manufacturing Systems Engineering, University of Missouri, Columbia, MO, USA
Raghav Jandhyala SAP, Tempe, AZ, USA
Bobin Cherian Jos Department of Mechanical Engineering, Mar Athanasius
College of Engineering, Kothamangalam, Kerala, India
M. Mathirajan Department of Management Studies, Indian Institute of Science,
Bangalore, India
Chandrasekharan Rajendran Department of Management Studies, Indian Institute of Technology Madras, Chennai, India
Suchithra Rajendran Department of Industrial and Manufacturing Systems Engineering, University of Missouri, Columbia, MO, USA
Department of Marketing, University of Missouri, Columbia, MO, USA
A. Ravi Ravindran Pennsylvania State University, State College, PA, USA
T. N. Srikantha Dath Department of Mechanical and Manufacturing Engineering,
M S Ramaiah University of Applied Sciences, Bengaluru, Karnataka, India
Sharan Srinivas Department of Industrial and Manufacturing Systems Engineering, University of Missouri, Columbia, MO, USA
Department of Marketing, Trulaske College of Business, University of Missouri,
Columbia, MO, USA
xvii
xviii
Contributors
M. Vimala Rani Vinod Gupta School of Management, Indian Institute of Technology, Kharagpur, Kharagpur, India
V. Viswanath Shenoi Department of Computer Science and Engineering, Amrita
College of Engineering and Technology, Nagercoil, Tamil Nadu, India
Hans Ziegler School of Business, Economics and Information Systems, University
of Passau, Passau, Germany
An Overview of Decisions, Performance
and Analytics in Supply Chain
Management
Sharan Srinivas, Suchithra Rajendran, and Hans Ziegler
Businesses today face major challenges in terms of greater competition and
increased customer expectations from the global market. With advances in logistics
and information technology, today’s customers are exposed to abundant products
and services offered worldwide. Thus, businesses aim for competitive advantage
and product/service differentiation to stay relevant and profitable. They strive to
build robust supply chains that can help them deliver the right product/service
more quickly and economically than their competitors. The focus of this book is
to provide an overview on the current trends in supply chains as well as present
advanced analytical models to optimize the design, planning and operation of
supply chains. This chapter discusses the concept of supply chain management,
various levels of supply chain decisions and their impacts, drivers and enablers
of a supply chain, types of supply chain, and introduces the role of analytics in
supply chain. Further, this chapter also presents relevant case studies to help readers
better understand various aspects of supply chain management and their importance.
Finally, this chapter links the various supply chain problems addressed in this book
to the key decision levels and analytical methods, thereby setting the stage for the
readers.
S. Srinivas () · S. Rajendran
University of Missouri, Columbia, MO, USA
e-mail: SrinivasSh@missouri.edu
H. Ziegler
University of Passau, Passau, Germany
© Springer Nature Switzerland AG 2021
S. Srinivas et al. (eds.), Supply Chain Management in Manufacturing and Service
Systems, International Series in Operations Research & Management Science 304,
https://doi.org/10.1007/978-3-030-69265-0_1
1
2
S. Srinivas et al.
1 Overview of Supply Chain
A supply chain contains all stakeholders and activities involved in completing a
customer’s order, be it a product or a service. Supply chains are not just restricted
to the suppliers and manufacturers, but any stage directly or indirectly involved,
including transporters, distributors, retailers, and even end-customers. Further, these
stages may even be located in different countries across the world for a company
with a global supply chain footprint. For example, the journey from coffee bean to
a beverage at Starbucks involves such a global supply chain, where coffee beans
and related items are brought from around the world and consigned to Starbucks’s
16,700 retail stores to serve over 50 million buyers across 51 different countries
each week. A single cup of coffee at Starbucks, from the coffee bean, milk, sugar
to the paper cup, can be dependent on as many as 19 countries, connecting some of
the poorest countries in the world to the richest.
A formal definition of supply chain is given by Ravindran and Warsing (2016, p.
2) using two components:
(i) “a series of stages (e.g., suppliers, manufacturers, distributors, retailers, and
customers) that are physically distinct and geographically separated at which
inventory is either stored or converted in form and/or in value.”
(ii) “a coordinated set of activities concerned with the procurement of raw materials, production of intermediate and finished products, and the distribution.”
Supply chains tend to be highly dynamic; in addition to product movement,
they also involve the flow of information and funds between different stages. For
example, e-commerce websites, such as Amazon.com, adopt a series of interrelated
activities, as shown in Fig. 1, to satisfy customer orders. The products from the thirdparty sellers are bought and shipped to one of the company’s fulfillment centers
present worldwide, via air hubs, port facilities, and cross-docks. Cross-docks are
places where goods from inbound transport are removed and then directly loaded
onto an outbound carrier, to facilitate logistics efficiency. Usually, the fulfillment
centers hold required inventory levels predicted by analytical algorithms to enable
express deliveries. The fulfillment centers not only act as a warehouse but also
host facilities to package products and prepare them for delivery when needed.
Following customer orders, the products are usually moved from the fulfillment
center to the nearest sortation center, where they are segregated based on ZIP codes.
Consequently, they are transported to their appropriate delivery stations, where the
products are prepared for their last-mile delivery.
As demonstrated in Fig. 1, real-world supply chains are usually not linear but
complex convergent and divergent networks as a manufacturer may source from
multiple vendors and then supply to numerous distributors. Further, the flow can
happen in both directions and may be controlled by one or more intermediate
stages. Finally, through the whole process, along with the product, both information
and funds constantly flow to make the supply chain efficient. Thus, most supply
chains tend to be complex networks or webs needing holistic management strategies
An Overview of Decisions, Performance and Analytics in Supply Chain Management
3
Fig. 1 Stages of E-Commerce Supply Chain
to function effectively. The Association for Operations Management (APICS)
defines supply chain management as “the design, planning, execution, control,
and monitoring of supply chain activities with the objective of creating net value,
building a competitive infrastructure, leveraging worldwide logistics, synchronizing
supply with demand, and measuring performance globally” (Blackstone 2010, p.
148).
2 Supply Chain Decision Levels
While managing a supply chain, numerous decisions need to be taken regarding the
flow of materials, information, and funds. The various decisions taken in a supply
chain fall under one of the three levels, namely, strategic, tactical, and operational,
based on how frequently that decision is taken and duration over which its impact is
experienced.
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S. Srinivas et al.
2.1 Strategic
Strategic decisions primarily determine how a supply chain is designed and who will
be the partners for the upcoming years. Unlike other decisions, strategic decisions
have a significant long-term impact, and making sudden changes is generally both
expensive and not feasible. However, both the customer demands and market are
ever-changing, making it crucial for the companies to carefully account for the
uncertainties tied with the future before making any strategic decisions. These
decisions majorly decide the structure of the supply chain, method for material
procurement, strategy for allocating resources, and processes undertaken at each
stage. Broadly all the strategic decisions taken in a supply chain fall under one
of the three categories—network design, production and sourcing, and information
technology.
First, in network design, decisions regarding the number and type of facilities
needed, their geographic locations, and their production and storage capacities are
made. Other strategic decisions regarding the mode of transport between these
facilities also fall into this category. Second, in production and sourcing, decisions
on making or buying (whether to outsource or conduct the activity in-house) at each
stage of a supply chain are taken. Moreover, decisions on the selection of vendors,
sub-contractors, and other alliances also belong to this category. Finally, strategic
decisions can also be related to managing information technology infrastructure.
Decisions like what type of information systems are needed, and whether to develop
them internally, buy the related commercially available version or employ the freely
available open-source alternatives are taken here.
An American multinational food and beverage corporation, PepsiCo, is an apt
example of how effective strategic decisions can better satisfy customers. While
PepsiCo’s best-known products were carbonated drinks packaged in metal cans or
plastic bottles, the change in customer’s preferences towards nutritious food and
environmentally friendly products created a challenge. To satisfy this new set of
customer preferences, PepsiCo took the strategic decision to empower alternatives
like Naked Juice and O.N.E Coconut Water (Forbes 2016). The company sourced
ingredients from across the world, including certified non-GMO, fresh, and organic
alternatives. Further, to enable the production of these products, global supply
chains were set-up with refrigeration capabilities throughout the chain. Finally, to
satisfy environmentally conscious customers, Naked Juice products were packaged
in recyclable bottles. Furthermore, these bottles were explicitly designed in cuboidal
shapes to improve packing efficiency during transportation, and the modes of
transportation employed were also changed to reduce the overall carbon footprint.
Chapters “Product Life Cycle Optimization Model for Closed Loop Supply
Chain Network Design”, “Supply Chain Risk Management in Indian Manufacturing
Industries: An Empirical Study and a Fuzzy Approach”, “Improving Service Supply
Chain of Internet Services by Analyzing Online Customer Reviews”, and “An
Integrated Problem of Production Scheduling and Transportation in a Two-Stage
An Overview of Decisions, Performance and Analytics in Supply Chain Management
5
Supply Chain with Carbon Emission Consideration” address some of the key
strategic issues in a supply chain.
2.2 Tactical
Tactical decisions, unlike strategic choices, have a relatively moderate impact.
Typically, these decisions are related to supply chain planning activities and are
made every month or quarter. Due to the shorter time frame, these decisions face
lesser uncertainties, though not insignificant. However, companies can leverage
better prediction tools to mitigate these medium-term uncertainties and even alter
their decisions with relatively more ease. Specifically, strategic decisions taken
during the design stage can be capitalized to optimize the supply chain and meet the
changing customer demands and market conditions. The tactical decisions required
for planning the supply chain activities can be grouped into a few broad categories
such as
• Purchasing: Decisions on the quantity of materials (supplies) to procure as well
as the time to order.
• Production planning: Decisions related to the quantities needed to be produced
over different time periods to meet varying demands are made.
• Transportation: Decisions related to scheduling shipments of raw materials,
intermediates, and final products
• Inventory Management: Decisions on how much supplies should be stored to
mitigate shortage risks while keeping inventory costs minimal.
• Distribution: Decisions that aim to coordinate the distributor replenishment
schedule with the production capacity to make the product or service available
for the customer at the right time
IKEA, a multinational furniture retail company, is a success story on how
effective tactical decisions can revolutionize a business. IKEA is well-known for
providing a wide variety of home furniture at very affordable prices. This is
particularly made possible through innovative and optimized decisions in inventory
management. For example, IKEA stores observe a “cost-per-touch inventory”
principle, where the company seeks to reduce the cost-incurred by cutting down
on the number of times it handles (touches) a product (TradeGecko 2018). First,
IKEA stores are equipped with showroom inventories from where the customers
on selecting their products can retrieve their packages and take them home
by themselves. Second, apart from showroom inventory, the store also features
reserve inventories of two types—high-flow and low-flow inventories. The highflow inventories are filled with reserve stocks of fast-moving products where more
frequent handling takes place. However, by equipping these high-flow facilities with
automated storage and retrieval systems, IKEA could further cut-down handling
costs efficiently. All the cost-cutting made through the tactical decisions mentioned
here is reflected in the low price of the products available for IKEA customers.
6
S. Srinivas et al.
Chapters “An Integrated Problem of Production Scheduling and Transportation
in a Two-Stage Supply Chain with Carbon Emission Consideration” and “A
Simulation-Based Evaluation of Drone Integrated Delivery Strategies for Improving
Pharmaceutical Service” prescribe the best course of action for tactical decisionmaking.
2.3 Operational
Operational decisions are short-term choices (such as day-to-day operations) characterized by low uncertainty and expenditures. A supply chain is pre-determined by
the strategic and tactical decisions, and the operational decisions do not impact its
configuration nor planning policies. However, operational decisions can optimize
performance at an individual order level within the constraints fixed by the previous
decision levels. The focus is to deliver to the inbound customer orders in the most
effective manner. Operational decisions are related to activities, including allocating
production or inventory in response to customer order, selecting a date for delivering
the product or service, updating the pick-up task list used at the warehouse,
assigning appropriate shipment methods, and finally, placing replenishment order
to maintain inventory.
The importance of operational decisions can be emphasized using the case of
Target Corporation, one of the largest retailers in the United States. However, the
company could not successfully penetrate into the Canadian market. While there
are many reasons attributed to the Canadian stores’ failure, ineffective operational
decisions played a significant role. These stores ran out of stocks within the initial
days of their opening as enough replenishment orders were not being placed.
The empty shelves were very disappointing for the eager customers expecting
an abundance, as found in the US stores (Fortune 2015). Even during the latter
days, the store faced inconsistent supplies at the distribution centers and the retail
stores. The flow of individual products was not appropriately tracked, entries were
miss-understood, and even the demand forecasts were not accurate. These lead
to inefficient operational decisions that were expensive both in terms of storage
cost and customer satisfaction. Finally, all the customer resentments cumulated
and reached a stage from where recovery seemed far-fetched. Within 2 years, the
company shut down all of its 133 Canadian stores and incurred $2.5 billion in losses.
Chapters “Pro-Active Strategies in Online Routing” and “Prescriptive Analytics
for Dynamic Real Time Scheduling of Diffusion Furnaces” provide strategies to
make effective real-time operational decisions.
An Overview of Decisions, Performance and Analytics in Supply Chain Management
7
3 Supply Chain Enablers and Drivers
Enablers can be understood as things that are needed to achieve a goal. Marien
(2000) reports four enablers that are needed for the effective functioning of a supply
chain, namely
• Organizational infrastructure: It is crucial for an effective supply chain as it
determines how the different stages coordinate together to accomplish its goals.
The key concern here is organizing supply chain activities, both within the firm
and across firms, for vertical orientation or more decentralization.
• Technology: Two types of technologies, manufacturing technology, and information technology, are necessary to enable superior supply chains.
• Strategic alliances: Establishing long-term partners is a key enabler for a supply
chain. Alliances specifically play a vital role in a decentralized supply chain
where great power and responsibility are present with the suppliers.
• Human resources: Includes technical and managerial employees with a holistic
understanding of supply chain management concepts and tools. These employees
are needed to design and operate an effective supply chain.
While enablers support the smooth functioning of a supply chain, the drivers
are areas of crucial decision making. The four major drivers of a supply chain, as
described by Ravindran and Warsing (2016, pp. 7–9) and Chopra and Meindl (2013,
pp. 41–42), include inventory, transportation, facilities (plants and distribution
centers) and suppliers. These drivers do not function independently but interact
with each other to establish the overall performance of a supply chain. For example,
setting up facilities in remote places away from major cities may reduce rental costs,
but will increase transportation costs and affect delivery time. Similarly, procuring
and storing supplies in large quantities can reduce cost in terms of raw material and
transportation and even improve customer satisfaction levels, but will drastically
increase inventory storage costs. Hence understanding how these drivers interact
and making efficient trade-offs between them is crucial to achieving superior supply
chain performance.
4 Types of Supply Chain
Each company has a competitive strategy that involves satisfying the needs of
the customer belonging to a particular segment. The overall supply chain and its
individual stages need to align with this strategy. For example, customers of a
supermarket prioritize availability and variety over the price of the products. These
customers are willing to pay higher prices, provided they can buy everything from
vegetables to pastries at the same place. Hence, these stores have robust supply
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S. Srinivas et al.
chains that provide a vast range of 15,000 to 60,000 SKUs. While on the other
hand, customers of limited-assortment stores want lower prices and are ready to
compromise on variety. These stores tend to have cost-effective supply chains that
offer a limited range of items (fewer than 2000), with many stores not offering
any perishables. The requirements of the customers prioritized by the competitive
strategy must match with the capabilities of the supply chain, and this consistency
is referred to as strategic fit by Chopra and Meindl (2013, p. 21). In case of a lack of
strategic fit, the company must alter the supply chain to meet its competitive strategy
or modify its competitive strategy based on what its supply chain is designed to do
well. Depending on these supply chain capabilities, supply chains can be understood
as one or a mix of the following types:
•
•
•
•
•
•
Responsive
Efficient
Resilient
Humanitarian
Green
Sustainable
Figure 2 illustrates the key capabilities of the different supply chain types
with respect to seven different criteria, namely, profitability, cost reduction, speed,
flexibility, social responsibility, environmental concern, and ethical practice.
4.1 Responsive Supply Chain
Ravindran and Warsing (2016, p. 12) describe responsiveness as “the extent to
which customer needs and expectations are met, and also the extent to which the
supply chain can flexibly accommodate changes in these needs and expectations”.
Therefore, responsive supply chains seek to prioritize service levels over operating
costs. Further, the responsive supply chains tend to follow a push framework where
the supply chain is initiated in anticipation of a customer order instead of a response
to an actual customer order (pull strategy). The push strategy helps companies serve
their customers quickly but at the cost of higher inventory costs, including wastages.
The characteristics of responsive supply chains include:
•
•
•
•
•
Short delivery time
Wide product varieties
Provision for customized orders
High reliability
Superior service quality
One of the industries that heavily depend on the responsiveness of their supply
chains is the fashion industry. To catch up with changing seasonal trends, the
companies rely on quick and flexible supply chains. These companies prioritize
flexibility, reduced lead time, and timely distribution over cutting costs. For this
An Overview of Decisions, Performance and Analytics in Supply Chain Management
9
Fig. 2 Capabilities based on supply chain type with respect to different criteria
reason, these companies prefer to have an in-house manufacturing facility despite
low-cost manufacturing options in other countries, which might compromise ontime performance or flexibility.
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4.2 Efficient Supply Chain
Not every competitive strategy prioritizes responsiveness. In the case of stationery
manufacturers, where both the product variety and its demand are mostly standard,
responsiveness may not be the key. For instance, adopting a flexible supply chain
that manufactures custom stationery in small batches and then ships them using
an express transporter only makes the stationery unnecessarily expensive, leaving
the customers dissatisfied. In such scenarios, a cost-efficient supply chain is vital.
Efficiency is the output obtained per unit input, and in the case of a supply chain,
it is the ratio of the revenue generated to the cost incurred. Thus, the sole goal of
an efficient supply chain is to minimize the costs. The stages where supply chains
focus to cut-costs include:
•
•
•
•
•
Raw materials procurement
Inventory holding
Manufacturing
Transportation and distribution
Facility operations
Efficient supply chains tend to follow a pull strategy that is practiced in
environments where the demand is known. Here the supply chain is only reactive
to the actual customer order. Such efficient supply chains tend to hold fewer
inventories and carry a level load in warehouses to minimize costs associated with
picking and packing. Efficient supply chains have their drawbacks as well. For
instance, to reduce costs, product offerings need to be standardized. This affects
variety and personalization capabilities, which decreases responsiveness. In fact,
for every strategic decision to increase efficiency, there is usually a compromise on
responsiveness. This relationship can be observed in the responsiveness-efficiency
tradeoff frontier described by Ravindran and Warsing (2016, p. 9–10) and Fisher
(1997, pp. 105–117), as shown in Fig. 3.
Fig. 3
Responsiveness-efficiency
trade-off frontier
An Overview of Decisions, Performance and Analytics in Supply Chain Management
11
4.3 Resilient Supply Chain
In recent years, a supply chain’s capability to anticipate and handle disruptions
(i.e., resilience) has taken importance. This change is primarily due to two reasons.
First, today’s world has grown into a highly interconnected global village where a
small disruption at a particular place is transmitted to the entire world. For example,
earlier, an epidemic was usually contained within a region. However, today, owing
to high connectivity, epidemics quickly spread and become a global pandemic like
the novel coronavirus disease 2019 (also referred to as COVID-19), causing a
worldwide disruption. Secondly, today’s supply chains have become truly global,
with each stage located in a different country altogether. Hence a small workers’
strike at one of the countries can stop the entire global supply chain. Thus, in a
world of globalization where disruptions are felt everywhere, it is essential to design
supply chains by keeping resilience as a priority.
In recent times, the COVID-19 global pandemic highlighted the importance of
a resilient supply chain. Deloitte (2020) reported that the companies that could
perform well during the pandemic were the ones that invested in supply chain risk
management. These companies diversified their supply chain from a geographic
perspective to avoid risks from disruptions caused in any one country. Further,
they multi-sourced vital components to reduce dependency on any one vendor.
Finally, they also considered inventory plans that allowed for buffers needed to
manage unprecedented disruptions. On the contrary, the companies that scrambled
were highly dependent on a specific geography or a particular supplier for vital
commodities. A singular focus on cost-cutting caused negligence towards resilience,
making these supply chains brittle and vulnerable during disruptions.
4.4 Humanitarian Supply Chain
Barve and Yadav (2014) describe the humanitarian supply chain as the “flow of
relief aid and the related information between the beneficiaries affected by disaster
and the donors so as to minimize human suffering and death”. In a humanitarian
supply chain, the customers include the affected people and the intermediate storage
facilities, while the supplies include relief aids like materials, logistics, and even
volunteers. Since humanitarian supply chains face a lot of unknowns, uncertainties
and need coordination among numerous stakeholders (like donors, volunteers,
government, NGOs, and military), along with relief aids, the effective flow of
information is vital. This type of supply chain is complex as they tend to have
limited infrastructure and other resources, making them dependent on donors and
volunteers.
Humanitarian supply chains need to be highly responsive to the disaster type and
its changing phases. While they may be newly established during a particular crisis,
humanitarian works during the COVID-19 pandemic showed the importance of
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leveraging existing supply chains’ flexibility. Highly responsive fashion companies
like Prada, Armani, Gucci, and Giorgio could customize their manufacturing
facilities to produce medical overalls. Moreover, within 72 h from the France
government’s request, Louis Vuitton, a French luxury conglomerate, converted its
perfume factories to manufacture sanitizers and provided pandemic support (Vogue
Business 2020).
4.5 Green Supply Chain
With increasing customers showing concern for the environment, companies seek
to identify and incorporate environmentally friendly practices in their supply chain
to gain competitive advantage. Further, the government imposed environmental
regulations that have made it imperative for companies to build greener supply
chains, especially in countries like France, Spain, Morocco, and Kenya. Building
a green supply chain requires a unified effort from all the stakeholders and stages.
Manufacturers must work alongside both the suppliers and customers to enable
their environmental goals. These environmental goals usually include reducing solid
waste, effluent waste, air emissions, and usage of toxic materials.
While employing a green supply chain can give a competitive advantage, there
still are concerns about whether they will translate into substantial improvement
in profits or market share. However, employing environmentally harmful practices
have impacted businesses negatively. For example, Nestle, a global food processing
company, had to face a myriad of issues when Greenpeace International held the
production of Nestle’s confectionery product, KitKat, responsible for deforestation
(Purkayastha and Chaudhari 2012). The company was accused of destroying
precious rainforests to increase palm plantations and palm oil produce needed to
manufacture their confectionary. Further, by facilitating high impact social media
campaigns, Green peace could pressurize Nestle to stop sourcing palm oil from
Sinar Mars, an organization accused of illegally clearing rainforests, and source its
palm oil responsibly. Similarly, Unilever too had to stop purchasing palm oil from
controversial vendors.
4.6 Sustainable Supply Chain
With dwindling resources, today’s supply chains need to focus on social and environmental facets along with common economic goals to achieve sustained growth.
These three dimensions of sustainability are together referred to as the “triple bottom
line (3BL)”. The goals of a sustainable supply chain can be understood as the
following:
An Overview of Decisions, Performance and Analytics in Supply Chain Management
13
• Economic dimension—This is primarily concerned with generating higher profits and achieving growth.
• Social dimension—The focus here is on improving employment opportunities,
workplace safety, charity, and overall community wellbeing.
• Environmental dimension—This deals with aspects involving global warming,
ozone layer depletion, climate change, different types of pollution, and ecological
preservation.
One of the pioneers in sustainability, Ben & Jerry’s, could successfully incorporate all the three dimensions in its mission (Performance Magazine 2020). As
early as 1989, the organization opposed the use of recombinant growth hormones
to prevent harsh financial impact on family farming. The company introduced the
“Caring Dairy” program and established Fair Trade prices to support its farmers
in conducting sustainable farming practices. The organization also established Ben
& Jerry’s Foundation to motivate its employees to give back to their societies.
Further, the company also invested in sustainable packaging solutions. With all
its sustainability endeavors, Ben & Jerry’s is considered as an example of how
prioritizing sustainability helps businesses build a well-liked brand.
5 Impact of Industry 4.0 on Supply Chain
The current industrial revolution, Industry 4.0, is about integrating physical and digital systems to enable effective decisions that require minimal human supervision. It
focuses on inter-connectivity using technologies such as the internet of things (IoT),
cloud computing, artificial intelligence (AI), and advanced robotics. While these
intelligent technologies have transformed numerous areas, they specifically have
a profound impact on supply chains. Industry 4.0 has led to major improvements
in different supply chain stages, including procurement, inventory management,
logistics, production, and retailing, by facilitating process integration, automation,
digitization, and analytical power. The ability to exchange data and make decisions
is particularly useful in supply chain as they have a dynamic network consisting of
multiple stakeholders and stages needing collaborative decisions at every level.
Despite numerous benefits, a majority of companies are yet to adopt Industry
4.0 technologies into their supply chain due to doubts on return on investments,
struggle to find qualified talent to implement and maintain these systems, shortage
of financial resources, concerns over data security, lack of information technology
infrastructure or even due to the sheer lack of knowledge about its benefits (Horváth
and Szabó 2019). However, there have indeed been numerous success stories. One of
the best examples is the widespread adoption of intelligent tools that can accurately
predict consumer behavior and guide demand planners. Demand planning is a
manually intensive week-long task repeated at the beginning of every month (SAS
2018). Demand planners spend 40% of their time cleaning and managing inventory
and sales data, an additional 30-40% of time reviewing forecasting models and fine-
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tuning them, and 10% of time reporting their findings (SAS 2018). This further
becomes cumbersome when a supply chain deals with numerous products across
different categories. Nevertheless, with developments in analytical technologies,
supply chains leverage AI tools to effectively handle most of these manual and
repetitive tasks, leaving time for demand planners to focus on other value-adding
work.
Apart from demand planning and then sourcing from reputed suppliers, ensuring
the quality of a product/service is a vital aspect of supply chain management
(Romano and Vinelli 2001). With improvements in capturing and analyzing sensory
data, companies continuously monitor their product/service to ensure consistent
quality. For example, manufacturing industries capitalize on image processing
and machine learning developments to automate visual inspections, which would
otherwise be time-consuming, labor-intensive, and prone to human error. Like few
modern call centers, other service providers also leverage voice recognition and
natural language processing tools to ensure their agents’ quality and drive customer
satisfaction. Further, with developments in IoT technologies, not only quality is
monitored but also controlled. For example, many plastic goods manufacturing
companies continuously monitor their manufacturing process at all stages. When
any temperature deviation or product abnormality is identified, the production
process enabled using IoT is automatically modified to produce ideal results. Thus,
Industry 4.0 also supports supply chains by enabling consistent quality, cuttingdown costs, improving resource utilization, and driving process efficiency.
Due to the developments in Industry 4.0 technologies, specifically IoT and
sensor technology, both manufacturing and service organizations generate massive
amounts of industrial data. To a large extent, the success of a supply chain lies in its
ability to effectively capitalize on this data by leveraging advanced analytical tools
to gain intelligence. Supply chain analytics deals with the use of quantitative models
for data-driven management of all the decision levels—from helping in supply
network design and vendor selection at the strategic level to managing procurement,
inventory, demand planning, and logistics at tactical and operational levels.
The traditional sources of data for supply chain analytics include radio frequency
identification (RFID) systems that automatically track items attached with RFID
tags, global positioning systems (GPS) that provide the locations of shipments in
transit, and barcode enabled systems that capture transactions. Further, supply chain
analytics also heavily depends on data visualization techniques to report its findings
in a human interpretable manner. Depending on the complexity and value addition,
supply chain analytics can be classified into three types—descriptive, predictive,
and prescriptive.
• Descriptive analytics uses aggregation, visualization, and mining of historical
data to provide insights on prior trends and patterns. It helps supply chain
practitioners to learn from the past and also uncover relationships between
variables, thus empowering them to make better decisions in the future. Apart
from past data, descriptive analytics also uses current data to provide valuable
real-time information and visibility needed to effectively manage the supply
An Overview of Decisions, Performance and Analytics in Supply Chain Management
15
chain. For example, information on the current locations and quantities of
different products in a supply chain can help managers optimize several decisions
such as delivery schedule, transportation modes, and replenishment orders. Tools
such as dashboards, scorecards, and sales reports are key enablers of descriptive
analytics.
• Predictive analytics enables an organization to estimate the likelihood of future
outcomes using historical data. It relies on forecasting and machine learning algorithms to achieve its purpose. In a supply chain, predictive analytics is majorly
used to predict demand, customer purchasing patterns and behaviors, inventory
records, and performances of various supply chain stages. The complexity of the
models deployed as well as the value-addition from predictive analytics is higher
as opposed to descriptive analytics.
• Prescriptive analytics aims to provide the best course of action, given a
particular situation, and also report on the consequence of undertaking such
an action. While descriptive and predictive analytics provide decision support,
prescriptive analytics ventures one-step further with decision automation. For
example, predictive analytics can forecast the demand given historical data,
whereas prescriptive analytics will be able to capitalize on such predictions
to provide the optimal replenishment policy along with its impact on the
inventory costs. Thus, prescriptive analytics provides the highest degree of
intelligence, but is also the most complicated among the three types of analytics.
The key tools for prescriptive analytics are mathematical optimization models,
simulations, and heuristics. Though many companies have employed prescriptive
analytics to automatically optimize production, inventory, and logistics, the use
of prescriptive analytics is still at its early stages (Lepenioti et al. 2020)
This book focuses on introducing the recent trends in supply chain management
as well as the applications of advanced analytical models that impact different
decision levels. While adoption of Industry technologies in supply chains has
numerous facets, improving visibility is one of them. Visibility provides the ability
to track orders and products as they move through the manufactures’ value chain to
the final customer. Chapter “Intelligent Digital Supply Chains” provides a detailed
discussion on visibility in supply chains. Long-term decisions need to include future
uncertainties, and Chaps. “Product Life Cycle Optimization Model for Closed Loop
Supply Chain Network Design” and “Supply Chain Risk Management in Indian
Manufacturing Industries: An Empirical Study and a Fuzzy Approach” strive to
optimize these strategic decisions in the manufacturing sector by proposing a mixedinteger linear programming model and multi-criteria decision-making methods,
respectively. Particularly, Chap. “Product Life Cycle Optimization Model for Closed
Loop Supply Chain Network Design” is concerned with strategic decisions needed
to optimize the product life cycle in a specialized supply chain, while Chap.
“Supply Chain Risk Management in Indian Manufacturing Industries: An Empirical
Study and a Fuzzy Approach” aims to address strategic decisions that aid in
foreseeing exigency and mitigating risks. Chapter “Improving Service Supply Chain
of Internet Services by Analyzing Online Customer Reviews” focuses on the service
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supply chain, specifically that of internet services. It aims to discover strategic
insights from online customer reviews by employing text analytics and root cause
analysis techniques for this purpose. To address the need of the hour, environmentfriendly practices, Chap. “An Integrated Problem of Production Scheduling and
Transportation in a Two-Stage Supply Chain with Carbon Emission Consideration”
proposes a mathematical model for integrated optimization of strategic and tactical
decisions that also accounts for carbon emissions. Chapter “A Simulation-Based
Evaluation of Drone Integrated Delivery Strategies for Improving Pharmaceutical
Service” addresses tactical decisions in service delivery using simulation modeling.
It tries to evaluate the integration of futuristic drones for delivering vital pharmaceutical products. Finally, Chaps. “Pro-active Strategies in Online Routing” and
“Prescriptive Analytics for Dynamic Real Time Scheduling of Diffusion Furnaces”
propose optimization models and heuristics to deal with operational decisions
proactively. While Chap. “Pro-active Strategies in Online Routing” aims to control
urgent logistics in real-time, Chap. “Prescriptive Analytics for Dynamic Real Time
Scheduling of Diffusion Furnaces” focuses on prescriptive analytics for controlling
scheduling in a manufacturing process in real-time.
References
Barve, A., & Yadav, D. K. (2014). Developing a framework to study the various issues of
humanitarian supply chains. In Proceedings of 10th Asian Business Research Conference–
Bangkok, Thailand.
Blackstone, J. H. (2010). APICS dictionary, APICS the Association for Operations Management
(13th ed. revised, p. 148).
Chopra, S., & Meindl, P. (2013). Supply chain management: Strategy, planning, and operation (5th
ed.). London: Pearson Education.
Deloitte. (2020). COVID-19 - Managing supply chain risk and disruption.
Fisher, M. L. (1997). What is the right supply chain for your product? Harvard Business Review,
75, 105–117.
Forbes. (2016). PepsiCo’s practical application of supply chain resilience strategies. Retrieved
from https://www.forbes.com/sites/stevebanker/2016/10/01/pepsicos-practical-application-ofsupply-chain-resilience-strategies/#1d81bfea6293
Fortune. (2015). Why target failed in Canada. Retrieved from https://fortune.com/2015/01/15/
target-canada-fail/
Horváth, D., & Szabó, R. Z. (2019). Driving forces and barriers of Industry 4.0: Do multinational
and small and medium-sized companies have equal opportunities? Technological Forecasting
and Social Change, 146, 119–132.
Lepenioti, K., Bousdekis, A., Apostolou, D., & Mentzas, G. (2020). Prescriptive analytics:
Literature review and research challenges. International Journal of Information Management,
50, 57–70.
Marien, E. J. (2000). The four supply chain enablers. Supply Chain Management Review, 4(1),
60–68.
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Performance Magazine. (2020). Ben & Jerry’s – An example of how to integrate sustainability
in business. Retrieved from https://www.performancemagazine.org/ben-jerrys-sustainabilitybusiness/
Purkayastha, D., & Chaudhari, A. (2012). Greenpeace, nestle and palm oil controversy: Social
media driving change? IBS Center for Management Research.
Ravindran, A. R., & Warsing, D., Jr. (2016). Supply chain engineering: Models and applications.
Boca Raton, FL: CRC Press.
Romano, P., & Vinelli, A. (2001). Quality management in a supply chain perspective. International
Journal of Operations & Production Management, 21(4), 446–460.
SAS Institute. (2018). Assisted demand planning using machine learning for CPG and retail.
TradeGecko. (2018). IKEA supply chain: How does IKEA manage its inventory? Retrieved from
https://www.tradegecko.com/blog/supply-chain-management/ikeas-inventory-managementstrategy-ikea
Vogue Business. (2020). Luxury’s war effort against coronavirus. Retrieved from https://
www.voguebusiness.com/companies/european-luxury-fashions-war-effort-against-coronavirus
Intelligent Digital Supply Chains
Raghav Jandhyala
1 Introduction
Supply chains are growing increasingly complex with global, multi-enterprise and
multi-channel operations to meet the ever-growing customer expectations and need
for individualized products. In this intelligent era, supply chains need to be digitized
in order to be responsive, sustainable, profitable and resilient to disruptions. The
flow of information across all aspects of the supply chain from strategy to execution
and the collaboration across inter- enterprises and cross functional units are a
necessity of modern supply chains. An end-to-end visibility of connected supply
chain data is key to analyze, sense and respond to changing market situations. In
this chapter, we will cover the digitization of supply chains and intelligent visibility
with a focus on the following topics:
•
•
•
•
•
•
•
•
•
•
•
Digital supply chains.
Intelligent visibility connecting strategic, tactical and operational processes.
Supply chain control tower—global and local.
Next-gen supply chain analytics.
Supply chain alerts and exception management.
Insight-to-action on supply chain issues.
Supply chain performance metrics—KPIs.
Cognitive supply chains—role of technology in Industry 4.0.
Resilient supply chains.
Evaluation of supply chain assumptions, risks and opportunities.
Collaborative enterprise planning integrating finance with operations.
R. Jandhyala ()
SAP Labs, Tempe, AZ, USA
e-mail: raghavjv@gmail.com
© Springer Nature Switzerland AG 2021
S. Srinivas et al. (eds.), Supply Chain Management in Manufacturing and Service
Systems, International Series in Operations Research & Management Science 304,
https://doi.org/10.1007/978-3-030-69265-0_2
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R. Jandhyala
2 Digital Supply Chains
Digital supply chains are the next generation real time supply chains with seamless
visibility of data and well-orchestrated planning, execution, cross-collaboration,
analytics and intelligence and automation across all operations of a supply
chain.
Digital supply chains enable supply chain business processes such as Designto-Operate. Before we get into the digital supply chain, let us first understand the
challenges and opportunities in a supply chain network.
2.1 Challenges in Digital Supply Chain
Silos of information across different functional units and operational boundaries
with disjointed strategic, operational and execution plans along with disconnected
manufacturing, operations and logistics make it harder for companies to respond to
customer demand shifts. Below are some of the most common challenges faced by
supply chains.
1. Lack of visibility into the health of the supply chain, inventory position across
different locations, in-transit inventory and manufacturing bottlenecks.
2. Disconnected systems involved in supply chains needing manual steps for
integration along with multiple points of failures.
3. Lost sales because of inability to get a full demand and supply picture and run
simulations.
4. Disconnected planning and execution processes and systems.
5. Lack of collaboration across stakeholders involving sales, marketing, finance,
and supply chain with many ad-hoc communications and decisions lost in emails.
6. Offline disconnected excel spreadsheets to store and plan supply chains with
complex hidden rules.
7. Lack of what-if scenario planning where planners cannot wait for information
to be available the next day or next week from separate systems for planning,
execution, reporting and analysis.
The ecosystem of a supply chain in the modern world is complex with customer
networks, supplier networks, logistic networks, contract manufacturers, in-house
network and inter/intra company routing policies. Further, modern supply chains
are complex, owing to global manufacturing, inter-continental distribution network,
and mergers and acquisitions (Kepczynski et al. 2019a, 2019b).
Intelligent Digital Supply Chains
21
2.2 Business Processes Evolution and Trends in Digital Supply
Chain
With the changes in supply chain processes, thin margin and availability of
technology, business processes have changed over time, moving from a traditional
waterfall planning and execution to a more continuous planning and execution.
A closed loop and continuous alignment of planning and execution take the
business to a new level, which was not possible before, connecting the strategic
and tactical processes to the operations/execution and responding to short term
disruptions along with incorporating better customer and product experiences.
Integrated Processes Supply chains have evolved from being fragmented with
departmental planning and execution to a collaborative one with integrated planning
across demand, supply and finance.
Faster Planning Cycles By bringing data to one system of planning and execution,
customers now have a greater visibility of data and can plan faster and frequently
compared to the traditional monthly or quarterly planning processes.
Accuracy of Supply Chain Models With the advances in forecasting and planning
algorithms across demand, supply and inventory, enterprises have shifted from being
supply driven to demand driven by able to sense customer demand and respond to
changes more efficiently.
Improved Experiences With better visibility into customer and product experience, enterprises can support mass manufacturing of products and bring more
customization choices to their end customers.
Figure 1 shows an evolution of supply chain planning from efficiency to
experience. This figure is adopted from SAP positioning of digital supply chain.
Though the evolution of supply chains over the last few decades has dramatically
increased, the next generation, state of the art supply chains are moving towards
bringing:
Autonomous Self-Regulated Capabilities Supply chains that consider historical
trends, internal and external events, and signals to provide recommendations, based
on corporate goals assist the planners with informed decisions and supply chain
executives with strategies to drive the business forward.
Real time Synchronized Planning The traditional planning with monthly and
weekly cadence will evolve towards continuous and real time planning. This will
help to think beyond the traditional demand and supply streams to a new valuechain oriented supply chain view across the end to end supply chain.
Network Collaboration The business processes collaboration and visibility are
increasingly getting better within an enterprise. However, as seen in the recent
COVID-19 pandemic, supply chains are global, and business processes are interconnected with enterprises beyond their own chains. A disruption in one of the
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R. Jandhyala
Fig. 1 Evolution of supply chain planning
enterprises has an impact on the end to end supply chain. Therefore, a network of
inter-enterprises with collaboration and visibility into business processes, enabling
easy onboarding of business partners into a truly scalable network, is required.
2.3 New Business Models Enabled by Digital Supply Chain
By digitizing the supply chain, companies can expand beyond their traditional
boundaries and enter new markets with new business models. With the increasing
competition and continuously changing customer behavior, companies need to
evolve to be innovative and survive the market disruptions. Figure 2 shows the
new business models enabled by the digital supply chain, as adopted from SAP’s
positioning of Digital Supply Chain of One.
Consumer Buying Patterns Consumers in the current digital world have multiple
avenues to shop for the best product and get the best competitive price. Customers
have different buying patterns. They can even search online and buy at store, or
physically view that product in a store and buy online. Customers’ buying decisions
are influenced based on product reviews and trends on social media. The physical
and virtual boundaries are blurring for consumers, and the companies need to be
innovative to meet or exceed consumers’ expectations.
Examples of digital disruption by innovative companies: Uber is the largest
taxi company without owning any vehicle. Netflix is the largest streaming company
without owning movies; however, they later changed their business model to launch
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Fig. 2 New business models enabled by digital supply chain
Netflix movies. Airbnb is the largest accommodation provider without owning any
property.
Wholesale to Consumer Model
Consumer product companies are expanding their business boundaries to open
online channels to reach consumers directly; they now ship their products directly
to the consumers in addition to supplying them to retailers. This also helps them
to sense and respond directly to the changing market or customer buying patterns
and serve better products to the customer. These companies now have a better pulse
on the consumers as they do not solely rely on the retailers to provide the demand
signals.
Retail Supply Chains
Retailers are entering the consumer product market by providing their own brands
and products at a competitive price. Retailers have a better sense of the customer
demand and buying patterns by analyzing the point-of-sales transactions (POS) and
are expanding their supply chain footprints with their own distribution centers and
manufacturing plants for products that can bring additional revenue. For example,
Safeway provides signature branded products across different departmental units.
Walmart offers “Great Value” brand for various products manufactured by Walmart.
Retailers to Logistic Providers
With customer demand ever-increasing to get the product physically within the same
day or the same hour, we see retail companies entering the logistic business. Online
retailers like Amazon are entering the logistic space with their own fleet of logistics
or a mix of inhouse or external logistics partners. Companies like Albertsons own
and partner with logistic providers for same-day delivery of items purchased online.
Best Buy and Home Depot offer in-store pickup and delivery services. Companies
that offer a high level of logistics flexibility see increasing loyalty with customers
and now have firsthand information on customer demand and better visibility of
their inventory position.
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Logistics to Manufacturers
Logistic companies, to be competitive and provide the best customer experience,
are investing in 3D manufacturing to reduce the costs and carbon impact of
transportation wherever it is feasible. For example, auto logistic companies for spare
parts are partnering or establishing 3D manufacturing sites for spare parts in the
hubs where there is more customer demand, to reduce interstate transportation and
optimize intercity delivery.
Manufacturers to Service Delivery
With the digital supply chain and online digital twin representation of a physical
asset, manufacturers can get information on the asset usage and proactively provide
services for maintenance to the consumer. This integrated manufacturing and
servicing model generates additional revenue streams, better customer service, and
reduces costs.
2.4 Design-to-Operate Business Process
Design-to-Operate is a supply chain centric business process that enables companies
to digitally connect the end-to-end supply chain, from designing new products and
assets to managing them throughout the lifecycle from planning to manufacturing,
delivery and operation in the field. Such a business process delivers the speed of
innovation, operational efficiency and service effectiveness necessary to meet and
satisfy customer expectations.
Figure 3 shows an end-to-end business flow of Design-To-Operate scenario
for digital supply chain as adapted from SAP’s positioning of design-to-operate.
It includes design, plan, manufacture, delivery, and operations aspects across the
connected business network of customers, suppliers, manufacturers, assets and
logistics.
Example of Design-To- Operate process for an Automobile customer
1. Design: The design process allows the supply chain specialist to design the next
generation car based on market trends, customer sentiments, and technological
advancements. For example, the market shows a trend in buying electric and
diesel cars. Supply chain designers can create several options for the car like 2
door vs. 4 door, different desired colors e.g. blue matte, or an electric range for
the car. Supply chain designers can also validate their design with a focus group
of customers to perform a conjoint analysis and get their feedback on different
variants of the cars and the price they are willing to pay for different feature
options.
2. Plan: Supply chain planners can then create a feasible plan considering customer
demand, sales history, macro-economic factors, and customer sentiments to
arrive at a future forecast. A supply plan is then created for the demand by
performing capacity and materials planning with decisions to make, buy from
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Fig. 3 Design-to-operate process details
third part or outsource parts of the manufacturing process. A financially viable
plan is created to meet the customer demand.
3. Manufacture: In the manufacturing phase, the different phases of manufacturing
are carried out by collaborating and procuring raw materials from suppliers,
assembling semi-finished goods, and scheduling, in detail, the production of
finished goods, whether in-house or through contract manufacturers. Further, a
digital twin of the car is created and stored as a virtual asset to enable monitoring
by several cross-functional units.
4. Deliver: Finished goods, in this case, cars, are delivered to the customers based
on the customer orders received. To meet the customer service levels for on-time
delivery, the supply chain needs to be digitally connected to the logistic network
to determine the optimal routing, warehouse capacity, and disruptions based on
internal and external events.
5. Operate: The asset received by the customer is continuously monitored, and
key performance indicators (KPIs) like mileage and usage are sent to the digital
twin. The IoT sensors in the asset continuously analyze the asset and predict
any failures. This monitored data is continuously used to build better products
by the manufacturer. Further, if there is a need for maintenance, the customer is
alerted, and the asset is scheduled for a service through proactive and predictive
maintenance of the asset.
3 Intelligent Visibility in Supply Chain Networks
Organizations need to have a unified view of supply chain plans and the execution
of such plans to enable a real time supply chain that profitably balances demand and
supply. Organizations need to react faster to changes and break departmental silos
of information by enabling a connected supply chain that can reduce planning cycle
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time and run scenarios for decision making. An intelligent supply chain enables
organizations to profitably analyze, sense and react to changes in real time to meet
the supply chain goals of
•
•
•
•
•
Increasing supply chain agility
Increasing end-to-end visibility
Reducing cycle time for planning
Increasing on-time delivery and customer service levels
Reducing supply chain cost
In order to realize the end to end digital supply chain, it is important to have an
end to end visibility of all the processes and activities across several stakeholders
and interconnected networks. These include:
•
•
•
•
Visibility of planning processes for strategic, tactical and operational planning
Close loop alignment of planning and execution
KPIs to measure supply chain performance and health
Real time visibility of events that happen during execution.
Figure 4 shows the systems, people, processes and tools across a supply
chain. These include planning types ranging from strategic to execution, business
processes covering sales and operations planning to manufacturing and sales &
distribution, time horizons ranging from years to hours, dimensionality ranging from
aggregated business units to detailed order level, systems ranging from planning
to logistics systems, and supply chain roles ranging from sales and marketing to
operations.
The subsequent chapters will cover some details of each of these areas that make
an end to end supply chain visibility challenging and the benefits of having a truly
integrated supply chain system.
Fig. 4 Supply chain systems, people, process and tools
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3.1 Challenges Achieving End-to-End Visibility
• Multiple systems are involved in a supply chain process with disconnected data
and processes
• Data warehouses with a lack of visibility across all involved parties. Misaligned
data and rigid models.
• Weekly/nightly processing of data to warehouses that delays visibility into
problems
• No proper visibility related to tracking of adherence to the plan.
• Disconnected functional units and processes—siloed and stale data.
• No visibility outside of the four walls of the supply chain network. e.g., lack of
knowledge on whether the supplier has enough inventory, and if they can commit
to the forecasts to keep up customer service levels.
• Lack of visibility to demand, supply, inventory along with insights into strategic
plans, execution, and financial targets.
3.2 Benefits of Having Visibility in Digital Supply Chain
Include
• Reduction in supply chain costs
• Improved customer service levels by proactively addressing lost sales and
demand shortages
• Reduced obsolescence or excess inventory
• Increased productivity without firefighting
• Improved revenue and profit margins
4 Global and Local Control Towers Providing Global E2E
Visibility
A supply chain control tower is a system of record for supply chain visibility
that connects the data across multiple sources to provide an end-to-end visibility,
performance management, exception management, case resolution, and simulation
capabilities. It provides insights into the current problems in the supply chain and
events that result in plan deviations, and it projects the future outcome by mitigating
the risks.
There is an increasing demand in the market for supply chain control towers
that provide real time insights into the internal and external events which affect the
normal operations of the supply chain and reduce the time spent by users to identify,
analyze and resolve issues in a timely manner. With the advances in technologies
and artificial intelligence, there is a need for autonomous supply chain control
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Fig. 5 Global and local supply chain control towers
towers that can self-identify and heal common and repeating pattern issues. Supply
chain control towers are not the systems for planning or execution, but they provide
visibility across the end to end supply chain with a single source of truth for decision
making. For example, a supply chain control tower can provide visibility that a sales
order will not be delivered on time and will affect the order fulfillment KPIs with
the strategic customer. The resolution for this issue is typically performed in the
planning or execution system. Supply chain control towers can be classified into the
global control tower and local control tower.
Global control tower brings focus on key aspects that affect the movement
of materials across the supply chain with visibility to inbounds, outbounds and
in-transits across the internal and external networks. A global control tower may
provide visibility to critical events, and the details of such events are managed in
a local control tower. For example, a local control tower for manufacturing will
have visibility over the production plans, deployments and detailed scheduling,
whereas the control tower for logistics will have information about freight orders,
transportation schedules and other execution data. Once the data is sourced from
multiple systems, it is important to identify which data is relevant for visibility and
only bring that data to a global control tower and manage details in a local control
tower.
A global control tower would primarily have insights into the overall demand,
supply and inventory positions along with insights into execution data related to the
movement of goods. Further, any events from the local control tower, for example,
a truck delay should be visible in the global control tower for visibility into the
issues and decision making. Figure 5 shows local control towers of sales, assets,
manufacturing and logistics providing insights to the global control tower for endto-end visibility, scenario planning and collaboration. This figure is adapted from
SAP’s positioning of Integrated Business Planning (IBP) Supply Chain Control
Tower.
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Supply chain control towers also connect digitally with the extended supply chain
networks including suppliers, contract manufacturers and logistic providers. For
example, a forecast commit scenario between manufacturer and supplier digitally
connects the requirements of raw materials for manufacturing of finished goods
by the buyer with the forecast commit of raw materials from the supplier. Further
visibility of supplier’s inventory allows the buyer to make better decisions on
which customer demands to fulfill. This reduces uncertainty in planning and better
transparency across all stakeholders of the extended supply chain.
Supply Chain Systems of Record
Most enterprises have multiple supply chain systems to manage the end to end
supply chain, each with different purpose. Thus, for an organization, a supply chain
is an interconnected system of systems. Some of the common systems in the supply
chain are listed below:
• One or more enterprise resource planning (ERP) systems as execution system
of records based on different geographic regions or different business units.
Examples include SAP ECC, SAP S4HANA and JDA.
• One or more planning systems as planning system of record based on different
geographic regions or different business units. Examples include SAP Integrated
Business Planning, JDA, Kinaxis, and E2OPEN.
• External systems for collaboration with suppliers and customers. Examples
include SAP ARIBA and Oracle.
• External pricing systems.
• Warehouse management systems.
• Transportation management systems.
• Logistic business network providers.
• Systems from mergers and acquisitions.
• Experience management systems for unstructured data/customer sentiment analysis.
• Data warehouses for standard reporting and KPIs.
• 3PL third party systems.
Although it would be desirable to have a single system which can manage the end
to end supply chain, in reality, the enterprises have multiple supply chain systems.
Visibility to data across all these systems gives insights into supply chain health to
identify potential issues and take corrective measures.
A supply chain control tower connects the data across these multiple sources to
provide an end-to-end visibility and single source of truth across all stakeholders. It
provides instant insights into the demand, inventory and supply across the entire
supply chain with visualizations in the form of supply chain network diagram,
geographic views, heatmaps and charts.
However, it should be carefully managed as to what data should be included
for visibility because bringing data across all these systems into a single system
for visibility would be a massive amount of records with high latency, multiple
points of failure, storage costs, and noise which defeats the purpose of gaining useful
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Supply Chain Control Tower integrates performance data from multiple systems
SAP Supply Chain
Control Tower
SAP Integrated Business
Planning (SAP IBP)
SNC
ERP
ERP
S/4 HANA
S/4 HANA
APO
3rd Party
ERP
S/4 HANA
Inventory
Management
Manufacturing
Insights
3rd Party
3rd Party
Fig. 6 Multiple supply chain system of records
insights from data. Therefore, it is important for end-to-end visibility to bring in the
key information from these different systems with aligned master data and business
semantics and present to the end users at the right level of granularity for analysis
and decision making. Figure 6 is adapted from SAP’s positioning of IBP Supply
Chain Control Tower and shows an example of several supply chain systems across
different regions and business units. These include systems like SAP ERP or SAP
S/4 HANA for supply chain execution, Advanced Planning and Optimization (APO)
for supply chain planning, SAP Integrated Business Planning (IBP) for next-gen
supply chain planning, SAP SNC and SAP ARIBA for collaboration with external
suppliers and several other third party systems that are integrated to SAP IBP Supply
Chain Control Tower.
5 Next-Generation Supply Chain Analytics
Analytics on real-time supply chain data is very much needed to understand the
health of the supply chain and make data-driven decisions. Analytics has been a
very important and integral part of supply chain management, especially needed
to visualize and make a meaningful analysis of the vast amount of supply chain
data. Analytics provides a graphical way to understand the supply chain data,
trends, seasonal and buying patterns, comparisons (cost-to-serve by region), and
view anomalies and outliers (e.g., promotions in sales history) in the data. Analytics
provides end to end visibility of inbound and outbound activities that happen in a
supply chain by integrating important information across multiple source systems. It
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is the basis for managing exceptions and driving actionable results from the derived
insights in supply chain networks.
Supply Chain Data Is a Big Data Problem An enterprise with 1000 customers,
100 products, 10 distribution centers, and a few factories can plan millions of
combinations of products, customers and locations. Add to that the time series data
of past 2 years and predictions on next 3 years in weekly granularity and multiple
plans across sales, marketing, supply chain and finance. This can easily result in
billions of planning data points. As supply chain data grows and changes rapidly
with accumulating sales, production, procurement, transportation and events data,
it is necessary to view the complete picture of a supply chain rather than a narrow
focus on a particular business process or business unit or region.
Even with technological advancements, there are several challenges that organizations face to get decent analytics
1. No real time visibility of data because of long ETL (Extract, Transform and
Load) processes that bring data to an enterprise data warehouse from transactional systems. This can take sometimes days or weeks for users to see the latest
and accurate data in the enterprise data warehouse.
2. Multiple supply chain systems of data (ERP, planning, logistics, warehouse, etc.)
with a lack of integration and semantics matching across systems.
3. IT overhead to create and maintain analytic charts and reports.
4. Fixed analytics with a lack of flexibility to create different chart types or perform
drill-downs or filter data.
5. Offline spreadsheets and charts to multiple copies of data that get stale over time.
6. No single version of truth resulting in poor management review meetings where
the discussions and firefighting are on who has the right data rather than making
decisions based on one version of the truth.
Beyond Visualization The technology advances in storing and analyzing data have
opened new avenues for supply chain executives to view one version of the truth and
make data driven decisions based on real time data. Such an integrated analytics
system allows supply chain analysts to go beyond simple visualizations of charts to
a comprehensive analysis and decision-making process, which includes:
1.
2.
3.
4.
Aligned supply chain KPIs and metrics.
Balances scorecard of key supply chain functions.
Transparency of information and collaboration across various functional units.
Exception management with insights: Automated alerts along with insights into
the root cause and solution recommendations.
5. Predictions of events that disrupt to supply chain. For example, container
shipping late.
6. Real time ‘what if’ analysis and scenario planning to evaluate several solutions
along with the financial impact of the decisions.
7. Connected supply chain sources with an end to end visibility of the network, both
internal and external.
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Fig. 7 Supply chain analytics dashboard
8. Navigation to the source system of records e.g., sales orders in ERP to make
changes to data.
Figure 7 shows an example of a supply chain dashboard with the following chart
types
•
•
•
•
•
•
Geo charts showing manufacturing locations
Heatmap presenting capacity utilization
Horizontal bar chart indicating resource overload
Pie chart showing sales by country
Combination line and bar chart comparing financial plan and forecast projections
Vertical bar chart reporting actual revenue by products segmented to A, B and C
categories.
5.1 Common Analytical Charts Relevant for Supply Chain
Analytical charts can be classified for different functional areas of supply chain e.g.,
demand, supply, inventory, financial reconciliation, or across process types from
strategy, tactical to operational plan across different time horizons, organizational
structures and roles (Kepczynski et al. 2019a, 2019b; Kusters et al. 2018; Chopra et
al. 2013). Figures 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, and 21 are some
of the common charts in a supply chain as adapted from SAP Integrated Business
Planning.
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Fig. 8 Forecast analysis
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Fig. 9 Customer service levels
Fig. 10 Time series characteristics
Fig. 11 Forecast error and bias
1. Forecast analysis: Line chart of forecast vs. actuals comparing statistical forecast
generated from sales history, sales quantity and actuals quantity
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Fig. 12 Plan adherence to financial target
Fig. 13 Inventory types valuation
2. Customer service levels: service levels by product family, customer segment and
location with drill down options to view customer fill rates.
3. Time series analysis: Distribution of time series property across the demand
inputs. This shows a pie chart of data distributed by continuous, seasonal and
trend data.
4. Forecast error and bias: A vertical bar chart by time series comparing the forecast
error and bias in the planned forecast vs. actuals for a 3 month lag.
5. Adherence to financial targets: compare consensus plans to the annual operating
plans and actuals quantity projection from the prior year across all product
families.
6. Inventory types valuation: inventory valuation for the next 12 weeks distributed
across safety stock, cycle stock, merchandise stock, pipeline, and in-transit
inventory.
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Fig. 14 Global inventory position
By Material Type ($USD)
25M
Projected Inventory
Target Inventory
Shortage
20M
15M
10M
5M
0
Finished Goods
Semi-Finished
Raw-Materials
Packaging
Fig. 15 Inventory projection by material type
7. Global inventory position: geo chart of stocking nodes in the supply chain
showing the on-hand position of inventory and locations with excess inventory
and low inventory.
8. Inventory projections by material types: view inventory position by finished
goods, raw materials, semi-finished goods and packing materials comparing
against inventory targets. Provides visibility into projected inventory shortages
below safety stock.
9. Production and distribution network: gives visibility to material flows, in-transit
quantities across the network, late shipments, etc. Figure 16 shows a supply chain
network visualization of all the downstream and upstream nodes of the network
from customers to distribution centers to manufacturing plants to suppliers along
with the production process from raw materials to finished goods.
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Fig. 16 Production and distribution network
250
200
150
100
50
0
Sim - Cap Usage PR
Fig. 17 Capacity overheads
Fig. 18 Customer demand fulfillment: demand vs. supply
10. Resource capacity views: shows the available capacity vs. the capacity usage of
the products. It provides visibility into resources that are over or under-utilized
with drill down capabilities to see which products are consuming more capacity.
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Fig. 19 Process monitoring
Fig. 20 KPI micro charts
Projected Inventory vs Demand
800K
600K
400K
200K
W1
W2
W3
W4
W5
W6
W7
W8
Fig. 21 Projected Inventory and Demand
11. Customer demand fulfillment: includes a horizontal bar chart of customer
demand vs. constrained demand for the product families considering supply
constraints
12. Process monitoring and collaboration: a process chart showing the current state
of the planning process, its due dates, tasks, and people collaborating in the
activity.
13. Key performance Indicators: these include KPI charts for performance metrics.
E.g., plan adherence, customer service levels, days of coverage and out of stock
percentages.
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14. Complete demand, supply and inventory: include charts showing both volumes
and financial valuation of quantities allowing drill downs into demand (forecast,
sales orders, dependent demand), inventory (current and projected inventory by
finished goods, raw, pack and work-in-progress) and supply elements (distribution receipts, in-transit inventory, production planned, production confirmed)
5.2 Business User Friendly Self-Service Analytics
Modern supply chain systems should provide business user friendly analytics
capabilities that help the user to quickly get to the right information without heavily
relying on the IT team to provide the necessary reports, data and models. Usability
of analytics and good visualizations are critical for the adoption of analytics by the
end user community. Below are some of the characters of analytical tools which
help users unleash the business value of data.
• Pre-delivered content with the standard set of dashboards, charts and reports for
most common areas in supply chain
• Self-service creation of charts specific to the user’s needs with very minimal
training.
• Multiple visualizations to represent the data in the right format which could be
both table and chart format.
• Various charting options like network charts, geographic chart, pie charts, bar
chart, dual axis charts and waterfall charts.
• Define and visualize thresholds for the key figure measures on the charts
• Easy filtering options on the data represented in the charts by time ranges or
supply chain dimensions like products, customers, locations, resources, etc.
• Drill down into the details of a specific section of the chart allowing users to
narrow down on an issue.
• Flexible slice and dice of data across different hierarchy levels.
• Easily define and view top N and bottom N values.
• Smart insights into the information beyond what is displayed on the chart
• Annotations on different sections of the chart
• Faster performance on charts with faster refreshes of data.
• Collaboration of analytics with other stakeholders by sharing charts.
6 Supply Chain Alerts and Exception Management
Alerts or exceptions to the planned results occur very frequently in supply chains.
In order to focus attention on the deviations in the supply chain, users require
robust alert management functions that work on the vast amount of data and provide
manageable alerts at the level which can be understood acted upon.
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Examples of alerts include:
• Sales orders with late delivery.
• Capacity overload exceptions where resource capacity usage is projected over
100%.
• Forecast accuracy of <80% for a product group.
• Projected inventory over target inventory for consecutive periods.
• Demand shortage.
• Customer and vendor order fulfilment alerts.
• In-transits overdue.
• Production overdue.
• Raw material shortages.
• Demand fluctuations.
• Supply variability.
Figure 22 shows an example of an alerts overview generated across different alert
categories in a supply chain: capacity overloads, forecast below thresholds, orders
vs. forecast for various product segments. Such alert overview charts provide the
number of alerts generated in each category along with a priority label of high,
medium or low such that the user can focus on the important alerts and drill down
into its details. This figure and others in this section are adapted from SAP Integrated
Business Planning to illustrate the flexible configuration and usage of alerts.
An alert framework should be flexible to allow business users to create their own
alert conditions. The alerts generated are real time on the fly based on the alert
definition. The main components of alert definitions include:
Alert description: qualitative information about the alert. e.g., capacity overload
alert.
Alert calculation levels: the aggregated levels at which the alert results are
calculated by rolling up the data across dimensions and time, e.g., weekly,
resource and location level.
Fig. 22 Alerts overview
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Time horizon: the level and period range for the alert to be calculated, e.g., 2020
calendar week 20 to 2021 calendar week 19.
Severity: the priority of alerts: high, medium, or low.
Alert rules: flexible conditions that can be defined by the user. The conditions can
be defined as absolute conditions, e.g., capacity utilization over 100%, or relative
conditions, e.g., projected stock >110% max inventory. Alert conditions can be
further defined in rules groups with or/and on alert conditions
Alert impact key figures: the measure of the impact of the alert: e.g., cost of
overstock.
Alert charts: the additional analytical charts attached as part of the alert definition
to understand the alert situation better.
Figure 23 shows the results of alerts generated by running an alert definition on
the data. The results show the combinations of resource, location and time for the
alert and its impact value. e.g. resource 1011 for location 1010 in week 2020 CW44
has a capacity utilization of 102%.
Alerts get notified to the user through emails or are shown in the user interface
based on the user’s preference. Users can take further actions from the generated
alerts. For example, snoozing the alert or defining a case to manage the mitigation
of alert with other stakeholders.
Machine Learning for Alert Recommendations
Alerts conditions in the alert framework are typically defined as static rules. It works
if data is not changing and the thresholds can be well defined. However, many
planners find it hard to define the right thresholds and rules for alerts, leading to
too little or too many alerts if not defined correctly.
Machine learning can perform pattern recognition on the data to detect anomalies
and report as alerts. One common outlier technique is to use DBSCAN unsupervised
learning clustering method on the data. DBSCAN detects outliers from the data
points that do not fit to the clusters. Typically, Planners have to manually provide
the lower and upper threshold to detect outliers. However, with machine learning,
the system can identify outliers based on the similarity of the key figure value in
other time buckets.
Fig. 23 User-defined alerts results
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Fig. 24 Insight to action
7 Insight to Action
True control tower systems provide insight to action, while allowing supply chain
analysts to visualize information, perform analysis, view exceptions and their
impacts and finally take actions to resolve the issue (Fig. 24).
Scenario: Depleting Inventory
For example, the projected inventory in a global inventory position chart shows that
in the next quarter, the inventory will be depleting and will go below the safety stock
levels. The supply chain analyst needs to analyze what is causing this issue. A drill
down into the specific region and time shows that the available supply is less than
demand for that quarter.
On further analysis, the reason could be that there is a planned factory shut down
for maintenance in that quarter and no additional production. Therefore, the demand
needs to be fulfilled from the existing inventory.
The analysis can further drill down to view which products at which locations
are affected and take corrective measures like rebalancing inventory from other
locations or using a contract manufacturer.
Scenario: Delayed Customer Orders
In this scenario, the control tower provides visibility into the sales orders execution
data and alerts on the orders that are not confirmed on time or confirmed below the
requested quantities. The supply chain planner can then look into the gating factors
or root cause analysis related to the orders with the late delivery and then navigate
to the source system to change the order dates or quantities to reduce the impact on
the customer service levels.
An intelligent visibility application brings together all the steps from insight to
action in a single user interface, without the planner having to login to different
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Fig. 25 Global visibility with alerts
systems containing different sources of information. This drastically improves the
productivity and experience of the user as they can see all information in one place
and make decisions.
The supply chain analyst views the geo-map of the supply chain network with
the connections across different distribution centers, plants and customers. There
are several alerts displayed on the map in North America for the customer service
KPI metric that this person is responsible for. These alerts are caused due to sales
order delays, which the analyst would like to further analyze and understand the
root cause of the disruptions and its impact on customers. Figures 25, 26, and 27
are adapted from SAP Integrated Business Planning to illustrated the intelligent
visibility and insight to action from alerts.
Narrowing down on the alerts, the analysts clicks on these 10 alerts and views
the individual sales orders and the impacted customers. The analyst drills down into
one of the sales orders to get details about the issue and its impact. Here there is a
delay of 3 days for a sales order, and the confirmation is at 90% of the requested
quantity. The root cause or gating factors for the delay are shown on the same screen
(Fig. 26). In this case, there is an insufficient lead time for different components of
the finished good.
The supply chain analyst can further navigate to the source system based on the
document type (Fig. 27), which in this case is the purchase requisition document in
SAP S/4 HANA system. In the source system, the order can be changed to re-plan
the quantities or dates to meet the customer service levels.
In summary, using a single application, the analyst is able to visualize global
network view of customer orders and transportations, view alerts for sales order
delays, analyze the impacted customers, perform root cause analysis and finally
navigate to the source system to take corrective action. This activity, which typically
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Fig. 26 Root cause analysis
Fig. 27 Navigate to source system to take action
takes days to identify and resolve, is now reduced to hours, increasing the user’s
productivity so they can focus on other important activities.
8 Supply Chain Key Performance Indicators
Key performance indicators (KPIs) are the measurable primary objectives of
business across multiple organizational functions. Supply chains are measured
for their performance using KPIs with metrics that are aligned, streamlined and
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transparent across all stakeholders. KPIs can be defined across areas like customer
service, demand, supply, inventory, production, logistics and procurement.
Supply chain data is vast, with several billions of planning combinations and data
points. In order to make a meaningful business representation of the data, KPIs are
necessary both to keep track of the progress and measure against the set objectives
of the business.
The KPIs are reviewed by all roles in the organization from the boardroom to
the shop-floor. C-suite executives have well defined KPIs that measure the business
growth, e.g., revenue margin and customer service levels. Production planners have
KPIs to measure the utilization rate of resource capacity.
KPIs should be measurable, transparent, mutually agreed, value oriented and at
the right business level to facilitate collaboration, communication and adherence to
common goals for an organization. KPIs should be adequate with a proper RACI
matrix across the stakeholders, and defining too many or too few KPIs without
owners of such KPIs will not add much value.
KPIs are visually represented for reporting in dashboards and scorecards.
They are usually easy to understand single metrics with visualizations such as
speedometer, percentage meter, waterfall chart, traffic lights or single values. They
are measured against target values or thresholds.
KPI are driven by organizational objectives, as illustrated below:
• Sales and marketing: improve promotion effectiveness by 20% by aligning
activities across all functional units from commercial, supply chain to finance.
• Finance: topline revenue increase by 10% and margin growth by 6% by
identifying growth opportunities.
• Inventory: reduce out of stocks and balance inventory to reach higher customer
service levels, reduce inventory write-offs by 5mi USD by better planning and
visibility of products with expiring shelf life.
• Demand planning: improve forecast accuracy to 86% from the current 78%,
launch new products faster to the market by improving the NPI (New product
introduction) process.
• Supply chain: reduce the lead time of products from 45 days to 30 days, improve
resource utilization from 82% to 95% by streamlining production activities.
• Process Improvements: improve planners’ productivity by reducing planning
cycle preparation from 8 days to 3 days, increase process automation and improve
process efficiency to better utilize time and critical resources.
• Customer service: increase customer service levels from the current 92% to a
target 96% with improved case fill rates.
Common Supply Chain KPIs
Some of the KPIs commonly used in supply chain across demand, inventory, supply
and production are listed below. These KPIs also bring together the data from
planning and execution to measure adherence to the plan.
Demand KPIs
• Forecast accuracy: it is measured as forecast error (MAPE), where MAPE (mean
absolute percentage error) is the average of absolute percentage error between
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forecast and actuals.
MAP E =
n 100 At − Ft A n
t
t =1
where A(t) is actual quantity at time t, F(t) is forecasted quantity at time t, and n
is the total number of periods
• Forecast bias: it measures the average percentage error, which is the deviation of
forecasts from actuals.
1 At − Ft
n
At
n
MPE =
t =1
where A(t) is actual quantity at time t, F(t) is forecasted quantity at time t, and n
is the total number of periods
Customer Service KPIs
• Projected fill rate: ratio of constrained demand (supply) vs customer demand in
current and future periods.
• Historical case fill rate: measures the ratio of quantities ordered vs. quantities
fulfilled across items ordered.
• Historical line fill rate: a measure of what percentage of items in the order are
fulfilled on time with the right quantity.
Inventory KPIs
• Days of supply: a measure of the number of days the projected inventory will
cover the demand of the subsequent periods
Demand
Projected
inventory
Day of
supply
Day 1
100
300
Day 2
200
200
1.5
0.5
Day 3
300
500
2
Day 4
400
400
2.5
Day 5
100
300
1
• Inventory turns: a measure of inventory cycles for a product which is calculated
as the ratio of cost of goods sold to average inventory, both measured in financial
valuation
• Stock below safety stock: percentage of projected stock below the safety stock
for the future periods.
• Stock above target stock: percentage of projected stock above the target inventory
for the future periods.
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Supply KPIs
• Production adherence: a measure of the ratio of planned production (scheduled
production) to the actual production from the execution system
• Supply shortage: a measure of the missing supply quantity compared to demand
forecast across the combinations of products and locations for a given time range.
• Capacity utilization: the ratio of capacity usage of the resource to its available
capacity.
Financial KPIs
• Total inventory valuation: total valuated cost of all inventory types including
finished goods inventory, raw materials and work-in-progress inventory
• Cost of goods sold: cost to serve the goods sold to customers, which includes
direct and indirect costs related to materials, labor, production, procurement and
transportation.
• Inventory turnover: the number of times the inventory should be cycled during
a year. It is based on valuated annual inventory and annual COGS i.e. ratio of
annual COGS to annual inventory
SCOR KPIs
SCOR KPIs (APICS SCC 2015) are industry standard KPIs provided by APICS that
spans all supply chain business process with a standard set of hierarchical KPIs of
around the six primary management processes: plan, source, make, deliver, return
and enable. Below is an example of perfect order fulfillment KPI.
Perfect order fulfillment: a measure of how the customer orders are serviced
based on other detailed KPIs like
• Orders delivered in full: a measure of whether an order is delivered to the
customer in the right quantities.
• On-time performance: a measure of whether orders are delivered within the
committed dates and to the correct customer location.
• Orders delivered in condition: a measure of whether the orders are delivered with
current documentation, e.g., billing, shipping and other documents.
KPI Templates
Organizations should follow a good template for defining and managing the KPIs.
Each KPI should be structured around one focus area, for example, supplier
performance with well-defined purpose of the KPI. Below is a guideline for defining
KPIs for managing supply chain
• Name: choose a name that provides a clear short definition of the KPI without
ambiguity.
• Definition: provide the business context of the KPI with clarity on the measure
(e.g., ratio, percentage, quantity, etc.) and how the results can be interpreted.
• Category: tag with the primary focus areas to which this KPI belongs, for
example, customer service.
• Calculation: provide a precise calculation using mathematical formulas to clearly
define the calculation.
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• Roles: define the users who are responsible for the KPI along with other roles
players who use this KPI. A RACI matrix of the KPI will help to define the clear
accountability of the KPI.
• Data source: define the sources of data and transformations required to retrieve
and load to the target system where the KPIs are stored or calculated. E.g., sales
orders and deliveries with details from order to line items to schedule lines from
SAP S/4 HANA, JDA, etc.
• Related KPIs: Define how this KPI is related to other KPIs in the same or
different categories. For example, KPI of safety stock projection may depend
on the KPI forecast accuracy.
Interested readers can find more information on supply chain KPI from
references (APICS SCC 2015; Ravindran and Warsing 2016).
9 Cognitive Supply Chains Enabled by Technologies
in Industry 4.0
Advances in technologies have played a key role and continue to transform supply
chains from traditional supply chains to next generation cognitive supply chains. A
cognitive supply chain is proactive to sense supply chain disruptions and is capable
self-correcting, triggering re-planning and providing intelligent recommendations
through advances in artificial intelligence like reinforcement learning where multiple agents work towards the common goal. With technological advancements to
store and process the complex and vast amount of growing big data along with
flexibility with cloud deployments, supply chains are smarter than ever before to
quickly respond in real-time to changing environments.
Figure 28 shows the technologies currently available and being used in enterprise
business applications to transform the business processes, business models and
workspaces. It is adapted from SAP’s positioning of intelligent technologies for
the intelligent enterprise.
We will look at some of the important technology areas that play a major role in
supply chains.
• Big data and in-memory computing
Advances in big data have made it possible to store a vast amount of supply
chain data from multiple sources into data lakes for real time visibility into the
supply chain and real time simulations. Big data provides the much needed 4Vs
for an enterprise:
– Velocity: Frequency of data which can be real time, batch or streaming data.
– Volume: store and process petabytes of supply chain data and aggregate results
on-the-fly.
– Variety: bring together structured and unstructured data.
– Value: enable smarter supply chains by deriving value from the data.
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In-Memory computing,
Big data
Cloud scalability,
elasticity, availability
Internet of things
Machine learning,
Artificial intelligence
Cloud Platform
Blockchain &
smart agents
Mobile &
conversational
3D Printing
Autonomous systems &
robotics
Ubiquitous connectivity, Edge
computing
Sensors built in/
intelligent things
Fig. 28 Technologies driving innovations in supply chains
Advances in in-memory computing with columnar databases bring forward
the real time enterprise with speed and agility to run complex supply chain
operations and do rapid scenario planning. In-memory data management brings
together transactional and analytical data in one place without separate systems,
redundant data copies or multiple data transformations. Big data and in-memory
technologies make it possible for complex analysis of supply chain with data and
processes spanning several systems.
• Cloud computing
Cloud computing, which was once considered a hype, is now a reality and
necessity for many enterprises. It gives enterprises the flexibility to onboard
new SaaS solutions and extend the existing on-premise landscape with a hybrid
approach using the cloud. Several planning and analytical solutions for supply
chain management are now available in the cloud, including the ERP systems for
storing transactional and master data.
Organizations that have adopted cloud see significant benefits with elastic
storage and processing, flexible pricing, easy onboarding, and effective maintenance and security provided by the vendors. This allows organizations to focus
on important business activities and venture into new business models.
Cloud also provides the hyper scalability to store massive amounts of data and
provide massive processing power, which helps enterprises run multiple whatif scenarios to determine the best outcome for the business. Further, the cloud
vendors for supply chain also provide Platform-as-a-service (PaaS) for complete
business suite solutions, including cloud data warehouses, predictive analytics,
collaboration, integration, planning, and process orchestration.
• Internet of Things
Billions of connected, internet-enabled devices now provide a networked
community of people, process, data, and things leading new ways to gain
insights into a business like never seen before. Internet-of-Things has its place
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and significance in the supply chain, especially in the execution areas like
transportation management and digital manufacturing.
In the digital manufacturing area, hundreds of IoT sensors are part of the asset,
e.g., production resource providing asset intelligence. These sensors monitor
the temperature, vibrations, and other performance metrics and can intelligently
sense if the asset needs maintenance. In transportation, IoT sensors monitor
the state and conditions of goods movement and automate material handling
with RFID technology across several logistic hand-over points without human
intervention. For example, in medical devices transportation, IoT sensors can
monitor the temperature fluctuations and severe vibrations that can damage the
equipment. IoT is an essential technology in the Industry 4.0 revolution of supply
chains.
• Blockchain
Blockchain provides the transparency, traceability, and auditability of all
documents and events that happen in the supply chain for material movements.
There have been good use cases for blockchain in supply chains in the areas of
agriculture, logistics and life sciences.
For example, in agriculture, blockchain helps track every stage of the process
for the fresh food items to reach consumer, and in an event of outbreak, trace back
to the origin. In logistics, blockchain helps track every movement of material
across logistic providers, shipping ports and inter-continental trading hubs to
instantly track and respond to delays.
• Machine learning, artificial intelligence and robotic process automation
Intelligence and automation elevate supply chains to focus on high value
activities. The digital transformation of supply chains, together with advances in
big data, algorithms and hardware, have enabled the new AI era of the cognitive
supply chain where systems learn from the data to augment human decision with
intelligent recommendations.
Supply chain systems have traditionally relied on statistical and deterministic
models for forecasting, supply network optimization and several other planning
and execution processes. With machine learning and AI techniques for supervised learning, un-supervised learning, reinforcement learning and deep learning,
systems can analyze patterns in the data, correlate with external data and events
and augment or replace the traditional planning with cognitive planning. For
example, demand sensing augments the statistical demand planning process by
sensing short term demand based on pattern recognition of sales orders, sales
history, demand history and external data like weather and point of sales.
Using robotic process automations, supply chain users can deploy bots that
can carry out mundane tasks of users, thereby freeing up their time for other
productive activities. The bots can carry out tasks like reading email for new
procurement requests, logging into multiple systems for data entry, solving
supply chain alerts, and trigger workflows.
A cognitive supply chain self-learns from the patterns in the data and provides
predictive and proactive insights to the users to make timely decisions before
disruptions occur. It provides:
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1. Automation to eliminate most of the manual processes and interventions based
on exceptions
2. Alerts and suggested actions
3. Automatic execution of corrective measures, self-learned from the users’ actions.
One of the applications of machine learning and automation in cognitive supply
chains is in the area of analytics, transforming the analytics from
Descriptive analytics to
|______ Predictive analytics to
|________ Prescriptive analytics
Descriptive Analytics
Many organizations see the need to view and report on the data and have invested
over years to build analytics using Data Warehouse platforms and powerful analytical and visualization tools. These are descriptive analytics with fixed representation
and visualization of data. Figure 29 shows a dashboard which has both structured
and unstructured data, as adapted from SAP for illustration. By integrating customer
sentiment data, the User is able to see how the negative perception of the product
is affecting the customer satisfaction. Such analytics help users to instantly see the
related data together to make some inferences.
Predictive Analytics
Predictive analytics provide smart data discovery tools for predictive insights into
the data and predicting future outcomes using Machine Learning. This helps with for
proactive analysis rather than being reactive. For example Figure 30 is based on the
customer sentiments analysis, it is predicted what the future customer satisfaction
will be and how it affects the revenue and profitability. This figure is adapted
Fig. 29 Descriptive Analytics
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Fig. 30 Predictive Analytics
from SAP Integrated Business Planning to illustrate predictive analytics. Planners
can then run different strategies to restore back the customer satisfaction while
maintaining profitability.
Prescriptive Analytics
A cognitive supply chain provides the next level of intelligence by sensing the
impact of the signals and applying intelligence to come up with alerts along
with prescriptions or recommendations to solve the alerts. The system evaluates
multiple what-if scenarios and business impact analysis to recommend the best
path forward using techniques like simulation, optimization and machine learning.
In Fig. 31, the system recommends the course of action for supply planning by recommending make-and-buy vs buy options along with impact analysis
on supply chain costs and a ranking for the recommendation. This figure is
adapted from SAP positioning of recommended actions to solve supply chain
alerts.
Evaluation of Supply Chain Assumptions, Risks and Opportunities
Supply chains have vast amounts of data distributed across multiple functional
systems. To manage the business outcomes in a supply chain, a well-supported
decision-making process driven by managing both qualitative and quantitative
information is required. With well-managed process to capture assumptions, risk
and identify new opportunities, organizations can quickly analyze their data and run
meaningful simulations of the business outcomes and reduce vulnerabilities.
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Fig. 31 Prescriptive Analytics
Examples of qualitative business drivers include events, signals, assumptions,
risks, opportunities and promotions.
Signal New qualitative information, including measurable ones like new sales
orders, and non-measurable ones like a bad product review or market trend. These
signals, when qualified as impactful, are considered in a planning cycle.
Assumptions Qualitative business drivers on expected business conditions and
outcomes. These are defined and consolidated across all functional units and tracked
for changes or deltas in each planning cycle. Example: market share steady for Q1
in EMEA region at 18%.
Risks and Opportunities Risks capture supply chain vulnerabilities, e.g., supplier
risk, and opportunities are new chances, e.g., an increase in market share with
competitors going out of business. These drivers are clearly defined for each
planning cycle with its impact, duration, planning level and qualitative attributes
like status, inclusion-to-plan, probabilities.
Compared to assumptions, the risks and opportunities are well defined with
business impact, duration and probability. These are reviewed collaboratively in
business review meetings and the agreed risks and opportunities are included into
the plan.
Events Events are both internal and external activities that are known upfront
(e.g., sports events and holiday seasons—Christmas) or unknown (e.g., supplier
warehouse affected by a hurricane). Events can also be cyclic, which repeats each
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year. Promotions can be thought of as a variant of an event with a short time span,
e.g., 2 with 1 offer and back-to-school promotions.
A well-orchestrated process for managing business drivers has the following
benefits:
• Captures all qualitative drivers like internal or external and short term or long
term across all functional units
• Reacts to vulnerabilities or new opportunities much quicker.
• Increases transparency and trust across different functional units and stakeholders.
• Consolidates drivers across top-down drivers from strategy and finance, with
operational drivers entered by local and regional planners and cross-functional
across different hierarchies.
• Increases productivity by capturing all influencing drivers that impact the supply
chain in one place.
• Collaborates on decisions making on which drivers to be included in planning
cycles
• Improves business review meetings by evaluating multiple scenarios of which
drivers to include in the plan, which is otherwise done offline with a toll on time
and resources.
Process for Managing Business Drivers
A sample business process of managing business drivers, including assumptions,
risks and opportunities is shown in Fig. 32. This can be different for each organization based on its business purpose. Assumptions are typically defined once a
year with collaboration across all the functional units, including commercial, supply
chain, finance and strategy. Assumptions are defined at an aggregate hierarchical
level, example product category along with the influencing drivers, example growth,
market share, competition etc. for each month or each quarter.
During the monthly review meeting, the assumptions are reviewed and updated
with any changes based on the market conditions and actual performance of the
business. Risks and opportunities are defined for each planning phase i.e., demand,
supply, financial reconciliation, etc. Next, the quantitative plans are adjusted to
include the agreed risks and opportunities. Further, any changes to the forecasts are
tagged with the change in assumption. This provides a context for the quantitative
changes by associating them with the assumption changes. During the review
meetings, an assumption changes report is generated, which brings together the
qualitative influencing factor and the quantitative change, e.g., forecast change
associated with it.
SAP integrated business planning product provides the required tools and
functionality to easily manage the business drivers. Figure 33 is an example of
managing risks and opportunities in a driver-based planning application of SAP
IBP that provides the required user interface, sample data model and best practice
content to easily manage risks and opportunities and collaboratively include them
in the plan.
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Fig. 32 Business process to manage assumptions, risks and opportunities
Fig. 33 Planning views to manage different business drivers
The planning views provide the flexibility to define a business driver e.g., risks
and opportunities across multiple planning hierarchy levels, time ranges and driver
impact key figures for the functional area. For example, there can be demand risks
and opportunities defined for the North America region for current and future
quarters; supply assumptions defined for all regions for the next 2 years.
From the planning view, which defines the structure of the business drivers,
planners can navigate to view and define the individual drivers. For example, below
is a summary view of all risks and opportunities defined at the hierarchical level
product family and customer region. Each driver has the following properties:
1. Qualitative information about the driver: this includes name, description, planning cycle and type.
2. Hierarchical level: a driver can be defined for one or more hierarchical levels,
e.g., risk 1 is for product family 1 and product family 2 for all customer regions,
and risk 2 is for product family 3 for EMEA region.
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3. Time range: each driver can be defined for different time ranges at a defined time
granularity, e.g., Jan–May 2020.
4. Impact key figures: these are the qualitative measures representing the impact of
the key figure. For example, opportunity 1 has an impact of 5mi USD in revenue.
5. Qualifying attributes: these include driver attributes like budgeted, status, probability and include-in-plan.
The drivers can be defined in one go at multiple planning hierarchical levels and
time ranges, with a qualitative value for the driver impact key figure. The key figure
value can also be adjusted for different time periods or planning combinations. For
example, opp1 has higher weightage of revenue in the later quarter compared to the
first 3 quarters.
The summary view lists all the risks and opportunities along with analytical
charts to view the impact of the drivers on planning data like top 5 risks, top 5
opportunities, alerts on risks and opportunities above thresholds (Fig. 34). Planners
can review all the risk and opportunities in one go and must maintain drivers by
changing attributes like probability and status or carry forward drivers to the next
planning cycle. Qualified drivers with the right granularity of planning attributes,
which are budgeted and financially viable, and with higher probability, can be
included in the plan by changing the attribute value include-in-plan to 1, and its
effects can immediately be seen in the planning data.
The contribution of the risk or opportunity can immediately be seen in the supply
plan. For example, risk1, risk 2 and opp1 are included in the plan. These drivers are
defined at an aggregated level of the product family and customer region than the
final forecast key figure, which is at the product/customer level. The contribution of
these three drivers is disaggregated from aggregate level to product customer level
Fig. 34 Risks and opportunities summary view
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Fig. 35 Contribution of risks and opportunities on supply plans
of the final forecast key figures and added or negated from the planned forecast
to arrive at the final forecast value that represents the plan considering risks and
opportunities, as illustrated in Fig. 35.
10 Resilient Supply Chains
Resilient supply chains require intelligent visibility and digitization of supply
chains. A holistic approach is required than a piecemeal approach to gain visibility
into the end to end supply chain connecting functional domains like planning,
manufacturing, logistics and execution. This is required to determine conditions that
need to be analyzed, simulate the impact and solutions for those conditions, apply
intelligence to evaluate alternatives that are financially viable, and finally, deploy
the solution without impacting the current execution.
Supply chains are more vulnerable than ever before, and businesses face unforeseen macro and micro supply chain disruptions. Examples include new tariffs, trade
wars, Brexit, hurricanes, pandemics like COVID-19, SARS, etc. Further planned
events such as seasonal sales, sports—FIFA, Superbowl, etc. and other internal and
external events with shifting market demands and supply have a big effect on the
supply chains. Other examples include:
•
•
•
•
Impact on late shipment on customer order fulfillment
Impact of unforeseen asset downtimes on production
Impact of EPA regulations on logistics and distribution of goods
Impact of raw material shortages e.g. impact of fires in Canada influence the
paper pulp used for toilet papers.
• Impact of pandemic like COVID-19 on medical supplies and essential services.
Such planned and unplanned events affect availability of supplies and lead to
long revival times, increased financial risk and labor shortages, for example, COVID
social distancing impacting production, as distancing rules need to be followed at
production lines. Figure 36 shows examples of COVID-19 impact and other supply
chain events that make the supply chains vulnerable. These figures are adapted from
SAP’s positioning of Resilient Supply Chain.
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Fig. 36 Supply chain vulnerabilities and impact
Resilient supply chains proactively determine supply chain disruptions, simulate events and find alternatives, and recover from disruptions. Goals of intelligent
digital systems in resilient supply chains include:
• Early warning systems that show vulnerabilities in supply chain.
• Faster what-if scenarios and simulations to find the best alternative.
• Use of the abundant supply chain knowledge from historical data combined with
planner’s actions and enterprise goals.
• Alignment of operations and finance to find the best financially viable solution.
• Application of intelligence to supply chains by co-relating data from multiple
sources to identify supply chain relationships, risks and opportunities.
• Mitigation of disruptions in a timely manner with transparency and collaboration
across stakeholders.
• Signal based management of short-term and long-term impacts.
• Manual to automated search and discovery of supply chain vulnerabilities.
• Continuously test the robustness of the supply chain by simulating failures and
tracking how supply chain respond to such disruptions.
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• Risk sensing to reduce risk from potential disruptions.
• Resilient supply chains require intelligent visibility and digitization of supply
chains that was discussed in the previous sections. A holistic approach is
required than piecemeal approach to gain visibility into the end to end supply
chain connecting functional domains like planning, manufacturing, logistics and
execution. This is required to determine conditions that need to be analyzed,
simulate the impact and solutions for those conditions, apply intelligence to
evaluate alternatives that are financially viable and deploy the solution without
impacting the current execution.
Examples of resilient supply chains across different Industries
Healthcare:
•
•
•
•
Manage allocation and inventory of available PPE equipment’s inventory
Planning of critical medical supplies during pandemics and natural calamities.
Telecommunications:
Faster response during catastrophic events or pandemics e.g. during COVID-19
where there was greater demand for network communications
• Manage available inventory for critical customers. For example, higher bandwidth allocation for government and medical workers.
Agriculture:
• Increase shelf life of products from farm to fridge.
• Manage production with plant shutdowns and fluctuating consumer demand. e.g.
Sugar demand surged during COVID pandemic where people during lockdown
were consuming more sugar.
• Better sustainable products with natural, organic ingredients.
Consumer products:
• Alternate sourcing for raw materials.
• Alternate modes of transport (rail, road, sea or air) to supply materials with high
demand. e.g. toilet papers, hand sanitizers, etc.
• Strategies for long term vendor determination.
• Faster product interchangeability to offer competitive products and optimally
consume supply from similar products.
Food and Beverages:
• Visibility of inventory in global supply chain and balance available inventory to
reduce obsolescence. For example, scarcity of water bottles during hurricane or
increased demand for chips and soda during gaming season.
• Scenario planning to determine alternate sources for packaging if packaging
factory is hit by calamity like fire.
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Oil and Gas
• Rapid scenario planning with fluctuating crude oil prices
• Demand collaboration across extended supply chain to determine projects for
drilling, fracture and oilfield rigs.
• Optimal productions and transportation of oil by-products. For example, during
pandemic, oil reservoirs were placed on containers in sea.
High-Tech
•
•
•
•
Real time visibility of critical suppliers for materials.
Evaluate alternate lead times and production lot sizes
Distribution planning to manage service levels of critical supplies.
Financial impact of raw material price changes
11 Collaborative Enterprise Planning: Integrated Supply
Chain and Financial Planning
An integrated operations and financial planning system helps enterprises instantly
gain visibility into the supply chain plans with its financial impact and vice versa.
However, the financial planning systems and supply chain systems are separate and
disconnected from each other, each with a different purpose and ownership. The
finance data focuses more on valuation (dollarized amount e.g., revenue, costs and
margin) with data structure based on financial account hierarchies at an aggregated
company, product line levels. On the other hand, supply chain data focuses on
volumes or quantities (e.g., demand forecasts and production quantity), and the
supply chain is a network model with material flow across connected locations and
bill of materials.
A Collaborative enterprise planning solution supports an integrated financial,
supply chain and commercial process. It consolidates budgets and financial plans
with commercial and operations plans, balancing supply, demand and inventory with
financial goals plans across the organization.
Use Cases for an integrated finance and operations system include the following:
–
–
–
–
–
–
–
Strategic planning
Investment plan on new production line
Headcount decisions to increase shifts for production
Capex planning on retiring an aging asset
Profitability impact on price changes for raw materials
Impact of mergers and acquisitions or sell-off
Trade effectiveness and tax changes
The business benefit of integrating the two systems is to provide an output
of a single approved plan (financial/operating) as a blueprint for all stakeholders
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Fig. 37 Planning types and horizons for finance and supply planning
Fig. 38 Supply Chain Costs-to-Serve
(internal and external) to follow, which positively impacts the balance sheet and
profit and loss statements of the business.
Most companies follow these planning cycles shown below for strategic planning, which is 3–5 year long term plan followed by an annual operating plan or
budget plan followed by sales and operations planning for the Rolling 18 months.
An example of the planning processes is shown in Fig. 37.
Financial planning systems need the volume-based supply chain plans for
running processes like budget planning and product cost planning, and the supply
chain planning systems need the valuation based financial plans for consensus
demand planning and financial reconciliation.
Supply chains have financial costs associated with each step to fulfill customer
demand, as shown in Fig. 38. These include raw material costs, production costs,
productive costs, direct and indirect costs, transportation costs and storage costs.
Supply chains employ profit optimization techniques to arrive at a profitable costeffective strategy to fulfill customer demands.
Figure 39 shows a consensus planning view in SAP Integrated Business Planning
consolidating inputs from all functional units -sales, marketing, demand planning,
finance to arrive at a consensus demand plan. AOP represents the annual operating
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Fig. 39 Financials in supply chain plans
plan. Both volumes and values of different plans are recorded or calculated, and the
gap between AOP and consensus plans is visible month to month. Planners have
a consolidated view of the complete demand picture and can make adjustments to
close the gap to the financial targets. Revenue and profit are calculated automatically
based on adjustments to the volumes.
The scope of financials in supply chains is to provide directional finance and
valuation for supply planning decisions. These include:
• Financial projections (valuation of the plans) that convert volumes to values and
report at different aggregation levels
• Currency conversions including simulations on price and exchange rates changes
• Rolling revenue and gross margin projections
• Planned price and costs as input at different aggregate levels than supply chain
plans.
• Budget plans or annual operating plans as financial targets to drive operation
plans
• Calculations of price based on revenues and volumes
• Weighted average of price and costs calculations
• Basic cost rollups for cost-to-serve calculations
The overall process for finance and supply chain integration is shown in Fig. 40
across tactical annual budget planning and operational rolling monthly revisions of
the plan. This figure is adapted from SAP’s positioning of collaborative enterprise
planning between finance and supply chain solutions.
Annual Financial Planning
1. Tactical annual financial planning is performed in financial planning systems for
the next fiscal year combining inputs from strategic planning, prior year plans
and operations plans from a supply chain that consolidates demand and supply
plans for the next 1–3 years.
Intelligent Digital Supply Chains
63
Fig. 40 Integrated finance and operational process
2. Supply chain systems send the operational plan to financial planning systems as a
starting point for granular financial planning (e.g., detailed annual budget—sales
units by region, cost assumptions and investments in capacity) that integrates the
operational plan.
3. The resultant annual operating plan is sent to supply chain systems as a financial
target for consensus and constrained demand planning to evaluate scenarios that
bridge the gap to meet financial targets.
Operational Rolling Financial Planning
1. The annual operating plan is sent from financial planning to supply chain
planning systems once a year. Iterations occur monthly in the supply chain
until a consensus is reached on a single plan considering the financial budget
and constrained supply plans that allocate the critical resources, inventory and
materials most efficiently to satisfy customer demands while maintaining high
profit margins and working capital.
2. The process in supply chain starts with arriving at a consensus demand plan
in S&OP based on sales, marketing, demand and financial plans followed by
constraining the plan based on profit optimization considering supply chain
constraints and costs, along with business assumptions, risks and opportunities
(Kusters et al. 2018).
64
R. Jandhyala
3. Every stage of the process in the supply chain also includes financial projections
like revenue, margin, costs for the demand and supply plans to see both the
volume and value for the plans.
4. Finance and operations collaborate monthly or quarterly on variance analysis
reporting (plan vs. actuals) to reforecast and model changes in the business,
industry or economy.
5. The revised demand, supply and inventory quantities are sent to the financial
planning system for monthly/quarterly revision of the plan. This includes running
processes like sales revenue planning, cost and activity planning, product cost
planning, profitability planning, and P&L planning. The net result is an aligned
profit and loss, balance sheet, and cash flow statements with the supply chain
plan.
6. The results are revised financial targets that are sent to supply chain for visibility
into the latest P&L. The revised financial plans, i.e., Q2 revision, Q3 revision,
Q4 revision are the new financial targets for the supply chain.
References
APICS SCC. (2015). The supply chain operations reference model (SCOR® ).
Chopra, S., Meindl, P., & Kalra, D. V. (2013). Supply chain management: Strategy, planning, and
operation (Vol. 232). Boston, MA: Pearson.
Kepczynski, R., Ghita, A., Jandhyala, R., Sankaran, G., & Boyle, A. (2019a). Enable IBP with
SAP integrated business planning. In Implementing integrated business planning (pp. 23–110).
Cham: Springer.
Kepczynski, R., Dimofte, A., Jandhyala, R., Sankaran, G., & Boyle, A. (2019b). Implementing
integrated business planning (Management for Professionals). Cham: Springer.
Kusters, J., Jandhyala, R., Mane, P., & Sinha, A. (2018). Sales and Operations Planning (S&OP)
with SAP IBP (SAP PRESS).
Ravindran, A. R., & Warsing, D., Jr. (2016). Supply chain engineering: Models and applications.
Boca Raton, FL: CRC Press.
Product Life Cycle Optimization Model
for Closed Loop Supply Chain Network
Design
Aswin Dhamodharan and A. Ravi Ravindran
1 Introduction
Closed Loop Supply Chain (CLSC) management is defined as ‘the design, control
and operation of a system to maximize value creation over the entire life cycle
of a product with dynamic recovery of value from different types and volumes of
returns over time’ (Guide and Li 2010). According to the authors, a business process
perspective on CLSC consists of three subprocesses, namely:
(i) Front-end that deals with product return management, ensuring the availability
of products at required quality and quantity.
(ii) Engine that deals with operational issues in remanufacturing, ensuring profit
from remanufacturing.
(iii) Back-end that deals with market development of remanufactured products,
ensuring demand for recovered products.
OEM has to make two major strategic decisions at the Front-end of CLSC,
namely, (i) identify the most profitable channel of product recovery and (ii) identify
the profit maximizing remanufacturing market that OEM should participate in.
We explicitly model the optimal collection of remanufactured products through
suppliers in a dynamic manner across multiple time periods over the product
life, accounting for demand, quality and remanufacturability of returned products.
A. Dhamodharan ()
Tesla Motors, San Carlos, CA, USA
e-mail: adhamodharan@tesla.com
A. Ravi Ravindran
Pennsylvania State University, State College, PA, USA
e-mail: axr32@psu.edu
© Springer Nature Switzerland AG 2021
S. Srinivas et al. (eds.), Supply Chain Management in Manufacturing and Service
Systems, International Series in Operations Research & Management Science 304,
https://doi.org/10.1007/978-3-030-69265-0_3
65
66
A. Dhamodharan and A. Ravi Ravindran
At the back-end of the CLSC, the OEM has to (i) make pricing decisions for
new and remanufactured products based on the consumer willingness to pay (WTP)
and (ii) estimate the demand for new and remanufactured products throughout
the product lifecycle. We use the extended Bass model (Debo et al. 2006) to
model total demand for the product, and cannibalization of new product demand
is reflected in pricing decisions of new and remanufactured products. We use
pricing decisions for new and remanufactured products based on Abbey et al.
(2015a, b).
We develop a multi-period CLSC network design planning model, incorporating
product life cycle for a supply chain consisting of three stages: (i) OEM manufacturing facility, (ii) distribution centers, hybrid facilities and recovery centers and (iii)
retailers. At the strategic level, the OEM has fixed locations for manufacturing plants
and retailers, and potential locations for distribution centers, hybrid facilities and
recovery centers. In the forward flow, OEM manufactures new and remanufactured
products and distributes them through warehouses and hybrid facilities to satisfy
demand at the retailers in fixed locations. In the reverse flow, retailers collect
commercial returns, end of use returns and end of life returns from consumers for a
collection cost, provided the return has a certain level of functionality. These returns
are then sent to hybrid facilities and recovery centers, where they are disassembled,
inspected and stored. The OEM’s manufacturing facilities pull these returns from
the hybrid facilities based on the demand for remanufactured products. At the
tactical level, we consider an uncapacitated model, where demand for new and
remanufactured products follow an extended Bass model (Debo et al. 2006). We
consider a segmented market with quality-sensitive and price-sensitive consumers
based on the empirical evidence from Abbey et al. (2015a). We assume that the
WTP functions for both quality-sensitive and price-sensitive consumers overlap.
We use monopolist pricing decisions for new products in all periods (Abbey et
al. 2015b). When making a pricing decision on remanufactured products, we use
monopolist pricing expression (Abbey et al. 2015b) only when there are enough
available returns to satisfy the demand. Under constrained availability of returns,
we use a myopic pricing decision, where the OEM maximizes profit from selling
remanufactured products for that period only. We also penalize any lost demand due
to shortage of returns.
In the literature review, we briefly discuss contributions to dynamics of product
lifecycle; consumer perception of new and remanufactured products, the demand
cannibalization for new products from remanufactured product sales and finally,
pricing decisions under remanufacturing. We introduce the product life cycle
optimization model and use a case-study to show that the OEM’s approach to supply
chain network design can significantly impact the profitability in the reverse supply
chain over the product lifecycle. Finally, we present the conclusions from sensitivity
analysis on various parameters in the model.
Product Life Cycle Optimization Model for Closed Loop Supply Chain Network Design
67
2 Literature Review
2.1 Product Life Cycle Dynamics
We use diffusion based models to study product lifecycle dynamics. Bass (2004)
has developed a growth model to forecast demand for consumer products, called
the Bass model. The Bass model is based on the behavioral assumption that the
probability of a new purchase of a product at any time depends on the number
of previous purchases of the product. The model is empirically tested and found
to accurately predict the highest number of sales in a period and its timing, for
various consumer durables, predominantly between 1950 and 1960. Debo et al.
(2006) are the first to consider product life cycle dynamics in a closed-loop setting.
The authors extend the Bass diffusion model to: (i) study life cycle demand of
new and remanufactured products and (ii) analyze investments in flexible capacity.
The authors find that slow diffusing products are best suited for remanufacturing,
while fast diffusing products and products with repeat sales, make investment in
flexible capacity valuable. They also comment that the right time for the OEM to
introduce remanufactured products is after the sales for the new product has peaked.
In addition to the closed-loop setting and the factors studied in this work, our model
additionally considers the evolution of product pricing over the product lifecycle,
since profit is a function of pricing and demand.
There are very few studies in the literature that explicitly consider supply chain
decisions, including pricing, across the life cycle of a product, in a closed loop
setting. To the best of our knowledge, Chen and Chang (2013) is the only study that
analyzes pricing decisions across the life cycle of a product, but does not model the
product demand. Papers that analyze specific CLSC problems, such as, Savaskan et
al. (2004), Ferrer and Swaminathan (2010), Toktay and Wei (2011), consider one
or two-period product life cycle to keep their models tractable. On the other hand,
papers analyzing broader CLSC problems, such as, Jayaraman (2006), Min et al.
(2006), Pazhani and Ravindran (2014), do not model demand from the product life
cycle view.
2.2 Consumer Perception of Remanufactured Products
Ovchinnikov (2011) shows empirical evidence that, because of the lower price of the
remanufactured product, a new market segment is ready to purchase remanufactured
products. Ovchinnikov (2011) also provides empirical evidence that, as the price
of remanufactured product increases, shifting of consumers from new product
purchase to remanufactured product purchase follows an inverted U shape. Pang
et al. (2015) analyze data on purchases of electronic products available in eBay,
UK. The authors determine seller identity and reputation, length of warranty period,
supply of remanufactured products, proxy demand, duration of sale and end day
68
A. Dhamodharan and A. Ravi Ravindran
of sale as significant factors that determine the price differential between new
and remanufactured products. While the studies above characterize customer WTP,
they lack extensive empirical evidence. In our work, we use the market structure
proposed by Abbey et al. (2015a, b), who conduct multiple studies to understand
consumer perceptions of remanufactured products, including technological, household and personal products. The authors find that product quality is significantly
more important than the discount offered in all the studies. They also find that
brand equity has insignificant impact and educating consumers on remanufacturing
process does not help in alleviating their negative perceptions. Based on their
extensive experimental results, the authors conclude that consumers fall into one
of the two categories:
(i) A new product only segment, where the consumer always has a significantly
higher preference for a new product. Even under extreme discounts for remanufactured products, the consumer prefers only new products. For all future uses,
we refer to this consumer segment as Quality-sensitive consumers.
(ii) An indifferent segment, where the consumers are indifferent between new and
remanufactured products. When both new and remanufactured products are
presented at the same discount level, the consumer segment only shows a slight
preference for new products. For all future uses, we refer to this consumer
segment as price-sensitive consumers.
We follow the consumer behavior model of Abbey et al. (2015b), overlaying it
on the demand model, and explicitly model the OEM pricing decisions over the
product lifecycle.
2.3 Demand Cannibalization
Introduction of remanufactured products in the marketplace affects the demand
for new products, called demand cannibalization. The majority of models in the
CLSC literature assume no cannibalization, which is not always true. Guide and Li
(2010) provide the first empirical evidence on the effect of product cannibalization.
Ovchinnikov (2011) argues that remanufacturing decisions based on consumers’
WTP for remanufactured product could be misleading, as lower price of remanufactured products does not always result in increased sales. This is the first study to
quantify product cannibalization, based on a behavioral study for cell phones, and
employ the observed cannibalization function into the model. However, the focus
of this study does not include network design and demand considerations over the
product lifecycle. Since we observe significant evidence for the cannibalization of
new product demand in the literature, we consider the cannibalization effect through
the OEM pricing strategy from Abbey et al. (2015b).
Product Life Cycle Optimization Model for Closed Loop Supply Chain Network Design
69
2.4 Pricing Under Remanufacturing
Majumder and Groenevelt (2001) initiated a stream of papers on the pricing strategy
for remanufacturing. The majority of the articles begin with the assumption of
a theoretical model for consumer behavior (Abbey et al. 2015b). Hammond and
Beullens (2007) are the first to extend the pricing model to CLSC. The authors
consider oligopolistic CLSC under the Waste Electrical and Electronic Equipment
(WEEE) directive. They derive the Nash equilibrium for the supply chain and find
that the legislation encourages reverse supply chain activities. Abbey et al. (2015b)
compute the optimal pricing strategy based on an empirically observed market
structure. The authors initially derive optimal pricing expressions of a two-period
model, where the OEM sells only new products in the first period and sells both
new and remanufactured products in the second period. The main assumption in the
initial model is no constraint on the availability of product returns. The authors then
impose this constraint and propose an algorithm to compute the optimal pricing
strategy. We use both the market structure and the optimal pricing strategy given
by Abbey et al. (2015b). We observe that OEM’s pricing strategy influences the
sales of new and remanufactured products. Also, the quantity of new products sold
impacts the quantity of returns. Hence, we analyze the OEM’s pricing strategy at
the network design level.
3 Methodology
3.1 Product Life Cycle Optimization Model for CLSC
We consider a single product, multi-period CLSC network model for an OEM, as
shown in Fig. 1. The location of manufacturing plants (MP) and consumers (C)
are fixed and known. In the forward supply chain of the network, MPs produce
new and remanufactured products that flow through distribution centers (DC)
and hybrid facilities (HF) and are sold to consumers (C) through retailers (R).
In the reverse supply chain, retailers (R) collect consumer returns and distribute
them to the recovery centers (RC) and the hybrid facilities (HF), where they are
disassembled, tested, sorted and then sent to the manufacturing plants (MP), for
repairs and remanufacturing. The network optimization problem determines the
optimal locations for the DCs, HFs and RCs that maximize the profit of the CLSC
network, given fixed locations for all MPs and retailers. We consider inventory
holding cost, transportation cost, fixed cost of opening the facilities, and shortage
cost. We also consider the revenue from selling new and remanufactured products
and the disposal cost. The following subsection describes the OEM’s pricing
decision that affects product demand. Initially, the OEM sells only the new product
in the market. After a few periods, the OEM introduces the remanufactured product
in the market. The optimization model generates demands based on the demand
70
A. Dhamodharan and A. Ravi Ravindran
Distribution
Centers
(DC)
Manufacuring
Plants (MP)
Hybrid
Facilities
(HF)
Retailers
(R)
Consumers
(C)
Recovery
Centers
(RC)
Fig. 1 Structure of CLSC network examined in PLCOM. Solid arrows represent the forward flow
of new and remanufactured products from the manufacturer to consumers. Dotted arrows represent
the backward flow of returns from consumers to the manufacturer
model (Appendix 2) that reflects product life cycle parameters. The optimization
model decides the optimal demand fulfillment and the optimal selling price based
on the pricing model (Appendix 1). The output of the optimization model gives the
optimal network design and the optimal distribution plan that maximizes the profit
over the product life cycle.
3.2 Product Life Cycle Optimization Model (PLCOM) for
CLSC
In this section, we present an integrated optimization model called product life
cycle optimization model (PLCOM), for designing an optimal CLSC network.
The PLCOM consists of three models. The functions of these models and their
interactions are shown in Fig. 2.
The major features of the PLCOM for CLSC are as follows:
1. A framework that applies the pricing model (Appendix 1) to decide the selling
price of new and remanufactured products. These pricing decisions are exogenous to the optimization model.
2. Integration of pricing decisions with the demand model (Appendix 2) that
accounts for the product life cycle. Thus, we compute the demand and the
selling price for new products and remanufactured products, exogenous to the
optimization model.
3. Integration of the demands and prices computed in the previous steps with a
network design optimization model.
Product Life Cycle Optimization Model for Closed Loop Supply Chain Network Design
71
Fig. 2 Product life cycle optimization model for CLSC
3.3 Assumptions of the Integrated Optimization Model
The following are the assumptions of the integrated optimization model:
1. All types of returns, such as commercial returns and end of use returns, are
modeled as consumer returns. Remanufacturability of all consumer returns is
assumed to be constant over the entire planning horizon.
2. Lead time for both manufacturing and remanufacturing of the products is zero.
This assumption can be easily relaxed to study the effect of lead time.
3. Transportation time across the supply chain is zero.
4. Demands at retailers are generated using both the pricing model and the
extended Bass model, for each period.
5. There are no capacity restrictions at the manufacturing plants for new and
remanufactured products.
6. Demand for new products must always be fulfilled.
7. Shortages are allowed for remanufactured products. However, all shortages are
treated as “lost-sales”.
8. The fraction of price-sensitive consumers and fraction of quality-sensitive
consumers remain constant throughout the planning horizon.
9. Consumer WTP functions (α 1 + β 1 θ 1 ) and (α 2 + β 2 θ 2 ) are constant over the
entire planning horizon.
10. Pricing model assumptions (Section Appendix 1) are (i) cn > cr , (ii) α 1 > α 2
and (iii) α 1 + β 1 > α 2 + β 2
72
A. Dhamodharan and A. Ravi Ravindran
11. Consumer returns for remanufactured products are not allowed.
12. Proportion of consumer returns that is remanufacturable is constant.
13. Only a fixed proportion of consumers make a repeat purchase. The rest of
the consumers are assumed to not buy the product again during the product
lifecycle.
3.4 Optimization Model
Sets:
N: planning horizon of the optimization model, t = 1,2,..,N
MP: set of all manufacturing plants in the network
DC: set of all potential distribution centers
HF: set of all potential hybrid facilities
RC: set of all potential recovery centers
R: set of all retailers in the network
Parameters used from integrated pricing and demand model (Appendix 3):
market size: the total size of the market considered. marketsize is normalized to 1
innov: innovation coefficient of the product that creates new demand. The coefficient
is expressed as a fraction of the total market size
imitation: imitation coefficient that creates new demand through word-of-mouth
effect from existing customers. This coefficient is also expressed as a fraction
of the total market size
marketexpt : market size expanded in period t, expressed as a fraction of total market
size
markett : market size at the end of period t, expressed as a fraction of total market
size
ϕ: the proportion of returns that can be remanufactured (quality of consumer returns)
residencej : the fraction of current product owners, markett , whose new product will
fail at period j
repeatt : demand created from repeat purchase by current product owners in period
t. This unit is a fraction of markett
L: product lifetime
newprdownt : number of consumers who own the new product in period t
returnst : number of consumer returns of new product in period t. Note that we do
not consider returns of remanufactured products. The consumer return may be a
commercial return or end of life return
Qnewt : optimal production quantity for the new product in period t
Qremt : optimal production quantity for the remanufactured product in period t
Product Life Cycle Optimization Model for Closed Loop Supply Chain Network Design
73
λ: the fraction of consumers that only buy a new product, referred to as Qualitysensitive consumers
1-λ: the fraction of consumers that only buy a cheaper product, referred to as Pricesensitive consumers
α 1 + β 1 θ 1 : willingness to pay function for quality-sensitive consumers
α 1 : intercept of willingness to pay function of quality-sensitive consumer
β 1 : the slope of willingness to pay function of quality-sensitive consumer
θ 1 : variable between 0 and 1 to give cumulative demand for new product
α 2 + β 2 θ 2 : willingness to pay function for price-sensitive consumers
α 2 : intercept of willingness to pay function of price-sensitive consumer segment
β 2 : slope of willingness to pay function of price-sensitive consumer segment
θ 2 : variable that varies between 0 and 1 to give cumulative demand for new product
innov: innovation coefficient of the product
imitation: imitation coefficient of the product
ϕ: the fraction of consumers that repeat purchase of a new product
remtime: time to remanufacture a product return
repcust: the fraction of current product owners making a repeat purchase (treated as
a constant)
cn : manufacturing cost for new product
intro: time period at which remanufactured product is introduced
Cost components:
cn : manufacturing cost for new product
pn : selling price for new product
pr : selling price for remanufactured product
cn : manufacturing cost of a new product
cr : remanufacturing cost
dispcost: unit cost of disposing a product
hc : unit cost of holding a returned product in inventory per period
hr : unit cost of holding a remanufactured product in inventory per period
sc: cost of not fulfilling a demand per unit
fw : fixed cost of opening a DC
fh : fixed cost of opening a HF
fr : fixed cost of opening a RC
tf 1mp,dc,s: unit transportation cost from a manufacturing plant mp, to retailer s,
through a distribution center dc
tf 2mp,hf,s: unit transportation cost from a manufacturing plant mp, to retailer s,
through a hybrid facility hf
tb1s,rc,mp: unit transportation cost from a retailer s, to a manufacturing plant mp,
through a recovery center rc
tb2s,hf,mp: unit transportation cost from a retailer s, to a manufacturing plant mp,
through a hybrid facility hf
74
A. Dhamodharan and A. Ravi Ravindran
Capacity components:
cap1mp: maximum capacity of manufacturing plant mp
cap2dc: maximum capacity of distribution center dc
cap3hf : maximum capacity of hybrid facility hf
cap4rc: maximum capacity of recovery center rc
Demand at retailers:
ωs : fraction of the total demand at retailers
γ s : fraction of consumer returns at retailer s
Qnews,t : demand for the new product at retailer s during period t, and is given by:
Qnews,t = (Qnewt ) ωs ;
where
∗
For t < intro, Qnewt = qn1
marketdemandt
∗
For t ≥ intro, Qnewt = qn2
marketdemandt
Qrems,t : demand for the remanufactured product at retailer s and is given by:
Qrems,t = Qremt ωs
where Qremt = qr∗ marketdemandt
Decision variables:
Iremt : inventory of remanufactured product at the beginning of a period
Irett : inventory of returned product at the beginning of period t
remcollecs,t : maximum number of product returns available at retailer s in period t
zt : binary variable that takes a value of 1 if there is a shortage of remanufactured
product in period t
shortaget : quantity of shortage of remanufactured product in period t
OpenDCdc : binary variable that takes a value of 1 if DC at d is open and 0 otherwise
OpenHFhf : binary variable that takes a value of 1 if HF at hf is open and 0 otherwise
OpenRCrc: binary variable that takes a value of 1 if RC at rc is open and 0 otherwise
Qnewtr1mp,dc,s,t: quantity of new products, transported from manufacturing plant
mp, through distribution center dc, to retailer s during period t
Qnewtr2mp,hf,s,t: quantity of new products, transported from manufacturing plant
mp, through hybrid facility hf, to retailer s during period t
Qremtr1mp,dc,s,t: quantity of remanufactured products, transported from manufacturing plant mp, through distribution center dc, to retailer s during period t
Qremtr2mp,hf,s,t: quantity of remanufactured products, transported from manufacturing plant mp, through hybrid facility hf, to retailer s during period t
Product Life Cycle Optimization Model for Closed Loop Supply Chain Network Design
75
Qrettr2s,hf,mp,t: quantity of consumer returns, transported from retailer s, through
hybrid facility hf, to manufacturing plant mp during period t
Qrettr1s,rc,mp,t: quantity of consumer returns, transported from retailer s, through
recovery center rc, to manufacturing plant mp during period t
Objective function:
The objective function consists of the following cost components:
• Revenue from sales (REV) = REVN + REVR1 where:
– Revenue from new products (REVN) =
(pn − cn )
N (1)
Qnews,t
t =1 sεR
(i) For t < intro, we use Eq. (2) for pn and Eq. (3) for Qnewt , given below.
p =
n
∗
pn1
1
cn
+
=
2
2
λβ2 (α1 + β1 ) + (1 − λ) β1 (α2 + β2 )
λβ2 + (1 − λ) β1
∗
marketdemandt
Qnewt = qn1
(2)
(3)
α2 + β2 − pn
α1 + β1 − pn
+ (1 − λ)
Qnewt = λ
marketdemandt
β1
β2
(4)
Qnews,t = Qnewt ωs
(5)
(ii) For t ≥ intro, we use Eq. (6) for pn and Eq. (7) for Qnewt , as shown below.
pn =
∗
pn2
α1 + β1
cn
+
= max α1 ,
2
2
∗ (marketdemand ) λ =
Qnew t= qn2
t
λ2 min
α1 +β1 −cn
,1
2β1
marketdemand t
(6)
(7)
– Revenue from remanufactured products:
∗ Periods when we meet demand for remanufactured products, we use Eq. (9)
for pr and Eq. (10) for Qremt , shown below (REVR1):
(pr − cr )
N
t =1
sεR
Qrems,t
− shortaget
(8)
76
A. Dhamodharan and A. Ravi Ravindran
where:
α1 + β1
cr
pr = max α2 , +
2
2
α2 + β2 − cr
Qremt = λ (1 − λ) min
, 1 marketdemandt
2β2
(9)
Qrems,t = Qremt ωs
(10)
(11)
• Inventory holding cost (IHC):
N
hr I remt + hc I rett
(12)
t =1
• Shortage cost (SHORT):
N
sc shortaget
(13)
(1 − ϕ) returnst dispcost
(14)
t =1
• Disposal cost (DISP):
N
t =1
• Transportation cost (TRANS):
N
t =1 mp ε MP dc ε DC s ε R
N
Qnewtr1mp,dc,s,t tf mp,dc,s +
t =1 mp ε MP hf ε H F s ε R
N
t =1 mp ε MP dc ε DC s ε R
N
Qnewtr1mp,dc,s,t tf mp,dc,s +
(15)
t =1 mp ε MP hf ε H F s ε R
N
t =1 s ε R rc ε RC mp ε MP
N
Qnewtr1mp,hf,s,t tf mp,hf,s +
Qnewtr1mp,hf,s,t tf mp,hf,s +
Qnewtr1s,rc,mp,t tf s,rc,mp +
t =1 s ε R hf ε H F mp ε MP
Qnewtr1s,hf,mp,t tf s,hf,mp
Product Life Cycle Optimization Model for Closed Loop Supply Chain Network Design
77
• Fixed cost of opening facilities (FAC):
fdc OpenDCdc +
dc ε DC
fhf OpenH Fhf +
hf ε H F
frc OpenRCrc
rc ε RC
(16)
The objective function is to maximize the net profit: maximize: REV(IHC + SHORT+DISP+TRANS+FAC)
Constraints:
1. Assign a route to all new products produced in period t to reach consumers
through a DC or a HF.
Qnewtr1mp,dc,s,t +
mp ε MP dc ε DC
Qnewtr2mp,hf,s,t
mp ε MP dc ε DC
= Qnews,t ∀s ε R, t ε T
(17)
2. Assign a route to all remanufactured products produced in period t to reach
consumers through a DC or a HF.
Qremtr1mp,dc,s,t +
mp ε MP dc ε DC
Qremtr2mp,hf,s,t
mp ε MP dc ε DC
= Qrems,t − ωs shortaget ∀s ε R, t ε T
(18)
3. Assign a route to all consumer returns collected in period t to reach MP through
a RC or a HF.
Qrettr1s,hf,mp,t +
Qrettr2s,rc,mp,t
hf ε H F mp ε MP
rc ε RC mp ε MP
= remcollecs,t ∀s ε R, t ε T
(19)
4. Capacity constraint for each DC where the sum of all products transported to a
DC in every period should not exceed its capacity, if the DC is built.
Qnewtr1mp,dc,s,t + Qremtr1mp,dc,s,t
mp ε MP dc ε DC
≤ capacitydc OpenDCdc ∀dc ε DC, t ε T
(20)
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A. Dhamodharan and A. Ravi Ravindran
5. Capacity constraint for each HF where the sum of all products transported to a
HF in every period should not exceed its capacity, if the HF is built.
Qnewtr2mp,hf,s,t + Qremtr2mp,hf,s,t + Qrettr2s,hf,mp,t
mp ε MP s ε R
≤ capacityhf OpenH F hf ∀hf ε H F, t ε T
(21)
6. Capacity constraint for each RC, where the sum of all consumer returns
transported to a RC in every period should not exceed its capacity, if the RC
is built.
Qrettr1mp,rc,s,t + ≤ capacityrc OpenRC rc ∀rc ε RC, t ε T
mp ε MP s ε R
(22)
7. Constraint to update the inventory of consumer returns. Until the remanufactured
product is introduced in the market, there is no production of remanufactured
product. Hence, the inventory of consumer returns keeps adding up with the
collection of returns.
I rett = I rett −1 +
remcollecs,t −1 ∀t < intro&t ε T
(23)
sεR
8. Constraint to update the inventory of consumer returns. When a remanufactured
product is introduced in the market, there is a production of a remanufactured
product. Hence, inventory at the start of the period is the sum of inventory from
the previous period and consumer returns minus the quantity of returns used to
produce the remanufactured product.
I rett = I rett −1 − Qremt −1 + shortaget −1
remcollecs,t −1 ∀t < intro&t ε T
sεR
(24)
9. Quantity of consumer returns collected in period t that can be used to remanufacture is the product of remanufacturability and the parameter returnst .
remcollecs,t ≤ ϕ γr returnst ∀s ε R, t ε T
(25)
Note: returnst is given by Equation in Appendix 2.
10. Constraint to update the inventory of remanufactured products.
I remt = I remt −1 +
⎛ Qremt −1 − shortaget −1
⎝
−
Qremtr1mp,dc,s,t −1 +
mp ε MP s ε R
dc ε DC
⎞
Qremtr2mp,hf,s,t −1 ⎠ ∀t
hf ε H F
(26)
Product Life Cycle Optimization Model for Closed Loop Supply Chain Network Design
79
11. Constraint to detect shortage of remanufactured product in period t. If there is
no shortage, zt = 0, else zt = 1.
shortaget ≤ M zt ∀t
(27)
where M is a large number.
12. Non-negativity and binary constraints:
I remt , I rett , Qremt , remcollect , shortaget ≥ 0∀t
Qnewtr1dc,t , Qrem1dc,t ≥ 0∀dc, ∀t
Qnewtr2hf,t , Qremtr2hf,t , Qrettr2hf,t ≥ 0∀hf, ∀t
Qrettr1rc,t ≥ 0∀rc, ∀t
(28)
(29)
(30)
(31)
OpenDCdc ε {0, 1} ∀dc, Open H Fhf ε {0, 1} ∀hf, OpenRCrc ε {0, 1} ∀rc
(32)
zt ε {0, 1} ∀t
(33)
3.5 Case Study Applying PLCOM to Design Supply Chain
Network for iPhone
Problem Description
We consider a CLSC with market segmentation consisting of quality-sensitive and
price-sensitive consumers. The aim of the example is to illustrate the impact of
considering the pricing model and the extended Bass model on the optimal supply
chain network structure.
Assuming each time period in PLCOM is 1 month long, we limit the lifecycle
of the product to 100 periods (approximately 8 years) for better exposition. We
assume the lifetime of a new product to be at least 12 periods (1 year) and up to
36 periods (3 years). Thus, a new product can only fail after 1 year of residence
with the customer, and all products fail after 3 years (estimation by Apple). These
are practical assumptions because electronic products are typically under warranty
for the first 12 months and are not designed to last longer than 3 years. Hence,
the residence time distribution ranges from period 12 to period 36. We use a
symmetrical beta distribution with parameter 2.5 to generate the probability density
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A. Dhamodharan and A. Ravi Ravindran
Probability density functionResidence time
0.08
0.07
0.06
0.05
0.04
0.03
0.02
0.01
0
13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36
Time Periods
Residence time distribution
Fig. 3 Probability density function for residence time from periods 13 to 36
function for residence time, as shown in Fig. 3. We also assume that remanufactured
products are introduced into the market in period 25.
Based on the lifecycle assumptions above and considering iPhones to be a
fast-moving product, we arrive at the innovative coefficient, innov = 0.0005 and
imitation coefficient, imitation = 0.000025, so that the sales in the initial period of
the lifecycle are significantly higher than the sales at the latter stage and the market
consumes the product at least once within the product lifetime. We assume a realistic
market structure with two groups of consumers: (i) quality-sensitive consumers with
WTP function 550 + 250θ and (ii) price sensitive consumers with WTP function
450 + 200θ . We obtain the WTP function based on the iPhone market prices at
different retailers. For quality-sensitive customers, we model the WTP function
based on the iPhone7 (32 GB)‘s market price of $699, from the retailers like
Amazon and Walmart. For price-sensitive customers, we model the WTP function
based on the pre-owned iPhone6 Plus (16 GB) market price of $540, from the
retailer Bestbuy. We assume the new product manufacturing cost to be $400 and the
remanufacturing cost to be $100 (total price of all the components inside an iPhone
is roughly estimated at $220). We assume 60% of the market is of quality-sensitive
consumers (λ = 0.6) and 80% of the customers make a repeat purchase. We also
assume all the collected returns are of good quality and can be remanufactured. We
consider a CLSC network with 3 manufacturing plants, 3 retailers and 10 potential
locations for distribution centers (DCs), recovery centers (RCs) and hybrid facilities
(HFs), each with a capacity of processing 100 items. Recall that DCs handle only
forward flows, RCs handle only reverse flows and HFs handle both forward and
reverse flows. We assume the fixed cost of opening a DC and a RC to be $500,000,
and the fixed cost of opening a HF to be $550,000. We assume per period inventory
holding cost for a unit of new product, returned product and remanufactured product
to be $5. Table 1 shows the proportion of market demand and product returns at each
retailer. Appendix 4 lists unit transportation cost within the CLSC network.
Product Life Cycle Optimization Model for Closed Loop Supply Chain Network Design
81
Table 1 Proportion of market demand and product returns at each retailer
Retailer
1
2
3
Proportion of market demand
0.3
0.4
0.3
Proportion of product returns
0.3
0.4
0.3
Problem Size
The optimization problem has 30 binary variables, 48,066 continuous variables
and 3972 constraints. The binary variables represent the selection of potential
sites for DC, HF or RC. The continuous variables represent the distribution plan
between manufacturing plants, DCs, HFs, RCs and retailers. The various constraints
represent capacity and demand restrictions and conservation of flow at each facility
for each period. The optimization problem was solved using CPLEX. The solver
took 0.1619 seconds to solve the problem.
Solution and discussion
The maximum profit obtained by the CPLEX solver was $1,277,540. The optimal
network structure uses 3 DCs (locations 1, 7 and 10) and 1 HF (location 10). No
RC is built. Both a DC and a HF are built at location 10 since transportation cost to
retailer 2 from location 10 is minimum. Retailer 2 has the maximum proportion of
demand and the maximum proportion of returns (see Table 1). The optimal network
flow for new products, remanufactured products and consumer returns are shown in
Figs. 4, 5 and 6.
In Fig. 4, we observe that the DC at location 1 serves new products to retailer
1, the DC at location 10 serves new products to retailer 2 and the DC at location
7 serves new products to retailer 3. In Fig. 5 we observe that DC at location 1
serves remanufactured product to retailer 1 and retailer 3, the DC and the HF at
Fig. 4 Optimal network flow for new products
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A. Dhamodharan and A. Ravi Ravindran
Fig. 5 Optimal network flow for remanufactured products
Fig. 6 Optimal network flow for consumer returns
location 10 serves remanufactured product to retailer 2, the DC at location 7 serves
remanufactured product to retailer 2 and retailer 3. In Fig. 6 we observe that the
HF at location 10 collects all consumer returns from retailers 1, 2 and 3 and returns
them to manufacturing plants MP1, MP2 and MP3. The flexibility offered by the
HF in handling products both in forward and reverse supply chains is utilized by the
model for supply chain efficiency.
Figure 7 shows the evolution of product sales over the product lifecycle with
respect to new products and remanufactured products. We note a decrease in
new product sales in period 25, which is attributed to the introduction of the
remanufactured product. Before the introduction of the remanufactured product,
new products are sold to 73% of the market at $565 per phone. After the introduction
of the remanufactured products, the new products are sold to only 60% of the
market, which is the proportion of quality-sensitive consumers in the market, at
$600 per phone. Thus, after the introduction of the remanufactured product, it is
only optimal to sell the new products to quality-sensitive consumers and the demand
for new products have been cannibalized by 13%.
Product Life Cycle Optimization Model for Closed Loop Supply Chain Network Design
83
Fig. 7 Evolution of new and remanufactured product sales
Since we limit the number of times a customer can repeat the purchase of a
product to one, we see that new product sales has a bimodal shape. The first peak
in the new product sales curve is caused by the first-time customers, both qualitysensitive and price-sensitive consumers. The second peak is caused by the qualitysensitive repeat consumers. Hence, the cumulative new product sales volume before
the new product sales peak is lower than the cumulative new product sales volume
after the peak. If the spread of the residence time is narrower, we can see a more
pronounced second peak due to repeat purchase, as discussed in Debo et al. (2006).
The remanufactured product sales have a bell-shaped curve with a sharp increase to
the peak, followed by a smooth decrease. The sharp increase to the peak is caused
by the price-sensitive first-time consumers. The smooth decrease is due to the pricesensitive repeat consumers who replaced their first-time purchase of a new product
with a remanufactured product.
Figure 8 shows the evolution of product sales over the product life cycle with
respect to first-time sales and repeat sales. We see the decrease in first-time sales at
period 25, which can be attributed to the introduction of the remanufactured product
into the market. We also observe that the peak of first-time sales occurs in period
43, and the peak of repeat sales occurs in period 68. The gap of 25 time periods
between the peaks is explained by the unimodal shape of the residence function,
with its maximum value at 25 periods after the purchase (see Fig. 3). The repeat
sales is more skewed than the first time sales due to the delayed introduction of the
remanufactured product.
Figure 9 shows the availability of returns to remanufacture and shortages in
remanufactured products. In period 25, we see a sharp increase in shortages as
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Evolution of First-Time Sales and Repeat Sales
350
300
250
200
150
100
Repeat Sales
First-Time Sales
Fig. 8 Sales across product life cycle: first-time sales and repeat sales
160
140
120
100
Fig. 9 Availability of returns and shortage of remanufactured product
Product Life Cycle Optimization Model for Closed Loop Supply Chain Network Design
85
sufficient returns are not available in the market to satisfy the demand for the
remanufactured product. However, from period 57 onwards, we see no shortages
of remanufactured products.
3.6 Illustration of Profit Reduction due to CLSC Network
Design in Sequential Manner
In this section, we illustrate to OEMs, the advantage of considering an integrated
CLSC network design using PLCOM when initially setting up their supply chain
network for a new product. PLCOM determines the optimal CLSC network design
in an integrated manner, considering both forward and reverse supply chains
together. We use the case study described previously to show the decrease in profit
over the product life cycle, if the OEM solves the CLSC network design problem
in a sequential manner instead of using PLCOM. In the sequential manner of
determining the CLSC network design, the OEM initially determines the optimal
forward supply chain network design and implements it. Later, when the OEM
decides to participate in the remanufactured market, the OEM revisits the network
design to obtain the optimal CLSC for the reverse supply chain.
In the illustrative example, the OEM would first solve for optimal forward supply
chain network design for the first 24 time periods (2 years) and implement the
solution. Then, the OEM would solve for optimal CLSC network design from period
25 onwards using PLCOM, with the existing forward network design imposed as
additional constraints to the PLCOM. The optimal forward supply chain network
for the illustrative example consists of DCs at locations 1, 7, 9 and 10. Hence, we
add additional constraints that DCs 1, 7, 9 and 10 are open to the PLCOM and solve
for the optimal CLSC network when considering reverse supply chains also. The
optimal CLSC network design adds the RC at location 7 to the optimal forward
network design to accommodate the reverse flows.
Table 2 compares the optimal network designs and profits obtained from the
integrated and sequential approaches for CLSC network design. There is an
additional RC chosen at location 7 and a DC at location 9 in the sequential approach,
compared to the integrated approach. Also, no HF is chosen in the sequential
approach. Since HFs were not chosen as part of the forward supply chain network
design, at least one HF or RC had to be chosen to handle consumer returns. Also,
the sequential approach imposes additional constraints to the PLCOM, resulting
in a decrease in the profit compared to the integrated approach by 28%. Thus, if
the OEM designs the CLSC network in a sequential manner, the profit over the
product lifecycle will be significantly reduced due to the existing forward supply
chain network. Hence, OEMs should take a long term view of the network design
problem and consider both the forward and reverse flows in the network design as
part of their decision-making process while launching a new product in the market.
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Table 2 Comparison of optimal network designs and profit between integrated and sequential
approaches
Factors
Optimal network design
Profit during product lifecycle
Integrated CLSC
DC:1,7,10; HF:10
$1,277,540
Sequential CLSC
DC:1,7,9,10; RC:7
$923,222
3.7 Sensitivity Analysis
Optimal Time Period to Introduce Remanufactured Product in the Market
In this subsection, we conduct a sensitivity analysis on the parameter intro, the time
period when the remanufactured product is introduced into the market, using the
illustrative example. The value of the parameter intro is varied from 25 to 75, and
the PLCOM is solved in an integrated manner to study its impact on the following:
•
•
•
•
•
•
•
network design,
profit,
shortage,
new product sales,
remanufactured product sales,
first time sales and
repeat sales.
Impact on Profits
Figure 10 plots optimal profit and shortage of remanufactured products, over the
product lifecycle, against the intro values from 25 to 75. The optimal profit increases
as the intro value increases to 45, then decreases smoothly until the intro value of
55. From the intro values of 60 onwards, we see a sharp decrease in the optimal
profit. We conclude that 40 is the most profitable time period to introduce the
remanufactured product into the market. Time period 60 and beyond is too late for
the OEM to realize maximum profits from the remanufacturing market.
Impact on Shortages
Also note that in Fig. 10, the total shortage over the product life cycle is 3500 for
the intro value of 25. As the intro value increases, that is, when the remanufactured
products enter the market later, there is a decrease in shortages because there are
more product returns available for remanufacturing. Interestingly, the minimum
shortage value of 0 occurs for intro values of 55 or more. The maximum profit
scenario with intro value of 40 has a shortage of 744 remanufactured units.
Product Life Cycle Optimization Model for Closed Loop Supply Chain Network Design
87
Fig. 10 Profit and shortage over product life cycle
Impact on New and Remanufactured Product Sales
Figure 11 plots new product sales, remanufactured product sales and total sales
over the product lifecycle against the intro values from 25 to 75. We observe that
both remanufactured product sales and the total sales decrease in a linear fashion
with higher values of intro. This is explained by the fact that as the value of
Fig. 11 New and remanufactured product sales over product life cycle
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Fig. 12 First time and repeat sales over product life cycle
intro increases, the time periods left for the price-sensitive consumers to purchase
the remanufactured products reduce. On the other hand, we see an increase in
new product sales for higher values of intro because the new product demand is
cannibalized after intro.
Impact on First-Time and Repeat Sales
Figure 12 plots first time sales repeat sales and total sales, over the product lifecycle
against the intro values from 25 to 75. We observe that first-time sales decreases
with an increase in intro value, while the repeat purchase remains constant. Firsttime sales made by the price-sensitive and the quality-sensitive consumers decreases
with an increase in intro value since price-sensitive consumers do not have enough
time to make first-time purchases of remanufactured products. There are only a
fixed number of quality-sensitive consumers in the market. The model restricts the
number of repeat purchases to one. Hence, repeat purchases are flat and independent
of intro value.
3.8 Sensitivity Analysis
Impact on the Supply Chain Network
Table 3 shows the optimal CLSC network design for different values of intro. It is
interesting to note that the DCs and the HFs chosen are the same irrespective of the
Product Life Cycle Optimization Model for Closed Loop Supply Chain Network Design
Table 3 Optimal network
designs for different values of
intro
Intro
25
30
35
40
45
50
55
60
65
70
75
DC
1,7,10
1,7,10
1,7,10
1,7,10
1,7,10
1,7,10
1,7,10
1,7,10
1,7,10
1,7,10
1,7,10
HF
10
10
10
10
10
10
10
10
10
10
10
89
RC
–
10
1
10
7
–
–
–
–
–
–
intro value. As intro value is increased from 25 to 30, 35, 40 and 45, we find that it is
profitable for the CLSC network to operate an additional RC in order to reduce the
shortage of the remanufactured products. For intro value of 50, in spite of a shortage
of 534 units of the remanufactured products (see Fig. 11), it is not profitable to
operate the additional RC as the fixed cost of the facility exceeds the profit made
from the sales of the 534 units. For intro value of 55 onwards, it is not profitable to
operate the additional RC due to the reduced volume of the remanufactured sales.
In summary, for the case study, the OEM can achieve a maximum profit of
$1,514,000 by introducing the remanufactured product at time period 40. The
optimal CLSC network will have:
• DCs at locations 1, 7 and 10
• an RC at location 10
• a HF at location 10
In other words, just at location 10, the OEM will have a DC, RC and HF.
The reason behind choosing location 10 is the minimum transportation cost to
retailer 2, who faces the maximum proportion of demand and collects the maximum
proportion of returns (see Table 1).
3.9 Characterization of Slow and Fast Diffusing Products
In this subsection, we conduct a sensitivity analysis on the parameter imitation,
which creates new demand through the word-of-mouth effect. As the value of the
imitation coefficient is increased, the produce lifecycle grows shorter. We define
fast diffusing products as products with lifecycle shorter than twice the maximum
product lifetime and slow diffusing products as products with lifecycle longer than
twice the maximum product lifetime. In our case study, the maximum product
lifetime is 36 time periods. Hence, products with a lifecycle shorter than 72 periods
are considered fast diffusing products, and products with a lifecycle longer than 72
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A. Dhamodharan and A. Ravi Ravindran
periods are considered slow diffusing products. We solve the illustrative example
using the PLCOM for lifecycle values of 100 periods, 80 periods, 60 periods and
40 periods. The products with lifecycle values of 40 and 60 periods are considered
as fast diffusing products, while the products with lifecycle values of 80 and 100
periods are considered as slow diffusing products. Note that in all the 4 cases the
total market size is maintained at 10,000.
Impact on Profit and Network Design
Table 4 compares the profit and the optimal network designs for the abovementioned values of the product lifecycle. We find that the fast diffusing product
with a lifecycle value of 40 periods results in a loss to the OEM, as it requires
extensive investment to open DCs at 6 different locations and HF at location 10,
in order to handle the high volume of product flow in each period. Hence, when
designing a supply chain for fast diffusing products, we recommend the OEM to
plan for flexible capacity, so that the fixed cost of opening facilities is minimal, as
noted by Debo et al. (2006). In the case of a fast diffusing product with a lifecycle
value of 60 periods, the OEM is able to make a profit by opening DCs at 3 different
locations and HF at location 10. The profit is significantly lower than the profit made
from slow diffusing products due to a high shortage of consumer returns. In the case
of slow diffusing products, we notice that the profit is higher for a product with a
longer lifecycle.
Under a fixed capacity setting, as the product lifecycle grows longer, the demand
is more evenly distributed. Thus, we expect the number of facilities in the supply
chain network to remain about the same. From Table 4, we notice that the number of
facilities in the network for the lifecycle value of 60 periods is lower than the number
of facilities for the lifecycle value of 40 periods. Similarly, the number of facilities
in the network for the lifecycle value of 80 periods is lower than the number of
facilities for the lifecycle value of 40 periods. Finally, the number of facilities in the
network for the lifecycle value of 100 periods is lower than the number of facilities
for the lifecycle value of 40 periods and 80 periods and is the same as the number
of facilities in the network for the lifecycle value of 60 periods. Interestingly, we
observe that the number of facilities in the network for the lifecycle value of 80 has
an additional facility compared to the network for the lifecycle value of 60. The
additional facility is to satisfy the demand for the remanufactured products from
cannibalization, explained later in this section.
Table 4 Profit and optimal network design for slow and fast moving products
Category
Fast diffusing
Fast diffusing
Slow diffusing
Slow diffusing
Lifecycle
40
60
80
100
Profit
−$1,853,000
$390,900
$1,035,000
$1,277,540
DC
1,2,7,8,9,10
1,7,10
1,7,10
1,7,10
HF
10
10
10
10
RC
–
–
7
–
Product Life Cycle Optimization Model for Closed Loop Supply Chain Network Design
91
Impact on New and Remanufactured Product Sales
Figure 13 plots new product sales and remanufactured product sales over the
product lifecycle for different lifecycle values. We notice that as the product
lifecycle shortens, the peaks from the first-time sales and the repeat sales are more
pronounced. In the case of a fast diffusing product with a lifecycle value of 40
periods, we see a big drop in the new product sales when the remanufactured product
is introduced. This indicates a big proportion of new product consumers switching
from the new product to buy the remanufactured product. The revenue loss from
the demand cannibalization also results in a net loss to the OEM, as shown in Table
4. Thus, in this case, it is clearly not profitable for the OEM to participate in the
remanufacturing market. In the case of fast diffusing products with a lifecycle value
of 60 periods, we notice that the demand cannibalization is significantly lower, and
the OEM makes a net profit. In order to analyze whether it is profitable for the
OEM to participate in the remanufactured market, we solve only for the forward
supply chain and find that the OEM would incur a net loss operating only in the
forward supply chain, due to fixed capacity. Hence, we conclude that in this case, it
is profitable for the OEM to participate in the remanufacturing market.
Fig. 13 New and remanufactured product sales for different values of product lifecycle
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A. Dhamodharan and A. Ravi Ravindran
Impact on First-Time Sales, Repeat Sales and Shortage
Figure 14 plots first-time sales, repeat sales and shortage over the product lifecycle,
for different lifecycle values. We notice that, as the product lifecycle grows shorter,
there is an increase in the amplitudes of the first-time sales, repeat sales and the
shortage functions. We also find that the total first-time sales increases and the total
repeat sales decreases, due to lack of time for the product to fail. Initially, the total
shortage increases due to the shortage of consumer returns. In the case of lifecycle
value of 60 periods, we notice a second peak in shortage, coinciding with the peak
of repeat sales. This second peak is due to increased demand for remanufactured
products from the delayed cannibalization effect. The delayedcannibalization effect
occurs when the price-sensitive consumers, who initially bought the new product,
before the introduction of the remanufactured product, switch to buy the remanufactured product when making their repeat purchase. In the illustrated example, the
delayed cannibalization effect is expected to occur from periods 38 to 61, as shown
in Fig. 14. As the product lifecycle grows longer, the number of consumers causing
the delayed cannibalization effect is smaller and hence more evenly distributed,
providing time to collect consumer returns and satisfy the increased demand for
the remanufactured product. Thus, in the case of the lifecycle value of 80 periods,
we observe the second shortage peak with a significantly lower amplitude, and in
the case of the lifecycle value of 100 periods, there is no second shortage peak. This
also explains why the optimal network design for the lifecycle value of 80 periods
has an additional RC at location 7 compared to the lifecycle value of 60 periods.
Fig. 14 First-time sales, repeat sales and shortage for different values of product lifecycle
Product Life Cycle Optimization Model for Closed Loop Supply Chain Network Design
93
The additional RC is used to collect more returns and minimize the shortage, as
illustrated in Fig. 14. However, the additional RC is not required in the case of the
lifecycle value of 100 periods, because of the very small increase in the demand for
remanufactured products from the delayed cannibalization.
In summary, we conclude the following from the sensitivity analysis of the
imitation coefficient:
• Slow diffusing products are more profitable for remanufacturing compared to fast
diffusing products because there is enough time to collect consumer returns
• OEMs dealing with fast diffusing products should plan for flexibility in capacity
in order to avoid the fixed cost of opening additional facilities
• Managers should understand the effect of delayed cannibalization when designing the reverse channel and planning for the collection of consumer returns
3.10 Incentivizing Consumers to Improve the Quality
of Returns
We conduct a sensitivity analysis on the quality of the consumer returns, which is
defined as the percentage of consumer returns that are qualified to be remanufactured. In the illustrative example, we assume that 80% of the returns are qualified
to be remanufactured. We solve the illustrative example using the PLCOM for
consumer return quality values of 60%, 70%, 80% and 90%. Table 5 shows the
profit and the optimal network design for the 4 values of quality mentioned above.
We expect that as the quality of the consumer returns increases, the profit should
also increase, as shown in Table 5. Also, we notice from the table that the number
of facilities in the optimal network increases with an increase in the quality of the
returns. This is also expected as an increase in the quality of the consumer returns
increases the available remanufactured products, resulting in the handling of a larger
number of consumer returns and remanufactured products by the CLSC network.
Note that there are 4 facilities in the optimal network for the quality values of 60%,
70% and 80% while there are 5 facilities in the optimal network for the quality
value of 90%. The additional RC facility handles the extra remanufactured products
produced from the higher-quality consumer returns.
Table 5 Profit and optimal network design for different values of the quality of consumer returns
Quality of consumer returns
60%
70%
80%
90%
Profit
$960,000
$1,190,000
$1,277,540
$1,454,000
DC
7,10
1,7,10
1,7,10
1,7,10
HF
1
–
10
10
RC
7
10
–
1
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A. Dhamodharan and A. Ravi Ravindran
It is in the interest of the OEMs to collect consumer returns of high quality. Thus,
the OEMs can provide incentives to consumers to encourage high-quality returns.
Such a practice is followed in the smartphone industry, where the consumer gets
a rebate when buying a new smartphone for returning a good quality smartphone.
The difference in profits for the various quality values (Table 5) provides an upper
bound on the incentives that can be provided by the OEM, without incurring loss.
For example, if the OEM is collecting consumer returns of 60% quality, then the
OEM is justified to spend up to $229,700 (the difference between $1,1,90,000 and
$960,300) in consumer incentives, to ensure a 70% quality of consumer returns.
Figure 15 plots the new product sales and the remanufactured product sales over
the product lifecycle, for different values of the quality of consumer returns. We
notice that, as the quality of the consumer returns increases, the second peak of the
new product sales and the remanufactured product sales increase due to an increase
in repeat purchase. We also find that as the quality of the consumer return increases,
the total new product sales and the total remanufactured product sales increase in a
linear fashion.
Figure 16 plots the first-time sales, the repeat sales and the shortage over the
product lifecycle, for different values of quality of consumer returns. We notice that
as the quality of the consumer returns increases, the total first-time sales and the
total repeat sales increase. However, as the quality of consumer returns increases,
the total shortages do not monotonically increase. This can be explained by the
following example: When the quality of consumer return value increases from 60%
from 70%, the total shortage increases from 2743 units to 3079 units. The profit in
the case of 70% quality of consumer return is still higher than the profit in the case
Fig. 15 New and remanufactured product sales for different values of the quality of consumer
returns
Product Life Cycle Optimization Model for Closed Loop Supply Chain Network Design
95
Fig. 16 First-time sales, repeat sales and shortage for different values of the quality of consumer
returns
of 60% quality of consumer returns (see Table 5) due to the cost savings from the
network design. Note that the optimal network design for 60% quality of consumer
returns opened a HF at location
1, which costs $50,000 more than the DC at location 1 opened in the optimal
network design for 70% quality of consumer returns (see Table 5). Since a HF can
handle both products and returns but a DC can only handle products, we notice an
increase in shortage in the case of 70% quality of consumer returns.
In summary, we conclude the following from the sensitivity analysis on the
quality of the consumer returns:
• Higher quality of consumer returns provide higher profit for the OEMs
• The analysis provides OEMs insights on how much to spend on consumer
incentives in order to improve the quality of the consumer returns, without
incurring a net loss from the incentives
Total sales usually increase with an increase in the quality of consumer return
• Total shortages do not always decrease with an increase in the quality of
consumer returns. Sometimes it may be optimal to have a higher shortage due
to the high fixed cost of opening an additional facility.
96
A. Dhamodharan and A. Ravi Ravindran
4 Conclusions
In this chapter, we began by introducing the PLCOM and illustrated the evolution
of new and remanufactured product sales, first-time and repeat sales and shortage
of remanufactured products over the product lifecycle using a case study. The case
study also quantified the cannibalization of the new product demand, a phenomenon
that deters the OEMs from participating in the market for the remanufactured
products. We continued with the same example to illustrate that an integrated
approach to design a CLSC network is more profitable for the OEM to participate
in the reverse supply chain.
Sensitivity analysis on the time period to introduce the remanufactured product in
the market identified the optimal time period maximizing the profit and showed the
tradeoff between the shortage of remanufactured products and the new product sales.
Sensitivity analysis on imitation coefficient showed that slow diffusing products
are more profitable in the reverse channel compared to fast diffusing products. We
also identified the delayed cannibalization phenomenon in case of fast diffusing
products caused by the price-sensitive consumers switching to the remanufactured
product during their repeat purchase. Finally, the sensitivity analysis on the quality
of consumer returns provided insights into how much the OEM can spend on
incentives to improve the quality of consumer returns, without incurring a net loss
from the incentives.
Appendices
Appendix 1
Pricing Model
Quality-sensitive consumers will only buy new products and are not motivated
by price discounts. The willingness to pay (WTP) function for quality-sensitive
customer segment is modeled as a linear function given by:
f (θ ) = α1 + β1 θ
(34)
where θ in (0,1) and (1- θ) is the fraction of the customer segment with
WTP = α1 + β1 θ.
Price-sensitive consumers are indifferent to whether the product is new or
remanufactured and are purely motivated by the price of the product. Hence, there
is no need to model new and remanufactured products separately. The willingness
to pay (WTP) function for this customer segment is modeled as a linear function
given by:
f (θ ) = α2 + β2 θ
(35)
Product Life Cycle Optimization Model for Closed Loop Supply Chain Network Design
97
where θ in (0,1) and (1- θ) is the fraction of the customer segment with
WTP = α2 + β2 θ, α2 is the maximum selling price at which all the customers in
this price-sensitive category are willing to buy the product (new or remanufactured).
The proportion of price-sensitive consumers, who are willing to buy
new/remanufactured product for price pr , denoted by qr , is given by:
pr − α2
qr = (1 − λ) 1 −
β2
(36)
where λ is the fraction of quality-sensitive customers.
∗
pn1
=
1 λ β2 (α1 + β1 ) + (1 − λ) β1 (α2 + β2 )
cn
+
2
2
λ β2 + (1 − λ) β1
(37)
λ β2 (α1 + β1 ) + (1 − λ) β1 (α2 + β2 )
λ β2 + (1 − λ) β1
(38)
∗
qn1
=
where:
cn : manufacturing cost of new product
cr : remanufacturing cost
λ: the fraction of quality-sensitive consumer segment
1- λ: the fraction of the market falling under price-sensitive consumer segment
In order to make use of the above equations, we assume that α2 < α1 ≤ α2 + β2 ,
∗ ≥
i.e., there is overlapping in WTP between the two consumer categories. Also, pn1
∗
α1 and α2 ≤ pn1 ≤ α2 + β2 .
When the OEM introduces the remanufactured product into the market, assuming
no constraint on the production of remanufactured products, the optimal pricing pn∗ ,
pr∗ and the optimal production quantity qn∗ , qr∗ , expressed as a fraction of the market
demand for new products and remanufactured products, respectively, are given by
the following equations (Abbey et al. 2015a).
α1 + β1
cn
∗
+
= max α1 ,
pn2
2
2
∗
qn2
= λ min
α1 + β1 − cn
,1
2β1
(39)
(40)
α2 + β2
cr
∗
pr2
= max α2 , +
2
2
∗
qr2
α2 + β2 − cr
= (1 − λ) min
,1
2β2
(41)
(42)
We consider a multi-period optimization model in which the OEM tries to
maximize the revenue in each time period. The OEM initially sells only the new
98
A. Dhamodharan and A. Ravi Ravindran
product until the introduction of the remanufactured product. During those periods,
we use Eqs. (37) and (38) to decide the pricing and quantity of the new product
produced. Let intro be the time period when the OEM introduces the remanufactured
product into the market. During the periods after the introduction of remanufactured
products, assuming no capacity restriction on producing new products, we use
Eqs. (39) and (40) as the new price and the new production quantity for the
new product. For the remanufactured products, however, the optimization model
constraints the production quantities based on the quantity of consumer returns and
the remanufacturability of the consumer returns. Hence, if the availability of the
consumer returns in a period exceeds the demand for remanufactured products, we
use the Eqs. (41) and (42) to make the pricing and the production quantity decisions
for the remanufactured products. If the availability of the consumer returns in a
period is lower than the remanufactured product demand, then we assume that the
OEM sells all the available inventory of remanufactured products at pr∗ , and the
shortages are lost sales.
In order to make use of the Eqs. (39) through (42), we need to make the following
assumptions:
cn > cr . The manufacturing cost of the new product is greater than the remanufacturing cost
α1 > α2 . The minimum WTP for the new product is greater than the minimum WTP
for the remanufactured product
α1 + β1 > α2 + β2 . The maximum WTP for the new product is greater than the
maximum WTP for the remanufactured product
All the three assumptions are very practical. Under such assumptions, pn∗
> pr∗ . Hence, when remanufactured product is available in the market, pricesensitive customers will not buy the new product. Thus, new product demand might
decrease due to the introduction of the remanufactured product, resulting in demand
cannibalization.
Appendix 2
Demand Model
Debo et al. (2006) extended the Bass model to study life cycle demand of new
and remanufactured products and analyzed the cost-effectiveness of investments in
capacity for remanufacturing, as explained in the literature review section. Debo et
al. (2006) assumed a specific OEM selling price function for both the new and the
remanufactured products, without considering the market structure. We adopt their
model to generate demand for both new and remanufactured products throughout
the product life cycle and then integrate it with the OEM’s pricing model, which is
described in the previous section. The demand model is described below.
Product Life Cycle Optimization Model for Closed Loop Supply Chain Network Design
99
Product Life Cycle Parameters:
marketsize: refers to the total size of the market considered. marketsize is normalized
to 1
marketdemandt: total demand (for new and remanufactured products) in period t as
a fraction of the marketsize
marketexpt : market size expansion for both new and remanufactured products due to
the extended Bass model, in period t, expressed as a fraction of the total market
size
markett : market size of first-time product owners using new and remanufactured
products at the end of period t, expressed as a fraction of the total market size
innov: innovation coefficient of the product that creates new demand. The coefficient
is expressed as a fraction of the total market size
imitation: imitation coefficient that creates new demand through word-of-mouth
effect from existing quality-sensitive consumers. This coefficient is also
expressed as a fraction of total market size
ϕ: the fraction of current product owners, making a repeat purchase. This parameter
is treated as a constant over time
residencej : the fraction of current product owners, whose products will fail at period
j
repeatt : demand from repeat purchases by current product owners in period t,
expressed as a fraction of markett
L: product lifetime
intro: time period when OEM introduces new product
newprdownt : number of consumers who own the new product in period t
returnst : number of consumer returns of new product in period t. Note that we do
not consider returns of the remanufactured product. The consumer return may be
a commercial return or end of life return
Qnewt : demand for new product at the beginning of period t. Since we assume no
capacity restrictions at MP, this is also the number of consumers who bought the
new product at the beginning of period t. Qnewt = qn∗ (marketdemandt)
Demand Equations
In the extended Bass model, the market size for new and remanufactured products
grows each period due to the appeal of the product to the market, denoted by the
innovation coefficient, and word-of-mouth from the existing customers, denoted by
the imitation coefficient. Market size expanded during period t is a fraction of the
uncaptured market (1-markett-1) that is open to buying the product, as given by the
Eq. (43).
marketexpt = (innov + imitation newprodownt −1 ) (1 − markett −1)
(43)
100
A. Dhamodharan and A. Ravi Ravindran
Market demand for new and remanufactured products in period t, given by the
Eq. (44), is the sum of market expanded during period t and repeat consumers.
marketdemandt = (marketexpt + repeatt ) marketsize
(44)
Market demand is split into new and remanufactured product demand using
the parameter λ, defined as the fraction of quality-sensitive customers in the
market. Hence, the new product demand is given by λ (marketdemandt) and the
remanufactured product demand is given by (1 − λ) (marketdemandt).
The market size of first-time product owners, given by Eq. (45) using both new
and remanufactured products at the end of period t, is updated as the sum of the
market size at the end of the previous period and the market size growth during
period t.
markett = markett −1 + marketexpt
(45)
In the extended Bass model, the term residence time distribution is used to refer
to the probability distribution of the number of time periods for which the product
(new or remanufactured) stayed with a customer. Hence, the number of new product
owners at the beginning of period t is obtained by subtracting the number of new
product consumers who stopped owning the product at the end of the period (t-1)
and adding the number of consumers who bought the new product at the beginning
of period t, as given by the Eq. (46).
newprodownt = newprodownt −1 +Qnewt −
L
residencej newprodownt −j −1
j =1
(46)
where Qnewt = qn∗ marketdemandt .
At the end of the residence time, the customer is assumed to return the new
product. At this time, the customer is treated as a potential repeat customer of
the new product. Parameter repcust represents the fraction of the potential repeat
customers who actually repeat purchase, making them repeat customers. The size
of the repeat customers in period t is given by Eq. (47).
repeatt = repcust
min(t,L)
residencej − marketexpt −j + repeatt −j
(47)
j =1
Consumer returns at the beginning of period t is given by the sum product
of residence and market size that owns new products. Repeat consumers, part of
newprodown, can once again return their new product, as given by Eq. (48).
returnst =
L
j =1
residencej newprodownn,t −j
(48)
Product Life Cycle Optimization Model for Closed Loop Supply Chain Network Design
101
Appendix 3
Integration of Pricing and Demand Models
In the multiperiod optimization model, we model the demand (both new and remanufactured products) in each period based on the extended Bass model, given by Eqs.
(44) to (46). However, the production quantities of new and remanufactured prod∗ , q ∗ , q ∗ ), are expressed as a fraction of the market demand for new products (qn1
n2 r
ucts and remanufactured products, based on the pricing model, as given below:
1. During time periods when only the new products are produced (upto t < intro),
∗
we use Eq. (38) from Appendix 1 to determine qn1
2. During time periods when both new and remanufactured products are produced
∗ , for new
t ≥ intro), we use Eq. (40) from Appendix 1 for determining qn2
products,
3. For remanufactured products, we use Eq. (42) from Appendix 1 for determining
qr∗ ,
The marketdemand, for new and remanufactured products in period t is given by
Eq. (44). Using marketdemand, the production quantities for new and remanufactured products are determined as follows:
Let Qnewt be the production quantity of new product. Then,
∗ , we get:
(i) Using Eq. (38) from Appendix 1 for qn1
Qnewt =
λ
α1 + β1 − pn∗
1 − β1
+ (1 − λ)
α2 + β2 − pn∗
1 − β2
marketdemandt
(49)
for period t < intro, when only the new product is produced.
∗ , we get:
(ii) Using Eq. (40) from Appendix 1 for qn2
Qnewt =
λ min
α1 + β1 − cn
,1
2β1
marketdemandt
(50)
for period t, when both the new product and the remanufactured product are
produced.
(iii) Let Qremt be the weighted production quantity for the remanufactured product.
Qremt = qr∗ marketdemandt . Using Eq. (42) from Appendix 1 for qr∗ , we
get:
Qremt =
(1 − λ) min
α2 + β2 − cr
,1
2β2
marketdemandt
(51)
102
A. Dhamodharan and A. Ravi Ravindran
Appendix 4
Table 6 Transportation cost
from manufacturing plant 1
Plant
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
DC/HF/RC location
1
2
3
4
5
6
7
8
9
10
1
2
3
4
5
6
7
8
9
10
1
2
3
4
5
6
7
8
9
10
Retailer
1
1
1
1
1
1
1
1
1
1
2
2
2
2
2
2
2
2
2
2
3
3
3
3
3
3
3
3
3
3
Cost ($)
0.5
0.55
0.6
0.65
0.7
0.75
0.8
0.85
0.9
0.95
0.95
0.9
0.85
0.8
0.75
0.7
0.65
0.6
0.55
0.5
0.7
0.75
0.8
0.85
0.9
0.95
0.5
0.55
0.6
0.65
Product Life Cycle Optimization Model for Closed Loop Supply Chain Network Design
103
Table 7 Transportation cost
from manufacturing plant 2
Plant
2
2
2
2
2
2
2
2
2
2
2
2
2
2
2
2
2
2
2
2
2
2
2
2
2
2
2
2
2
2
DC/HF/RC location
1
2
3
4
5
6
7
8
9
10
1
2
3
4
5
6
7
8
9
10
1
2
3
4
5
6
7
8
9
10
Retailer
1
1
1
1
1
1
1
1
1
1
2
2
2
2
2
2
2
2
2
2
3
3
3
3
3
3
3
3
3
3
Cost ($)
0.5
0.55
0.6
0.65
0.7
0.75
0.8
0.85
0.9
0.95
0.95
0.9
0.85
0.8
0.75
0.7
0.65
0.6
0.55
0.5
0.7
0.75
0.8
0.85
0.9
0.95
0.5
0.55
0.6
0.65
Table 8 Transportation cost
from manufacturing plant
Plant
3
3
3
3
3
3
3
3
3
DC/HF/RC location
1
2
3
4
5
6
7
8
9
Retailer
1
1
1
1
1
1
1
1
1
Cost ($)
0.5
0.55
0.6
0.65
0.7
0.75
0.8
0.85
0.9
(continued)
104
Table 8 (continued)
A. Dhamodharan and A. Ravi Ravindran
Plant
3
3
3
3
3
3
3
3
3
3
3
3
3
3
3
3
3
3
3
3
3
DC/HF/RC location
10
1
2
3
4
5
6
7
8
9
10
1
2
3
4
5
6
7
8
9
10
Retailer
1
2
2
2
2
2
2
2
2
2
2
3
3
3
3
3
3
3
3
3
3
Cost ($)
0.95
0.95
0.9
0.85
0.8
0.75
0.7
0.65
0.6
0.55
0.5
0.7
0.75
0.8
0.85
0.9
0.95
0.5
0.55
0.6
0.65
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Supply Chain Risk Management in
Indian Manufacturing Industries:
An Empirical Study and a Fuzzy
Approach
V. Viswanath Shenoi, T. N. Srikantha Dath, and Chandrasekharan Rajendran
1 Background and Motivation
The evolution of the free trade agreement during the past decade has facilitated
the movement of goods across the world (Moore and Moore, 2003). This enabled
companies to compete in the international markets with products produced for
the domestic markets without any trade barriers. Further, the liberalization and
the consequent economic reforms in India such as Foreign Direct Investment
(FDI) led to substantial investments in the manufacturing sector which not only
augmented as an opportunity for the revival of the Indian economy (Kumar, 2005)
but also led to increased competition. Globalization and competitive environments
necessitated companies to implement an aggressive and integrated enterprise-wide
approach towards risk management (Saeidi et al., 2019). Hence the implementation
of Supply Chain Risk Management, a critical strategic approach has become a
top priority. For managing and mitigating risk, effective SCRM systems control adverse outcomes through systematic implementation are required (Manuj
V. Viswanath Shenoi ()
Department of Computer Science and Engineering, Amrita College of Engineering and
Technology, Nagercoil, Tamil Nadu, India
e-mail: v_viswanathshenoi@amrita.edu.in
T. N. Srikantha Dath
Department of Mechanical and Manufacturing Engineering, M S Ramaiah University of Applied
Sciences, Bengaluru, Karnataka, India
e-mail: srikanthadath.me.et@msruas.ac.in
C. Rajendran
Department of Management Studies, Indian Institute of Technology Madras, Chennai, Tamil
Nadu, India
e-mail: craj@iitm.ac.in
© Springer Nature Switzerland AG 2021
S. Srinivas et al. (eds.), Supply Chain Management in Manufacturing and Service
Systems, International Series in Operations Research & Management Science 304,
https://doi.org/10.1007/978-3-030-69265-0_4
107
108
V. Viswanath Shenoi et al.
et al., 2014). This study provides a platform for researchers to study the concept
of SCRM and discover the underlying issues. Further, the practicing managers
can utilize the presented instruments to review and to redefine their SCRM
issues.
The study is done in two folds. First, the critical factors of SCRM are identified
and a framework is developed. The developed framework helps the researchers to
understand the relationship between the critical risk-related factors. The empirical
study is performed to gain the perceptions of the practicing managers about risks
perceived in their organization. In the second part, the critical factors of SCRM
are constituted as the states of the Fuzzy Cognitive Map, which represents the
dynamical system of the supply chain. The system helps researchers to identify
all plausible risks in the long run, given a risk observed from a point of time, and
suggests mitigation strategies for practicing managers. This chapter is organized
as follows: Sect. 2 describes the literature evidence of the supply chain risk
and mitigation strategies. Section 3 deals with the methodology of the research
frameworks and explains each of the methods used in the study. Then results are
presented in Sect. 4. The detailed discussion of the results can be seen in Sect. 5 and
the conclusion in Sect. 6.
2 Literature Review
2.1 Critical Factors for SCRM
According to the Supply Chain Operations Reference Model (SCOR) model,
the possible disruptions in a supply chain may occur inside the supply chain,
like inconsistent in quality or uncertainty in demand/supply, or external to the
supply chain, namely, strikes, natural calamities or terrorist attacks. Hence, the
probable disruptions should be systematically identified, evaluated and mitigated
through SCRM. The supply chain disruptions arise from three primary sources,
namely, environmental risks (e.g., socio-political actions and natural disasters),
organizational risks (e.g. machine breakdown, labor strikes and IT break-down
etc.), and network risks (e.g. interactions within the supply chain such as information sharing) (Sodhi and Chopra, 2004; Ghadge et al., 2013). Wagner and
Bode (2008) categorized the risks based on their location of occurrence. Demand
side and supply side risks are categorized as internal supply chain risks, and
bureaucratic, infrastructure, and catastrophic risks are categorized as external
supply chain risks. Trkman and McCormack (2009) classified risk based on
their source i.e. within or external to the supply chain. Oke and Gopalakrishnan
(2009) considered factors including the likelihood of a risk and its impact for
classification.
SCRM in Indian Manufacturing Industries
109
Description of the Construct of SCRM: Manufacturer’s Perspective
Demand Side Risk1 (DSR) For good business performance, organizations need to
deliver the required quantities of the product at the pre-agreed time and place. Issues
such as volatile customer demand, inaccurate demand forecasting, competitive rival
products, poor understanding of customer preferences, short product life cycle, and
defects in the products are predominant demand side risks (Sodhi and Chopra, 2004;
Wagner and Bode, 2006; Oke and Gopalakrishnan, 2009; Trkman and McCormack,
2009).
Supply Side Risk1 (SSR) The supplier plays an important role in providing the
sub-assemblies to the original equipment manufacturers (OEMs) or (Tier-2 supplier
to Tier-1 supplier). The risks faced by the suppliers are due to market characteristics,
quality problems, financial instability, technological changes, and product design
issues (Wagner and Bode, 2006; Trkman and McCormack, 2009; Thun and Hoenig,
2011).
Logistic Risk (LR) Logistics service providers deliver products and services at the
predestined place at the appropriate time, which is a characteristic of an effective
and efficient supply chain. For efficient logistics, choice of a transport, the type of
vehicle, and their utilization play a major role (KPMG, 2010; Rogers et al., 2012).
Regulatory, Legal and Bureaucratic risk1 (RLB) Government, top management
and administration take part in important decisions pertaining to planning (longterm, short-term), regulatory policies and reforms.While implementing a new plan,
policy, or reform, apart from confirming the overall cost-effectiveness, it has to be
ensured that every stakeholder in the supply chain gets an advantage (Hendricks and
Singhal, 2005; Wagner and Bode, 2006; Rogers et al., 2012).
Infrastructure Risk1 (IR) The infrastructure of the firm includes humans,
machines, buildings, and service systems, as they are all essential for the growth
and sustenance of the firm. So, it is mandatory for the firm to maintain cordial
Human Resource policies and perform regular maintenance (breakdown, preventive,
corrective) to machinery (FCCI, 2013).
Stock/Data Management risk1 (SDM) Stock information needs to be maintained
and shared across the supply chains Specifically for Vendor Managed Inventory
(VMI), it is vital to dispense the information across all the stakeholders of the supply
chain (Dath et al., 2009). Firms needs to maintain standard product identification,
EDI, CFAR, and customer preferences details to enable the vendors and suppliers
to know the expansions in the stock levels and replenish them suitably.
Environmental Risk1 (ER) Natural hazards, terrorist attacks on the establishment,
civil unrest, and epidemics disrupting the supply chain have been reported from the
1 Indicates the factors adapted appropriately and suitably from the work of Sodhi and Chopra (2004)
and Wagner and Bode (2006).
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V. Viswanath Shenoi et al.
various parts of the world. Certain events may be unavoidable, but one needs to be
prepared to face unfavorable/undesirable events (Oke and Gopalakrishnan, 2009;
Thun and Hoenig, 2011).
Financial Risk1 (FR) Firms need to have sound financial stature to sustain in the
competitive environment. Hence, it vital to maintain the optimum cash flow into the
business from the customers on time. Also, financial activities such as diversification
of the funds, inappropriate drawing of loans to fund the business, over-leveraging of
the funds out of business in high-risk investments are undertaken to maximize the
returns that may prove fatal to the firms’ existence (NMCC, 2008; Dath et al., 2009;
CRISIL, 2010).
Top Management Commitment2 (TMC) SCRM needs are better understood by
Senior-level managers as they form the decision making team, which enables the
firm to be competitive. They should show their commitment, by guiding and leading
an effective SCRM implementation process (Dath et al., 2009; KPMG, 2010).
Mitigation Strategies and Risk Management Process1 (MS-RMP) In the competitive and unstable environment, every firm inevitably faces risk. The risk in
the organization cannot be avoided, but it can be curtailed to a certain degree.
The organization can identify deviation, and implement appropriate strategies and
convert negative events into positive or near positive outcomes (Sodhi and Chopra,
2004; Oke and Gopalakrishnan, 2009; Tang and Nurmaya Musa, 2011; Radke and
Tseng, 2012; Arcelus et al., 2012; Chen et al., 2013; Sawik, 2013; Kırılmaz and
Erol, 2017; Tarei et al., 2020). In this study, the following sequence is followed for
alleviating risk:
• Risk Planning (RP) – establishment of plan of actions such as a business
continuity plan.
• Risk Monitoring (RM) – continuous assessment of control parameters, active
involvement of risk managers.
• Risk Avoidance (RA) – collaboration among trading partners, implementation of
forecasting and advance planning systems.
• Risk Sharing (RS) – equitable sharing of the impact of risk among the trading
partners as per agreeable established policies, insurance, and fund provisioning
to meet exigencies.
Performance measures2 (PERF) The effectiveness and efficiency of any system
can be estimated only when they are measured. It helps in benchmarking the
firms’ capabilities with the competing firms. The SCRM implementation warrants
inclusion of measures of performance from an internal perspective include the
inter-functional, customer and partner perspectives. From the perspective of the
manufacturer, the measures of performance are finance, customer perspective,
2 Indicates the factors adapted appropriately and suitably from the work of Dath et al. (2009). The
considered items are suitably modified and additional items added to every factor.
SCRM in Indian Manufacturing Industries
111
trading partner perspective, internal business perspective and innovation, and
learning perspective (Dath et al., 2009).
Conceptual Framework
The factors identified in Sect. 2.1 are portrayed through the conceptual model, as
shown in Fig. 1, from the perspectives of the manufacturers (including OEMs and
Suppliers) (Shenoi et al., 2016). However, they do not provide a comprehensive
questionnaire based on the conceptual framework. They hypothesized the framework. In the model depicted below is a mapping from the SCRM constructs (the
Independent Variables (IVs)) and the performance measures, i.e., the Dependent
Variable(DV). The items in the middle are the mediator(s). The mediators cause
the relationship between the IV and DV to change the pathway depending on the
mediating variable’s participation in the relationship. In this study, it is evident
that the presence of the mediators (namely, risk planning, risk monitoring, risk
avoidance, and risk sharing described in Sect. 2.1) has a mediating effect on the
Fig. 1 The framework of SCRM with respect to Manufacturers (Shenoi et al., 2016)
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V. Viswanath Shenoi et al.
association between the IVs and the DV taken one at a time (refer Fig. 1). Similar
to SCM, the SCRM also involves managing complex flow of information and
understanding the irrelevance. The SCRM gained importance due to the following
(Faisal et al., 2007):
• Focus on core-competencies.
• Elimination of geographical boundaries for establishing partnerships.
• Escalating disruptions in the supply chain due to man-made attacks and due to
natural disasters.
• Reduction in the supplier base attributed to JIT production.
• Turbulent nature of the economy due to strong linkage to commodities.
Lavastre et al. (2012) identified techniques for minimizing supply chain risks
by considering the attitudes of the managers towards risk and the tools used to
understand the risk.
2.2 Fuzzy Cognitive Map (FCM) and Its Applications in the
Present Study: An Overview
The findings from the empirical study help us to develop the Fuzzy Cognitive Map
(FCM) needed to predict risks and suggest mitigation strategies. In the Sect. 2.1 and
section “Description of the Construct of SCRM: Manufacturer’s Perspective”, we
have identified the factors that affect the Supply chain. In the globalized environment, it is necessary to identify early warning signals and implement appropriate
strategic decisions to meet the exigencies. The objective is to develop a fuzzy model
to identify and predict all the plausible risks based on the instantaneous risk vector.
We utilize the responses of the empirical study for the construction of FCM. The
factors of SCRM represent the states of the FCM. FCM is used to represent the
overall behavior of the dynamic supply chain system. The instantaneous risk vector
is passed on to the dynamic system to identify all plausible risks until saturation.
Risk Modeling: Graph Theory Approach
The research contributions to managing supply chain risks, to date, are based on the
identification of sources of risk, and recommendation of corresponding mitigation
strategies. However, Sodhi and Chopra (2004) proposed that risks are dependent
on each other, and therefore, it is necessary to understand the interdependencies
between the risk constructs. Graph modeling seems to be an appropriate method
for understanding the interdependencies. Graph modeling approaches have been
applied to coordination, customer sensitivity, risk alleviation, and risk mitigation
(Kaur et al., 2006; Faisal et al., 2007). The other cognitive approach widely used is
neural regression.
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Using fuzzy logic and Analytic Hierarchical Process (AHP), Kaur et al. (2007)
evaluated the extent of coordination among the trading partners in the supply chain.
An integrated approach using FCM and the fuzzy soft set model is proposed to
solve the supplier selection problem. The approach utilizes the feedback effect
among the criteria and considers the uncertainties in the decision-making process.
Micheli et al. (2014) developed an analytical model using stochastic integer linear
programming approach that incorporated supply chain managers, judgments using
utility functions, and the model employed fuzzy-extended pairwise comparisons for
choosing the mitigation strategy. However, the application of FCM in SCRM context
is sparsely available.
In our study, FCM is utilized to construct the dynamical system model of
risk present in the supply chain. Recurrent structures such as FCM help us to
establish cause-effect relationships, as the system variables are identified to the
graph nodes. The proposed model helps to determine all the plausible risks which
may appear in a supply chain and suggests strategies to mitigate risk through a fuzzy
approach.
Basics of Fuzzy Cognitive Maps
The Fuzzy Cognitive Map concept, extended by Kosko (1986), used fuzzy causal
functions considering numbers in the interval [−1, 1]. Its application is extended to
decision making (Stylios et al., 2008), performance measurement (Glykas, 2013),
supplier selection (Shaw et al., 2012), and modeling of resilient supply chain
network design (Kristianto et al., 2014).
FCM provides a quick solution while modeling complex systems with lesser
computational effort. The relationships between variables obtained constitutes the
matrix of edges known as an adjacency model, representing the overall behavior of
the system. A non-linear activation function is used for transformation of the path
and is directed towards them into a value [0, 1] or [−1, 0, 1].
Cognitive mapping of FCMs helps the respondent to have an awareness of
their prediction model by drawing, capturing and transferring causal knowledge. It
enables a way to elicit, capture and transfer causal knowledge. Maps are constructed
in various ways, such as conducting interviews, analyzing texts, capturing participant perceptions through a questionnaire, and discussing in groups, to name a few.
Perceptions from varied and diverse sources are integrated to deal with limitations of
expert opinions. FCMs also allow quantitative analysis and interpretation of quasidynamic behavior and support in making timely decisions. While adopting FCM,
modelers can use all combinations of input variables or suggest alternative system
descriptions while addressing complex problems or linking internally consistent
future state.
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A1
e14
A4
e31
R1
e43
e12
A2
e23
R2
A3
Fig. 2 Risk interdependency graph
The Proposed Model: Mapping Supply Chain Risks and Mitigation
Strategies
In the present study, a holistic approach is adapted to understand the interdependency of risks and associate them with a mitigation strategy via the concept of
Fuzzy Cognitive Maps. Bivariate correlation on the responses helps us to understand
the interdependency of the risk constructs on performance. The relationship is
established for those correlations of the constructs which are significant. The
impact of risk on the supply chain is categorized into two, namely, disruption
and lower performance. For example, Ai indicates a risk construct in the supply
chain, R1 indicates supply chain disruption and R2 indicates lower supply chain
performance. The graph formulation is shown in Fig. 2. The Fuzzy construction of
risk interdependency graphs is dealt in Sect. 2.2.
In Fig. 2, Ai → Aj denotes that the risk construct Ai influences the risk
construct Aj . Ai → R1 indicates that the risk construct Ai leads to supply
chain disruption. Similarly, Ai → R2 indicates that the risk Ai leads to lower
supply chain performance. Similarly, the association between the risks, mitigation
strategies, supply chain disruption, and supply chain performance can be obtained.
The graph formulation is shown in Fig. 3. In Fig. 3, Mi → Aj denotes the mitigation
strategy, Mi mitigates the risk construct Aj , abates supply chain disruption (R1 ) and
enhances supply chain performance (R2 ).
Construction of FCM
“FCM is a directed graph with nodes referred to as policies, events, etc., and causalities as edges. It represents the causal relationship between concepts” (Kandasamy
et al., 2007). From the Bivariate Correlation established in Tables 15 and 16, and the
rules defined for establishing the relationships, the FCM is developed. The directed
edge eij from causal concept Ai to concept Aj measures the extent of influence
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115
R2
M1
R1
A1
A3
M2
A2
A4
Fig. 3 Risk-mitigation interdependency graph
of Ai on Aj . The edge eij takes values in the fuzzy interval [0,1]. If eij = 0
indicates no causality, whereas eij > 0 indicates causal influence. The influence
of concept Aj increases as Ai increases. if eij < 0, indicates causal decrease
or negative causality i.e., the influence of concept Aj decreases as Ai increases.
Consider the nodes A1 , A2 , . . . , An of the FCM. The edge eij represents the causal
relationship between causal concept Ai to concept Aj , its value is one when there is
significant correlation, otherwise it is zero. The adjacency matrix of FCM has been
constructed by observing Table 16 in respect of the presence/absence of significant
correlation;
eij =
⎧
⎨0
⎩
1
no significant correlation exists between causal concept Ai and concept Aj ;
significant correlation exists between causal concept Ai and concept Aj .
(1)
A symmetric Matrix E is obtained (see Table 3) and the entries in the main diagonal
are equal to 1. The relation represented is reflexive and symmetric. Hence, it is a
compatibility relation (Kandasamy et al., 2007). The diagonal entries are zero, as
they are self-contained.
eij = 0
if i = j
To identify and understand the causal pathways and to initiate an appropriate
measure to reduce the impact of disruption, early detection of the risk is essential.
Appropriate mitigation strategies need to be suggested and adopted to reduce
the impact of risk on the supply chain. The risk factors, namely supply chain
disruption and supply chain performance, augmented with the concept of risk factors
in the adjacency matrix. The dimension of adjacency matrix E is n × n. The
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relationship between the concept Ai and the risk factor-disruption is captured in
r1 :
(Ai , r1 ) =
1
0
ifAi disrupts the supply chain;
ifAi does not disrupt the supply chain.
(2)
The relationship between the concept Ai and its risk factor-performance is captured
in r2 :
(Ai , r2 ) =
1
0
if Ai impacts the performance of the supply chain;
if Ai does not impact the performance of the supply chain.
(3)
Thus, the dimension of adjacency matrix changes from n × n to (n + 2) × (n + 2).
The new augmented matrix is denoted by E . The mapping between risk items and
mitigation strategies needs to be established for studying the mitigating effects. Let
B = (b1 , b2 , . . . , bm ) be the set of mitigation strategies. The relationship matrix M
is constructed as follows:
⎧
⎨1 if bi reduces the impact of risk due to Ai and;
enhances performance
(Ai , bi ) =
(4)
⎩ if b does not reduce the impact of risk due to A
0
i
i .
Thus the dimension of the matrix is n × m. To identify the appropriate mitigation
strategy, it is necessary to establish the relationship between the impact factor and
mitigation strategy:
(r1 , bi ) =
n
−1
if bi reduces disruption due to Ai and enhances
;
performance
(5)
−1
if bi reduces risk due to Ai and enhances perfor;
mance
(6)
i=1
(r2 , bi ) =
n
i=1
After augmenting the impact factors with the mitigation strategies, the dimension
of new relationship matrix M is (n + 2) × m.
Prediction of Future Risks Based on the Current State of Risk Observance
Let A1 , A2 , . . . , An be the nodes of an FCM. A = (a1 , a2 , . . . , an ), where ai ∈
{0,1}. A is called an instantaneous state vector and it denotes the on-off position of
SCRM in Indian Manufacturing Industries
the node in an instant of time:
0 if ai off, no risk is perceived for the concept Ai ;
ai =
1 if ai on, risk is perceived for the concept Ai .
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(7)
When A = (a1 , a2 , . . . , an+2 ) vector is passed into a dynamical system of matrix
E . The resultant vector A ∗ E = (a1 , a2 , . . . , an+2
) is subject to threshold of
1. The vectors are updated and renamed as D = (d1 , d2 , . . . , dn+2 ). We denote
the above operation by (a1 , a2 , . . . , an+2
) (b1 , b2 , . . . , bn+2 ). The symbol represents the resultant vector has been subject to a threshold of 1 and updated. The
above process is repeated till the FCM state vector reaches the equilibrium state
of a dynamical system, i.e., when there is no change in the resultant state vector’s
component-wise from that of the immediate preceding resultant vector. Then the
resultant vector obtained is called the fixed point. The fixed point is the saturation
point of risk that may lead to a total breakdown of the supply chain.
Identification and Effectiveness of Mitigation Strategy for Risk During the
Run
To identify the appropriate mitigation strategy, the vector D = (d1 , d2 , . . . , dn )
is multiplied with matrix M. Thus the resultant vector, B = (b1 , b2 , . . . , bn ), is
obtained. The set of bi ’s, which are equal to 1 or non-zero values, are the mitigation
strategies to be implemented at that level to alleviate the impact of risk and enhance
the supply chain performance.
To study the effectiveness of the mitigation strategy B , we need to find the
product of the vector, D = (d1 , d2 , . . . , dn+2 ), and the matrix M. If the resultant
vector is zero or a negative value, then it signifies that all the risks are alleviated.
2.3 Fuzzy TOPSIS Approach
In order to compare the results of the FCM approach, we consider the Fuzzy
TOPSIS approach. Identification of risks present in the supply chain and appropriate
mitigation strategies to curtail them is a Multi-Criteria Decision Making (MCDM)
problem. A solution to the MCDM problem can be achieved through TOPSIS
(Technique for Order Performance by Similarity to Ideal Solution) approach
(Rostamzadeh et al., 2018; Al Zubayer et al., 2019). This technique suggests the
best solution alternatives closest to the Positive Ideal Solution (PIS) and distant
from the Negative Ideal Solution (NIS). The benefit criteria is maximized and cost
criteria is minimized by PIS and NIS. This section deals with TOPSIS applied to
the fuzzy environment as proposed by Shemshadi et al. (2011) and Zadeh (1965) to
map the linguistic variables to the numerical variables.
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Definition 1 Let a = (a1 , a2 , a3 ) and b = (b1 , b2 , b3 ) be two fuzzy triangular
numbers, then the distance between them is determined by the vertex method given
in equation 8 (Dağdeviren et al., 2009).
d(a, b) =
1
[(b1 − a1 )2 + (b2 − a2 )2 + (b3 − a3 )2 ]
3
(8)
Definition 2 The importance values for each criterion, the weighted normalised
fuzzy decision matrix is constructed as:
V = [vij ]m×n
i = 1, 2, . . . , n j = 1, 2, . . . , m
vij = rij ⊗ wi
(9)
The steps of Fuzzy TOPSIS is as follows:
Step 1:
The linguistic ratings (xij i = 1, 2, . . . , n j = 1, 2, . . . , m) for
the alternatives with respect to criteria R is obtained. To normalise the
decision matrix R, let: xij = (aij , bij , cij ) xj = (aj , bj , cj ) xj∗ =
(aj∗ , bj∗ , cj∗ ) then
rij =
Step 2:
Step 3:
Step 4:
⎧
⎪
xij
⎪
⎪
⎪
⎪
⎨ x∗
j
⎪
⎪
xj
⎪
⎪
⎪
⎩ xij
=
aij bij cij
,
,
aj∗ bj∗ cj∗
=
a j b j cj
,
,
aij bij cij
;
(10)
The weighted normalized fuzzy decision matrix is calculated. The
weighted normalized value vij is calculated by using the equation 9.
The Fuzzy Positive Ideal Solution (FPIS) and the Fuzzy Negative Ideal
Solution (FNIS) is identified. As this work is based on the risk criteria,
FPIS will have maximum value and FNIS will have minimum value.
The distance is calculated for each of the alternative from FPIS and FNIS
as mentioned in equations 11 and 12.
Dj∗ =
m
(vij , vj∗ )
j = 1, 2, . . . , m
(11)
j = 1, 2, . . . , m
(12)
j =1
Dj− =
m
(vij , vj− )
j =1
SCRM in Indian Manufacturing Industries
Step 5:
119
The similarity to ideal solution is calculated.
CCj =
Step 6:
Dj−
Dj− + Dj∗
(13)
The alternatives are ranked according to CCj in decreasing order.
2.4 Research Gaps, Hypotheses and Contributions
Literature to date concerning India is majorly on supply chain flexibility (Khan and
Pillania, 2008), and alignment of supply chain strategy with the business strategy
(Dath et al., 2010). However, articles based on SCRM practices in Indian scenario
are rare. Hence, the present study provides insight into SCRM research in the Indian
context, considering the vibrant expansion of industries in India.
Researchers observed that SCRM is multifarious, involves numerous interrelated risk factors, and needs a rigorous study to address it. Risk mitigation
strategies differ based on the perception of risk and the implementation strategy
that could extend for a short, medium or long term. Based on the literature review,
12 effective techniques widely employed by mangers for minimizing supply chain
risks have been identified. The techniques have been grouped into four categories,
namely risk planning, risk monitoring, risk avoidance, and risk sharing. The above
discussion strengthens the argument that the four mitigation strategy constructs have
a substantial effect on the relationship between the eight SCRM constructs and
the performance measures. The above observations lead us to 32 hypotheses from
the perspective of manufacturers. For example, H1a – corresponds to the effect of
the mitigation strategy, namely risk planning on the risk construct ‘Demand side
risk’. Similarly, other hypotheses can be developed based on mitigation strategies
with the risk constructs taken one at a time. Table 1 summarizes the hypotheses
conceived in this work. The hypotheses conceived shows the effect of each of the
individual mediators (namely, risk planning, risk monitoring, risk avoidance, and
risk sharing) on the relationship between Independent variables (IVs), taken one
at a time, and Dependent Variable (DV), namely, performance measures. Thus the
hypotheses H1a, H1b, H1c, H1d, H1e, H1f, H1g, and H1h correspond to the effect
of mediator variable, Risk avoidance (RA), on Demand Side Risk, Supply Side
Risk, Logistic Risk, Regulatory, Legal and Bureaucratic Risk, Infrastructure Risk,
Stock/Data Management risk, Environmental Risk, and Financial Risk respectively.
The hypotheses H2, H3, and H4 correspond to the effect of mediating variables, Risk
Planning, Risk Monitoring, Risk Avoidance and Risk Sharing, on the individual risk
constructs.
Further, the FCM is developed by using the bivariate correlation among the risk
variables. The study also illustrates practitioners and researchers applying concepts
such as FCM and Fuzzy TOPSIS for the identification and ranking of strategies.
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V. Viswanath Shenoi et al.
Table 1 The list of formulated hypotheses: the perspective of manufacturers
Mediator
Risk Planning (RP):
(corresponding hypothesis are
H1a, H1b, H1c, H1d, H1e, H1f,
H1g, H1h)
Hypothesis
RP has a significant and mediating effect on the relationship between the individual independent variables taken
one at a time and the measures of performance. Refer to
Fig. 1.
Risk Monitoring (RM):
(corresponding hypothesis are
H2a, H2b, H2c, H2d, H2e, H2f,
H2g, H2h)
RM has a significant and mediating effect on the relationship between the individual independent variables taken
one at a time and the measures of performance. Refer to
Fig. 1.
Risk Avoidance (RA):
(corresponding hypothesis are
H3a, H3b, H3c, H3d, H3e, H3f,
H3g, H3h)
RA has a significant and mediating effect on the relationship between the individual independent variables taken
one at a time and the measures of performance. Refer to
Fig. 1.
Risk Sharing (RS):
(corresponding hypothesis are
H4a, H4b, H4c, H4d, H4e, H4f,
H4g, H4h)
RS has a significant and mediating effect on the relationship between the individual independent variables taken
one at a time and the measures of performance. Refer to
Fig. 1.
3 Methods
The framework of the study is presented in Fig. 4.
3.1 Data Description
This study helps practitioners understand the likelihood of risks and their severity in
the practitioners’ respective companies. The responses to the questionnaire3 from
the practicing managers serves as the data for our study.
3.2 Survey Description
The population considered for the study includes OEMs and suppliers who are
the manufacturers in India. The final product manufacturers are the OEMs in
various sectors, namely, the automotive (e.g., manufacturers of two and fourwheeler), the heavy engineering (e.g., manufactures of heavy machinery), home
appliances (e.g., manufacturers of washing machines), and general engineering.
The respondents considered for our study includes Vice-Presidents and Senior
personnel in the purchasing and materials management departments. Numerous
respondents were contacted, and the questionnaire was shared with around 120
shortlisted respondents. Around 40 respondents are from OEMs, and 80 respondents
3 https://tinyurl.com/scrmquestionnaire
SCRM in Indian Manufacturing Industries
121
Fig. 4 Framework of the study
are suppliers to OEMs. The contacted respondents work in various industrial sectors
mentioned in the sample profile summarized in Table 2 below.
Similarly, for the supplier perspective studies, sub-assembly suppliers to OEMs
in the above-mentioned sectors were shortlisted. The supplier respondents are
manufacturers of fasteners, brake linings, wheels, electrical components such as
coils, motors, etc. In the supplier perspective study, out of 80 suppliers contacted,
52 suppliers responded. A majority of the respondents (44) are from the organi-
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V. Viswanath Shenoi et al.
Table 2 OEM respondents’ categorization
Company categorization
Automobile (two and four-wheeler manufacturers)
Heavy vehicles (trucks and medium sized vehicles
manufacturers)
Heavy engineering (earth moving equipment and heavy
machinery manufacturers)
General engineering (automobile components and spare part
manufacturers)
Number of respondents
OEM
Supplier
7
12
6
10
2
10
18
20
zations that supply sub-assemblies and parts to OEM’s of two and four-wheeler
vehicles, large capacity vehicles and earth-moving equipment manufacturers. The
questionnaires were circulated to the respondents in three different forms, namely,
direct interview, E-mail and web forms.
3.3 Scales Used to Measure the Latent Variables
The supply chain managers underestimate risk in terms of their likelihood and
impact (Thun and Hoenig, 2011). The importance of development of instruments
is emphasized by the researchers for theory building (Dath et al., 2010). The survey
instruments help the managers to identify the aspects of SCRM and empirically
validate the SCRM constructs from the perspective of the manufacturers. The
exhaustive review of the literature resulted in the development of the instrument.
An instrument with 1 indicating ‘very low’ and 5 indicating ‘very high’ (referred
to as five point Likert scale) has been adopted to study the likelihood of the risk and
its severity in this work.
3.4 Construction of FCM for SCRM
As discussed in Sect. 2.2, the adjacency matrices are computed for the risk constructs. The correlation matrix is given in Table 16. In this study, Ai ’s are considered
as risk constructs, namely, QDSR, QSSR, QLR, QRLB, QIR, QSDM, QER, and
QFR. ‘Q’ is prefixed with the constructs label to indicate the severity of the impact
due to that risk. The entries in Table 3 corresponds to the significant correlations
between the variables as observed from Table 16 along with equation 1, 2 and 3.
‘Q’ is prefixed with the mitigation strategies to indicate the impact of implementing
the alleviation strategy. Mi ’s are considered as QRP, QRM, QRA, and QRS. The
relationship matrix presented in Table 4 is also generated from Table 16, along with
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123
Table 3 Adjacency matrix E : FCM of concepts of risk constructs
∗
QDSR
QSSR
QLR
QRLB
QIR
QSDM
QER
QFR
R1
R2
QDSR
0
1
1
1
1
1
1
0
1
1
QSSR
1
0
1
1
1
1
1
1
1
0
QLR
1
1
0
1
1
1
1
1
1
0
Table 4 Relationship matrix
M : concepts of risk vs
mitigation strategies
QRLB
1
1
1
0
1
1
1
1
0
1
QIR
1
1
1
1
0
1
1
1
0
1
QSDM
1
1
1
1
1
0
1
1
1
0
∗
QDSR
QSSR
QLR
QRLB
QIR
QSDM
QER
QFR
R1
R2
QER
1
1
1
1
1
1
0
1
1
0
QRP
1
1
0
0
1
1
1
0
−3
−2
QFR
0
1
1
1
1
1
1
0
1
1
QRM
1
1
1
0
1
1
1
1
−3
−4
R1
1
1
1
0
0
1
1
1
0
1
QRA
1
1
1
1
0
1
1
0
−3
−3
R2
1
0
0
1
1
0
0
1
1
0
QRS
1
1
1
0
0
1
1
1
−3
−3
equations 4, 5 and 6. Figure 5 represents the construction of the FCM model of risk
constructs. Figure 6 represents the relationship between risk constructs, mitigation
strategies, and the nature of the impact on the supply chain.
3.5 Statistical Analysis
Empirical Validation of the Proposed SCRM Constructs
After data collection, processes including refinement, modification, and finalization
of the measurements are undertaken. The data is subjected to factor analysis
in order to discover the latent variables with the help of item-factor loadings.
The questionnaires are subjected to reliability and validity tests, for ensuring
standardization and making them operational (Sureshchandar et al., 2001; Dath
et al., 2010). Confirmatory Factor Analysis (CFA) method is followed in the present
study. The CFA approach helps the researchers confirm the hypotheses and test the
established relationship between observed and latent variables as a specified model
a priori by the researcher. Since the researchers possesses the knowledge of the
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V. Viswanath Shenoi et al.
QDSR
QSSR
QFR
QLR
R1
QRLB
R2
QIR
QER
QSDM
Fig. 5 Risk interdependency graph
observed variables, reliable indicators for each of the constructs, the CFA technique
has been adopted.
Reliability and Validity Tests
On freezing the scale of measurement, the researchers need to ensure the construct
validity of the instrument and gain confidence that the inferences derived based
on the proposed model reflects reality. The fundamental prerequisite for construct
validity is the unidimensionality of the measure. CFA is done for all the constructs.
A value of 0.9 or higher for the Comparative Fit Index (CFI) suggests that the model
framework has an adequate fit, and unidimensionality exists (Sureshchandar et al.,
2001). The Root Mean Error of Approximation (RMSEA) is an additional measure
for model fit adequacy. The maximum value for RMSEA deemed to be acceptable
is 0.1 (Hair et al., 2006).
The consistency of the instrument is a measure of Reliability, also referred to
as internal consistency to measure the intended issue. The measure of internal
SCRM in Indian Manufacturing Industries
125
Ai
QDSR
QSSR
Mi
QRP
QLR
QRM
R1
QRLB
QRA
R2
QIR
QRS
QSDM
QER
QFR
Fig. 6 Risk-mitigation interdependency graph
consistency is Cronbach’s alpha (α) (Cronbach, 1951), and a value of 0.7 and above
is acceptable.
Face and content validity indicates the extent to which the research instrument
depicts the concepts (Sureshchandar et al., 2001) and determines the face value of
a good representation of the construct (Kaplan and Saccuzzo, 2012). Review of the
questionnaire by expert professors and industry personnel ensures face validity. The
126
V. Viswanath Shenoi et al.
extent to which the factors converge is indicated by convergent validity and a value
of 0.9 and above for Bentler Bonet Fit Index (BBI) satisfies the requirement.
In the present study, the instrument is developed for manufacturers, which
include suppliers and OEMs. The items contained in instruments are drawn from
the literature.
4 Results
4.1 Factor Loadings
Table 5 presents the Factor loadings containing the values of various indices
such as BBI, CFI, Chronbach’s alpha and RMSEA. The values are above the
limits satisfying the required validity and reliability requirements. Top Management
Commitment (TMC) and Mitigation strategies (MS) have RMSEA values above
0.1, but the values of other indices such as BBI and CFI are in range. Tables 5, 15
and 16 indicate the presence of discriminant validity (Crocker and Algina, 1986).
4.2 Sobel Test for Mediation
Sobel test is performed to test whether a mediator realizes the influence of an IV to
a DV (Preacher and Hayes, 2008). The parameters in the Sobel test are summarized
in the Table 6 below. On performing the Sobel test (Preacher, 2010), Test Statistic
(TS), and Standard Error (SE) with associated p-value is obtained. If the p-value
falls below the established alpha level of 0.05, indicating the association between
the IV and DV (in this case, risk constructs, influence on performance), the impact
of the risk construct is significantly reduced by the inclusion of the mediator (in this
case mitigation strategies). It is a evidence of mediation. In the tables below, R is
referred to risk, MV is referred to mediating variable, and P is the performance. The
stepwise regression method is implemented as follows (Tables 7, 8, 9, 10, 11, 12,
13, 14):
(a) Risk(R) is considered as an independent variable, and the mediating variable(MV) is considered as a dependent variable and is denoted by R→MV(a).
(b) Risk(R) and mediating variable(MV) are considered as independent variables,
and the performance measures(P) is considered as a dependent variable, and is
denoted by R& MV → P(b).
4.3 Bivariate Correlation Between the Constructs
The Bivariate Correlation is performed for understanding the relationship between
the likelihood of the risk and performance. Also, the Bivariate Correlation is
SCRM in Indian Manufacturing Industries
127
Table 5 The Construct of SCRM: item-factor loadings and fit indices
Item
number
Item
loadings
BBI
CFI
α
RMSEA
1
0.45
0.97
0.99
0.73
0.08
2
3
4
1
2
3
4
1
2
3
4
5
1
0.88
0.94
0.56
0.93
1.06
0.93
0.80
0.73
0.75
0.77
0.93
1.04
0.72
0.98
0.99
0.90
0.11
0.96
0.97
0.90
0.14
0.87
0.89
0.77
0.00
2
0.88
Infrastructure Risk(IR)
3
4
1
2
3
0.53
0.51
0.71
0.73
0.98
0.99
1.00
0.82
0.00
Stock and Data Management (SDM)
1
0.92
0.99
1.00
0.86
0.00
2
3
0.83
0.71
1
1.03
0.98
0.99
0.898
0.16
2
3
4
1
2
3
1
2
3
4
5
6
7
8
9
1.00
1.04
0.88
0.61
0.66
0.85
0.79
0.88
0.57
0.77
0.80
0.76
0.79
0.80
0.72
1.00
1.00
0.84
0.00
0.94
0.96
0.94
0.13
Constructs
Demand
Side
(DSR)
Risk
Supply Side Risk (SSR)
Logistic Risk (LR)
Regulatory, Legal
and Bureaucratic Risk
(RLB)
Environmental
(ER)
Risk
Financial Risk (FR)
Top Management
Commitment (TMC)
(continued)
128
V. Viswanath Shenoi et al.
Table 5 continued.
Item
Item
Constructs
number
loadings
BBI
Mitigation Strategies and Risk Management Process (MS-RMP)
Risk Planning (RP)
1
0.92
1.00
2
0.61
3
0.69
4
0.64
Risk Monitoring (RM)
1
0.88
1.00
2
0.64
3
0.63
Risk Avoidance (RA)
1
0.81
1.00
2
0.73
3
1.00
Risk Sharing (RS)
1
0.84
1.00
2
0.86
3
0.80
1
0.61
0.90
Financial Performance
2
0.67
3
0.89
4
0.56
5
0.54
6
0.44
Customer
perspective
1
0.70
0.97
performance
2
0.73
3
0.79
4
0.79
5
0.86
6
0.54
Trading partner perfor1
0.79
0.95
mance
2
0.89
3
1.00
4
0.83
5
0.87
Internal
business
1
0.68
0.98
perspective performance
2
0.85
3
0.75
4
0.77
5
0.76
6
0.79
Innovation and learning
1
0.77
1.00
perspective performance
2
0.69
3
0.86
CFI
α
RMSEA
1.00
0.783
0.00
1.00
0.74
0.00
1.00
0.86
0.00
1.00
0.87
0.00
0.91
0.83
0.12
0.98
0.93
0.11
0.96
0.95
0.09
1.00
0.92
0.07
1.00
0.93
0.00
Notes: Acceptable limits: CFA Index: BBI > 0.9; CFI > 0.9; α > 0.6; RMSEA< 0.1
SCRM in Indian Manufacturing Industries
129
Table 6 Parameters of Sobel Test
Parameters
R
MV
R → MV (a)
Sa
R& MV → P(b)
Sb
TS
SE
Details
Risk construct
Mediating Variable
Raw (unstandardized) regression coefficient for the association
between Risk construct-R and Mediating Variable (MV)
Standard error of a
Raw (unstandardized) regression coefficient for the association
between the Risk-R and Mediating Variable (MV) and the Dependent Variable Performance (P)
Standard error of b
Test statistic
Standard error of Sobel test
Table 7 The effect of mediating variables on Demand side risk (DSR)
Risk-R
DSR
DSR
DSR
DSR
MV
RP
RM
RA
RS
R → MV(a)
3.87
4.09
4.21
4.50
Sa
0.23
0.27
0.30
0.29
R&MV → P(b)
0.44
0.49
0.36
0.40
SOBEL TEST
TS
SE
4.69 0.36
6.35 0.31
4.82 0.31
5.36 0.33
p-value
0.00
0.00
0.00
0.00
Sb
0.09
0.07
0.07
0.07
SOBEL TEST
TS
SE
4.74 0.34
6.40 0.29
4.23 0.32
5.32 0.31
p-value
0.00
0.00
0.00
0.00
Sb
0.10
0.07
0.08
0.07
SOBEL TEST
TS
SE
4.31 0.41
6.46 0.29
4.35 0.34
5.60 0.32
p-value
0.00
0.00
0.00
0.00
Sb
0.09
0.07
0.07
0.07
Table 8 The effect of mediating variables on Supply Side Risk(SSR)
Risk-R
SSR
SSR
SSR
SSR
MV
RP
RM
RA
RS
R → MV(a)
3.74
3.90
3.90
4.35
Sa
0.19
0.23
0.25
0.24
R&MV → P(b)
0.44
0.49
0.36
0.39
Table 9 The effect of mediating variables on Logistic Risk
Risk-R
LR
LR
LR
LR
MV
RP
RM
RA
RS
R → MV(a)
4.08
3.89
4.14
4.47
Sa
0.18
0.23
0.24
0.23
R&MV → P(b)
0.44
0.49
0.36
0.41
130
V. Viswanath Shenoi et al.
Table 10 The effect of mediating variables on Regulatory, Legal and Bureaucratic risk(RLB)
Risk-R
RLB
RLB
RLB
RLB
MV
RP
RM
RA
RS
R → MV(a)
4.08
3.89
4.14
4.47
Sa
0.18
0.23
0.24
0.23
R&MV → P(b)
0.44
0.49
0.36
0.41
Sb
0.11
0.07
0.08
0.08
SOBEL TEST
TS
SE
3.93 0.45
6.46 0.29
4.35 0.34
4.95 0.36
p-value
0.00
0.00
0.00
0.00
SOBEL TEST
TS
SE
4.94 0.34
6.66 0.28
5.11 0.29
5.64 0.31
p-value
0.00
0.00
0.00
0.00
Table 11 The effect of mediating variables on Infrastructure Risk(IR)
Risk-R
IR
IR
IR
IR
MV
RP
RM
RA
RS
R → MV(a)
3.76
3.78
3.99
4.19
Sa
0.19
0.23
0.26
0.25
R&MV → P(b)
0.46
0.51
0.38
0.42
Sb
0.09
0.07
0.07
0.07
Table 12 The effect of mediating variables on Stock/Data Management Risk (SDM)
Risk-R
SDM
SDM
SDM
SDM
MV
RP
RM
RA
RS
R → MV(a)
3.94
3.84
4.35
4.37
Sa
0.20
0.25
0.26
0.26
R&MV → P(b)
0.44
0.49
0.36
0.41
Sb
0.10
0.07
0.08
0.07
SOBEL TEST
TS
SE
4.29 0.40
6.36 0.29
4.34 0.36
5.53 0.32
p-value
0.00
0.00
0.00
0.00
Table 13 The effect of mediating variables on Environmental Risk (ER)
Risk-R
ER
ER
ER
ER
MV
RP
RM
RA
RS
R → MV(a)
3.77
3.72
3.85
4.14
Sa
0.14
0.18
0.19
0.19
R&MV → P(b)
0.43
0.49
0.35
0.40
Sb
0.10
0.07
0.07
0.07
SOBEL TEST
TS
SE
4.24 0.38
6.63 0.27
4.85 0.27
5.52 0.29
p-value
0.00
0.00
0.00
0.00
SOBEL TEST
TS
SE
4.93 0.33
6.35 0.29
4.96 0.27
5.39 0.30
p-value
0.00
0.00
0.00
0.00
Table 14 The effect of mediating variables on Financial Risk(FR)
Risk-R
FR
FR
FR
FR
MV
RP
RM
RA
RS
R → MV(a)
3.60
3.90
3.75
4.08
Sa
0.19
0.23
0.26
0.25
R&MV → P(b)
0.46
0.48
0.37
0.40
Sb
0.09
0.07
0.07
0.07
performed for risk construct and performance. The Bivariate correlation helps the
researchers to understand the inter-dependency between the variables (Tables 15
and 16).
IDSR
1
0.61a
0.68a
0.31a
0.47a
0.29a
0.49a
0.11
−0.31a
−0.15
−0.29a
−0.29a
−0.42a
ILR
1
0.35a
0.65a
0.15
0.69a
0.29a
−0.36a
−0.21
−0.27b
−0.44a
−0.38a
ISSR
1
0.60a
0.47a
0.56a
0.09
0.35a
0.26b
−0.12
−0.16
−0.09
−0.27b
−0.30a
1
0.71a
−0.04
0.33a
0.15
−0.15
−0.21
−0.16
−0.16
−0.33a
IRLB
1
0.09
0.56a
0.06
−0.07
−0.07
−0.02
−0.11
−0.27b
IIR
1
0.16
−0.31a
0.04
0.25b
0.09
0.12
0.21
ISDM
IER
1
0.09
−0.33a
−0.20
−0.19
−0.39a
−0.46a
‘I’ is prefixed with the constructs label to indicate the measure of likelihood
a Correlation is significant at 0.01 level (2-tailed)
b Correlation is significant at 0.05 level (2-tailed)
Note:
*
IDSR
ISSR
ILR
IRLB
IIR
ISDM
IER
IFR
IRP
IRM
IRA
IRS
PERF
1
−0.14
−0.21
−0.13
−0.32a
−0.46a
IFR
Table 15 Bi-Variate Correlation between the likelihood occurrence of risk constructs and performance
1
0.71a
0.78a
0.79a
0.53a
IRP
1
0.78a
0.76a
0.59a
IRM
1
0.82a
0.64a
IRA
1
0.68a
IRS
1
PERF
SCRM in Indian Manufacturing Industries
131
QDSR
1
0.62a
0.63a
0.30a
0.38a
0.42a
0.44a
0.27b
−0.20
−0.22b
−0.22b
−0.28a
−0.28a
−0.31a
QLR
1
0.62a
0.77a
0.71a
0.81a
0.42a
−0.22b
−0.41a
−0.17
−0.31a
−0.34a
−0.29a
QSSR
1
0.71a
0.65a
0.63a
0.61a
0.55a
0.34a
−0.13
−0.20
−0.18
−0.22b
−0.28a
−0.34a
1
0.78a
0.58a
0.56a
0.39a
−0.17
−0.23b
−0.16
−0.28a
−0.22b
−0.35a
QRLB
1
0.69a
0.72a
0.44a
−0.09
−0.20
−0.12
−0.23b
−0.20
−0.24b
QIR
1
0.55a
0.35a
−0.23b
−0.29a
−0.14
−0.36a
−0.26b
−0.30a
QSDM
a Correlation
‘Q’ is prefixed with the constructs label to indicate the severity of impact
is significant at 0.01 level (2-tailed)
b Correlation is significant at 0.05 level (2-tailed)
Note:
*
QDSR
QSSR
QLR
QRLB
QIR
QSDM
QER
QFR
QTMC
QRP
QRM
QRA
QRS
PERF
1
0.50a
−0.25b
−0.29a
−0.12
−0.23b
−0.25b
−0.32a
QER
Table 16 Bi-variate Correlation with respect to risk constructs and performance
1
−0.38a
−0.11
−0.19
−0.12
−0.15
−0.36a
QFR
1
0.55a
0.67a
0.60a
0.65a
0.79a
1
0.76a
0.80a
0.77a
0.48a
QTMC QRP
1
0.81a
0.77a
0.61a
QRM
1
0.85a
0.51a
QRA
1
0.56a
QRS
1
PERF
132
V. Viswanath Shenoi et al.
SCRM in Indian Manufacturing Industries
133
4.4 FCM Results
Instantaneous vector Q1 represents the risk perceived during an instant of time.
The instantaneous vector Q1 is passed on to the adjacency represented in Table 3.
The resultant vector is threshold to one and is passed on to the adjacency
matrix until equilibrium. Equilibrium is attained in the third iteration (refer to
Table 17).
The equilibrium vector obtained is passed on to the relationship matrix M . The
results in Table 18 suggest that the risks are mitigated, and the impact of risks are
alleviated to a greater extent, as the results show negative values or zeroes. The
equilibrium vector obtained is passed on to the relationship matrix M. The results
in Table 19 suggest the levels at which the individual mitigation strategy needs to be
implemented to mitigate the risk and enhance the performance. The highest score of
7 is obtained for risk monitoring, suggesting that regular monitoring of the supply
chain is essential to make proactive decisions. The next highest score of 6 is realized
for risk avoidance and risk sharing, and a score of 5 for risk planning (Table 19).
Table 17 Results of product of instantaneous vector Q1 and dynamical system matrix E Q1
RS1
Q2 =˜ RS1
RS2
Q3 =˜ RS2
RS3
QDSR
1
0
1
8
1
8
QSSR
0
1
1
7
1
8
QLR
0
1
1
7
1
8
QRLB
0
1
1
7
1
8
QIR
0
1
1
7
1
8
QSDM
0
1
1
7
1
8
QER
0
1
1
7
1
8
QFR
0
0
0
8
1
8
R1
0
1
1
6
1
7
R2
0
1
1
4
1
5
R1
1
R2
1
Table 18 Results of product of equilibrium vector Q3 and relationship matrix M RS3
QDSR
1
QRP
0
QSSR
1
QRM
0
QLR
1
QRA
0
QRLB
1
QRS
0
QIR
1
QSDM
1
QER
1
QFR
1
Table 19 Results of product of instantaneous vector Q1 and relationship matrix M
RS3
QDSR
1
QRP
5
QSSR
1
QRM
7
QLR
1
QRA
6
QRLB
1
QRS
6
QIR
1
QSDM
1
QER
1
QFR
1
134
V. Viswanath Shenoi et al.
4.5 Fuzzy TOPSIS Approach
The risk is categorized into four types the low likelihood and low impact, the low
likelihood and high impact, the high likelihood and low impact, and high likelihood
and high impact. In the proposed model, the normalized weights for each criterion
is obtained from the likelihood of a risk. The ranking of the alternatives and the
weights of the criteria are calculated based on Fuzzy TOPSIS approach. The theory
of fuzzy sets reconciles the alternatives evaluation to deal with the uncertainty. The
triangular fuzzy numbers used for the evaluation are represented in Fig. 7.
In this problem, there are eight risk factors and four mitigation strategies as
alternatives. The desire is to rank the alternatives using risk and to reduce the risk
present in the supply chain. The steps involved in the ranking of the alternatives as
follows:
1. There are four alternatives, namely risk planning, risk monitoring, risk avoidance
and risk sharing.
2. Risk criteria considered are:
•
•
•
•
•
•
•
•
Demand Side Risk (DSR)
Supply Side Risk (SSR)
Logistic Risk (LR)
Regulatory, Legal and Bureaucratic risk (RLB)
Infrastructure Risk (IR)
Stock/Data Management risk (SDM)
Environmental Risk (ER)
Financial Risk (FR)
3. The weights of the criteria are calculated (see Table 20)
4. The alternatives are evaluated using the linguistic variables. The linguistic values
are converted to appropriate equivalent fuzzy numbers (see Tables 21 and 22).
1.0
0.1
Very low
Low
0.2
0.4
0.3
High
0.5
Fig. 7 Triangular fuzzy numbers (Liu et al., 2005)
0.6
Very high
0.7
0.8
0.9
SCRM in Indian Manufacturing Industries
135
Table 20 Total weights of
the criteria (normalised)
Criteria
DSR
SSR
LR
RLB
IR
SDM
ER
FR
Weights
0.13
0.14
0.13
0.13
0.12
0.12
0.11
0.12
ER
4.24
6.63
4.85
5.52
FR
4.93
6.35
4.96
5.39
Table 21 Decision matrix for the alternatives
RP
RM
RA
RS
DSR
4.69
6.35
4.82
5.36
SSR
4.74
6.4
4.23
5.32
Table 22 Relationship
between linguistic parameters
and fuzzy numbers
LR
4.31
6.46
4.35
5.6
RLB
3.93
6.46
4.35
4.95
IR
4.94
6.66
5.11
5.64
Linguistic parameter
Very Low (VL)
Low (L)
High (H)
Very High (VH)
SDM
4.29
6.36
4.34
5.53
Numerical value
<4.69
<5.11
<6.35
>=6.35
Fuzzy number
(0.1,0.2,0.3)
(0.3,0.4,0.5)
(0.5,0.6,0.7)
(0.7,0.8,0.9)
5. The weighted decision matrix for alternatives are calculated (see Tables 23, 24,
and 25).
6. FPIS and FNIS are calculated (see Table 26 for results)
7. The distance of each alternative is calculated from FPIS and FNIS to determine
the closeness coefficient for each of the alternatives (see Table 27).
8. Rank of the alternatives with respect to their closeness coefficient is thus obtained
and is shown in Table 28.
5 Discussion (Managerial and Theoretical Implications)
5.1 Validation of the Proposed Conceptual Framework
From the manufacturer’s perspective, the effect of each mediator in the relationship
between IVs is taken one at a time, and DV is individually studied through
regression analysis using SPSS. Some of the items are eliminated during the
validation process. The complete final instrument can be had from link.3
Tables 7, 8, 9, 10, 11, 12, 13, and 14 give the results of the effect of individual
mediators on individual IVs taken one at a time. The Sobel test is done to analyze the
RP
RM
RA
RS
0.3
0.7
0.3
0.5
DSR
0.4
0.8
0.4
0.6
0.5
0.9
0.5
0.7
0.3
0.7
0.1
0.5
SSR
0.4
0.8
0.2
0.6
0.5
0.9
0.3
0.7
Table 23 Decision matrix for alternatives
LR
0.1
0.7
0.1
0.5
0.2
0.8
0.2
0.6
0.3
0.9
0.3
0.7
RLB
0.1
0.7
0.1
0.3
0.2
0.8
0.2
0.4
0.3
0.9
0.3
0.5
IR
0.3
0.7
0.5
0.5
0.4
0.8
0.6
0.6
0.5
0.9
0.7
0.7
SDM
0.1
0.7
0.1
0.5
0.2
0.8
0.2
0.6
0.3
0.9
0.3
0.7
ER
0.1
0.7
0.3
0.5
0.2
0.8
0.4
0.6
0.3
0.9
0.5
0.7
FR
0.3
0.7
0.3
0.5
0.4
0.8
0.4
0.6
0.5
0.9
0.5
0.7
136
V. Viswanath Shenoi et al.
RP
RM
RA
RS
DSR (0.13)
0.33 0.44
0.78 0.89
0.33 0.44
0.56 0.67
0.56
1
0.56
0.78
SSR (0.14)
0.33 0.44
0.78 0.89
0.11 0.22
0.56 0.67
0.56
1
0.33
0.78
LR (0.13)
0.11 0.22
0.78 0.89
0.11 0.22
0.56 0.67
0.33
1
0.33
0.78
Table 24 Normalised Fuzzy Decision matrix for alternatives
RLB (0.13)
0.11 0.22
0.78 0.89
0.11 0.22
0.33 0.44
0.33
1
0.33
0.56
IR (0.12)
0.33 0.44
0.78 0.89
0.56 0.67
0.56 0.67
0.56
1
0.78
0.78
SDM (0.12)
0.11 0.22 0.33
0.78 0.89 1
0.11 0.22 0.33
0.56 0.67 0.78
ER (0.11)
0.11 0.22
0.78 0.89
0.33 0.44
0.56 0.67
0.33
1
0.56
0.78
FR (0.12)
0.33 0.44
0.78 0.89
0.33 0.44
0.56 0.67
0.56
1
0.56
0.78
SCRM in Indian Manufacturing Industries
137
RP
RM
RA
RS
DSR
0.04
0.1
0.04
0.07
0.06
0.12
0.06
0.09
0.07
0.13
0.07
0.1
SSR
0.05
0.11
0.02
0.08
0.06
0.12
0.03
0.09
0.08
0.14
0.05
0.11
LR
0.01
0.1
0.01
0.07
0.03
0.12
0.03
0.09
0.04
0.13
0.04
0.1
Table 25 Weighted Fuzzy Decision matrix for alternatives
RLB
0.01
0.1
0.01
0.04
0.03
0.12
0.03
0.06
0.04
0.13
0.04
0.07
IR
0.04
0.09
0.07
0.07
0.05
0.11
0.08
0.08
0.07
0.12
0.09
0.09
SDM
0.01
0.09
0.01
0.07
0.03
0.11
0.03
0.08
0.04
0.12
0.04
0.09
ER
0.01
0.09
0.04
0.06
0.02
0.1
0.05
0.07
0.04
0.11
0.06
0.09
FR
0.04
0.09
0.04
0.07
0.05
0.11
0.05
0.08
0.07
0.12
0.07
0.09
138
V. Viswanath Shenoi et al.
DSR
SSR
LR
RLB
IR
SDM
ER
FR
FPIS 0.1 0.12 0.13 0.11 0.12 0.14 0.1 0.12 0.13 0.1 0.12 0.13 0.09 0.11 0.12 0.09 0.11 0.12 0.09 0.1 0.11 0.09 0.11 0.12
FNIS 0.04 0.06 0.07 0.02 0.03 0.05 0.01 0.03 0.04 0.01 0.03 0.04 0.04 0.05 0.07 0.01 0.03 0.04 0.01 0.02 0.04 0.04 0.05 0.07
Table 26 Fuzzy Positive and Negative ideal solution
SCRM in Indian Manufacturing Industries
139
140
Table 27 Distance of
alternatives from FPIS and
FNIS, closeness coefficient
Table 28 Rank of the
alternatives with respect to
their closeness coefficient in
FTOPSIS
V. Viswanath Shenoi et al.
Alternatives
RP
RM
RA
RS
Alternatives
RM
RS
RA
RP
D+
0.55
0
0.53
0.25
CC
1
0.57
0.09
0.05
D−
0.03
0.58
0.05
0.33
CC
0.05
1
0.09
0.57
Ranking
1
2
3
4
mediating effect. It is observed that if the p-value falls below the established α level
of 0.05, indicating the association between the pair of IV and DV (i.e., individual
risk construct and performance), risk is significantly reduced by the inclusion of
the mediating variable (namely, risk planning, risk monitoring, risk avoidance, risk
sharing). From the results obtained, it is inferred that the mitigation strategies have
significantly reduced the risk. Hence, all hypotheses H1 through H4 are accepted.
Inference from the Empirical Study
We identify the constructs of SCRM through empirical study. The validation of
constructs is done through a series of tests to identify the various aspects of
SCRM. The present work reinforces that the organization needs to have a longterm association with the supply chain partners. The long-term associations between
the trading partners need to be established by sharing of information, risk and
rewards equitably. Our findings indicate that the mediators (namely, risk planning,
risk monitoring, risk avoidance, and risk sharing) have mediating effect on the
relationship between the constructs of SCRM, mitigation strategies, and the DV,
taken one at a time.
Implications for Practitioners
In a competitive environment, the risk is inevitable. A strategic plan is necessary
to face risk. In most cases, we observe that mediating variable, risk monitoring,
significantly reduces the risk, followed by risk sharing, risk avoidance, and risk
planning respectively. The findings suggest that regular monitoring of the supply
chain is essential for a resilient supply chain.
SCRM in Indian Manufacturing Industries
141
Suggestions and Recommendations for Policy Makers
The policymakers should train the practicing managers in risk management. The
process of SCRM involves the design of strategy and framework. An instrument
is essential to validate the framework pertaining to the industry. Based on the
framework, functional managers implement operational decisions. Managers in all
the levels of the supply chain need to know their business and be aware of types and
levels of risks perceived. Risk awareness leads to efficient monitoring of the supply
chain and enables the successful implementation of the SCRM.
5.2 Results of Application of FCM to SCRM
Instantaneous vector Q1 represents the risk perceived during an instant of time. The
instantaneous vector Q1 is passed on to the adjacency represented in Table 3. The
resultant vector is threshold to one and is passed on to the adjacency matrix until the
equilibrium.
Equilibrium is attained in the third iteration (refer to Table 29). The equilibrium
vector obtained is passed on to the relationship matrix M . The results in Table 30
suggest that the risks are mitigated, and the impact of risks is alleviated to a
greater extent, as the results show negative values or zeroes. The equilibrium vector
obtained is passed on to the relationship matrix M. The results in Table 31 suggest
Table 29 Results of product of instantaneous vector Q1 and dynamical system matrix E Q1
RS1
Q2 =˜ RS1
RS2
Q3 =˜ RS2
RS3
QDSR
1
0
1
8
1
8
QSSR
0
1
1
7
1
8
QLR
0
1
1
7
1
8
QRLB
0
1
1
7
1
8
QIR
0
1
1
7
1
8
QSDM
0
1
1
7
1
8
QER
0
1
1
7
1
8
QFR
0
0
0
8
1
8
R1
0
1
1
6
1
7
R2
0
1
1
4
1
5
Table 30 Results of product of equilibrium vector Q3 and relationship matrix M RS3
QDSR
1
QRP
0
QSSR
1
QRM
0
QLR
1
QRA
0
QRLB
1
QRS
0
QIR
1
QSDM
1
QER
1
QFR
1
R1
1
R2
1
Table 31 Results of product of instantaneous vector Q1 with relationship matrix M
RS3
QDSR
1
QRP
5
QSSR
1
QRM
7
QLR
1
QRA
6
QRLB
1
QRS
6
QIR
1
QSDM
1
QER
1
QFR
1
142
V. Viswanath Shenoi et al.
the levels at which the individual mitigation strategy needs to be implemented to
mitigate the risk and enhance the performance. The highest score of 7 is obtained for
risk monitoring, suggesting that regular monitoring of the supply chain is essential
for making proactive decisions. The next highest score of 6 is realized for risk
avoidance, and risk sharing and the score of 5 for risk planning.
5.3 Comparison of the Proposed FCM and Fuzzy TOPSIS
Model
The problem of selecting the best mitigation strategy is evaluated through two
approaches. The proposed model is based on the decision makers’ perception of
risk. The ranking of the mitigation strategy is comparatively the same using both the
approaches (refer to Tables 28 and 31). Both the methods scale well in the selection
of mitigation strategies in this case.
The FCM approach interacts with every other risk factors before attaining the
equilibrium. On the other hand, FTOPSIS helps us to evaluate the ranking of the
alternatives to mitigate the risk factors. The FCM approach not only suggests the
level of application of alternatives but also predicts the risk faced by the firm using
the instantaneous vector (refer to Table 30).
6 Conclusions
The study is performed to identify the constructs of SCRM from the manufacturers’ perspective. Validation of constructs is done through a series of tests to capture
the various aspects of SCRM. The present work reinforces that the organizations
needs to have a long-term association with the partners of the supply chain. It is
evident that every firm encounters risks that arise from various entities of the supply
chain. Hence, the supply chain managers need to be proactive in identifying the
risk as and when it surfaces in the supply chain. If ignored, it may lead to grave
consequences. Practicing managers can use the proposed methodology to identify
the risk, which also suggests an appropriate mitigation strategy that reduces the
impact of risk on the supply chain, resulting in improved performance.
FCM approach predicts the future risk and also suggests the mitigation strategies.
FTOPSIS ranks the alternate mitigation strategies. From our study, it is concluded
that risks have to be identified as soon as they surface. In our study, both the
approaches scale well in finding the appropriate mitigation strategies. Accordingly,
one needs to implement an appropriate mitigation strategy to reduce the impact
of risk and improve the supply chain performance. Our findings emphasize that
monitoring supply chain is essential for sustained performance. Also, training of
managers is essential to be risk-sensitive. Broader insights into the models are
SCRM in Indian Manufacturing Industries
143
possible only through a larger data set. Due to operational constraints in the present
study, the survey is restricted to the samples available. Future studies can also look
into specific risk aspects based on different types of manufacturing and service
industries.
Acknowledgments The authors are most grateful to the reviewers of the questionnaire, who have
suggested constructive improvements and augmentative comments. The authors also thank the
respondents for taking their valuable time in participating in the empirical study. The authors are
grateful to the editors and reviewers, who have given us a chance to improve the original version
of the book chapter.
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Improving Service Supply Chain
of Internet Services by Analyzing Online
Customer Reviews
Suchithra Rajendran and John Fennewald
1 Introduction
Service supply chain, a series of activities involving the flow of service, includes
customers, service providers and service producers, as the main entities (Sampson
2000). Among these components, customers and their opinion are considered to be
essential factors in the production process (Shahin 2010). In particular, services,
such as wireless telecommunications and internet service providers, involves direct
customer-provider interaction, and hence, it is necessary to investigate such services
for quality improvement. More specifically, the wireless communications industry
is proliferating with a reported growth of 4.1% from 2013 to 2018 and an expected
growth of 3.5% from 2018 to 2023 in the United States (Ibis World Report 2020).
This expected industry growth will lead to an estimated revenue increase of $114.6
billion (Ibis World Report 2020). Several components factor into this anticipated
advancement, including faster connectivity speeds and better service, which leads
to an overall desire for increased internet usage and the idea that internet access is a
necessity rather than viewing it was a luxury (Ibis World Report 2020). The wireless
communications industry is highly competitive, as approximately two-thirds of the
adults use broadband internet at home (Kohut 2018).
Despite this level of competition, in the United States, the industry is dominated
by very few noticeable players, as the five biggest companies combine to control
over 68% of total market share—AT&T (24.9%), Comcast (17.2%), Charter
S. Rajendran () · J. Fennewald
Department of Industrial and Manufacturing Systems Engineering, University of Missouri,
Columbia, MO, USA
Department of Marketing, University of Missouri, Columbia, MO, USA
e-mail: RajendranS@missouri.edu
© Springer Nature Switzerland AG 2021
S. Srinivas et al. (eds.), Supply Chain Management in Manufacturing and Service
Systems, International Series in Operations Research & Management Science 304,
https://doi.org/10.1007/978-3-030-69265-0_5
147
148
S. Rajendran and J. Fennewald
(12.9%), Verizon (7.4%), and CenturyLink (5.8%) (Ibis World Report 2020). In
such a competitive environment and a quickly growing industry, Internet Service
Providers (ISPs) must ensure that their efforts are targeted towards attracting and
retaining customers for the continued growth of their business. Nearly 78% of
revenue originates in the ISP-to-customer sector, representing the largest market in
which ISPs operate and compete. This sector includes both residential and business
services with the most common activities involving downloading multimedia contents, browsing the internet, and using basic internet applications such as accessing
email accounts (Ibis World Report 2020). ISPs in this domain compete on many
factors, namely, availability of service, price, speed of connectivity, and customer
service (Ibis World Report 2020).
In order to attract and retain the most customers possible, ISPs must determine
the client’s expectations and tailor their offerings to meet consumer needs and
wants. One of the best ways for ISPs to identify what their users perceive about their
services is to analyze online reviews posted by customers. Several social networking
sites allow consumers to discuss their experiences with various ISPs and detail
their levels of satisfaction or dissatisfaction. As Web 2.0 has gained traction and
popularity, this style of content that is generated by users has increased rapidly (Guo
et al. 2017). Reviews can be either negative or positive, and since negative reviews
circulate five to six times faster than positive reviews, their impact on the business
can be monumental (Salehan and Kim 2016).
As social media usage continues to grow, enterprises experience additional roadblocks since they must examine OCR to understand how customers are interacting
with their products and to determine the performance of their products and services
(Sen and Lerman 2007). Recently, studies have also investigated online customer
reviews (OCR) to find the strengths and weaknesses associated with the products
and services offered by a business (Salehan and Kim 2016; Srinivas and Rajendran
2019; Rajendran 2020). By using OCR to understand the strengths and weaknesses
of their offerings, any organization, including ISPs, can discover more ways to
improve and enhance the experiences that customers have with their products and
services.
Insights that ISPs may extract from online customer reviews include deciding
which of their services to improve in order to satisfy their clients who have been
disappointed in the past or determining the areas in which they perform very well
and developing marketing plans to highlight those fields of service to attract more
customers (Rajendran and Pagel 2020a). As studies, such as Cheung and Lee (2012),
have shown that new customers place heavy emphasis on the opinions of existing
clients and that OCR weigh heavily into a consumer’s view on which company to
choose, it is extremely likely that potential ISP consumers might check OCR to
gain insight into the experiences that prior users have had with each ISP that they
are considering.
Each ISP adds value to customers in a different way tailored to its own business
model. Many companies find success through economies of scale by bundling
phone, internet, and television services into one package for consumers. Other ISPs
find a niche market and focus on providing the best service in a particular sector,
Improving Service Supply Chain of Internet Services by Analyzing Online. . .
149
such as rural areas (Ibis World Report 2020). Regardless of what an ISP’s business
model is, OCR carry abundant information that can be beneficial to ISPs as they
seek to provide the most value for their customers. The purpose of this paper is
to identify the strengths, weaknesses, opportunities, and threats of an ISP utilizing
knowledge mining to improve customer satisfaction based on information gained
from online consumer reviews. Each consumer review explores various topics
(e.g., speed, connection, customer service, etc.), and conveys an attitude (positive,
negative, or neutral) towards each topic. In this study, we use text analytic methods
to extract and analyze thousands of reviews for identifying those key topics and
propose managerial recommendations based on our results.
The rest of the paper is organized as follows. Section “Literature Review”
covers a review of several prominent studies discussed in the literature. A thorough
discussion of the proposed methodological approach is given in Section “Methodology”. Section “Case Study” includes the description of the case study as well
as an analysis of the results obtained. Lastly, Section “Conclusions” presents the
conclusions and the scope of later work.
2 Literature Review
As discussed earlier, the focus of this paper is to capture the voice of customers
using online review mining and enable ISPs to provide better service. Therefore, in
this section, we present some prominent studies related to wireless service providers
and the impact of word of mouth on companies.
2.1 Wireless Service Providers
One of the very first studies on customer expectations in the wireless service
industry was pioneered by Zander (1997). The author explored the idea that
customers of wireless communication services expect to receive the same services
as they would receive from a fixed network. The paper analyzed both a “universal”
coverage scenario providing wideband services at all locations and a “hot-spot”
scenario where coverage is offered, but only in select geographical areas. The study
showed companies the benefits of presenting wireless services to all consumers and
but also indicated that as bandwidth increases, the infrastructure cost of a wireless
system would also increase. Bar and Park (2005) looked at public Wi-Fi networks
in the United States and the policy issues that the new trends raise. Metropolitan
cities already have the existing infrastructure which supports the Wi-Fi networks,
and they can help provide connectivity for city employees while making urban areas
more desirable for residents and businesses.
With the rapid development in technology, broadband connectivity has become
more widespread, and voice services are more mobile; as a result, ISPs are bundling
150
S. Rajendran and J. Fennewald
voice services with broadband connectivity in homes. According to Markendahl
and Makitalo (2007), ISPs consider the following important factors: network
operation and access provisioning, customer acquisition and relations, trustworthy
relationship with the customer. The authors also hypothesize that actors from outside
the telecommunication industry will venture into this business and that wireless
access is increasingly becoming a service with a wide coverage area rather than
existing solely in the location where the service is officially provided.
Seo et al. (2008) highlighted the challenges that the wireless telecommunication
market is facing, especially with the growing competition. Unlike goods, wireless
services have to be offered continuously, and hence, developing strategies to
retain and attract new customers is essential. Their study tried to understand how
variables such as customer-related factors (e.g., age and gender) affect service plan
complexity. A similar research was conducted by Quach et al. (2016), in which
the authors examined the different aspects of an ISP’s service quality and their
impact on customer loyalty. They collected data from more than 1200 internet users,
segmented the information based on the customer’s internet usage, and recorded
their perception of service quality dimensions. Their study proved that the perceived
service quality dimension has an impact on both attitudinal and behavioral loyalty.
2.2 Electronic Word of Mouth (eWOM)
Erkan and Evans (2016) examined the influence of eWOM in social media on
consumers’ purchasing intentions. Several hypotheses were considered, and the
authors proved that eWOM is positively related to consumers’ purchasing intention,
the usefulness of eWOM information is positively associated with the adoption
of eWOM information, and the quality of eWOM is positively associated with
usefulness. Wang et al. (2016) showed several differences between traditional WOM
and eWOM, including the anonymity of eWOM messages, the fact that multiple
people can access eWOM at any time, and that eWOM is more persistent and
measurable. Their research indicated that about 70% of 28,000 internet users in
56 countries rely on online consumer reviews for recommendations.
Text analytic tools for examining eWOM have not only been used in product
improvement, but also in the service sector (Rajendran and Pagel 2020a). For
instance, Liang et al. (2018) studied the effect of eWOM on hospitality and tourism
management. Their study showed that eWOM is highly influential due to its speed,
amplitude, and convenience. This idea, coupled with the fact that WOM reduces
a company’s ability to influence consumers through traditional marketing and
advertising, shows how vital it is for companies to become actively involved in
online consumer communities (Rajendran and Pagel 2020b).
While the studies mentioned above examined the relationship between online
reviews and customer purchasing intentions, Jalilvand et al. (2011) investigated the
reason for the extensive spread of WOM. Their research showed that consumers
spread WOM for several reasons, including extreme satisfaction or dissatisfaction,
Improving Service Supply Chain of Internet Services by Analyzing Online. . .
151
commitment to the firm, length of the relationship with the firm, and novelty of
the product. Additionally, users may be motivated to share their experiences due to
satisfaction, pleasure, or sadness. The authors showed that the main antecedents of
WOM influence are demographic similarity, and perceptual affinity, and that WOM
could influence product evaluations.
Balaji et al. (2016) examined how negative WOM (NWOM) on social networking sites (SNS) can adversely impact companies. Customer complaints can lead to
brand dilution, volatile stock prices, and on a grand scale can even be the cause for
public relations crises. Approximately 77% of online shoppers rely on user reviews
to make purchasing decisions, and over one million people read product or service
reviews every week on SNS, and more than 80% of these reviews are negative. Their
study looked at the cognitive dissonance and social support theories to understand
the determinants of NWOM on SNS. Since sharing opinions is a social activity,
the social support theory shows that NWOM sharing may be due to the desire for
social interaction. Additionally, consumers may engage in NWOM communications
in order to reduce their cognitive dissonance levels because they are frustrated with
a product or service.
This proposed research is one of the first to analyze online customer reviews
posted for internet service providers using text analytics. We propose insights and
managerial recommendations based on strategic planning tool (SWOT) and six
sigma techniques (root cause analysis) to capture the voice of the customers. Though
the literature discusses few studies that are conducting the SWOT analysis for
internet service providers, to the best of the authors’ knowledge, this study is the
first to leverage customers’ reviews and understand the voice of the customers for
this purpose.
3 Methodology
Figure 1 presents a flow diagram showing the methodological framework used
for data collection and analysis. The proposed approach will first use a web
scraper to extract thousands of publicly available customer review archives from
online sources. Next, the reviews are separated into three categories based on the
consumer’s rating (which is on a scale of 1–5). On the rating scale, a value of
“1” indicates a highly-dissatisfied customer, and “5” indicates a highly-satisfied
customer.
All reviews with a rating of “1” or “2” are marked as “negative”, reviews with
a score of “3” are labeled as “neutral”, and reviews with a rating of “4” or “5” are
labeled as “positive”. Upon separation into negative, neutral, and positive categories,
bigrams and trigram analyses are used to identify the bag of words commonly cooccurring and gain further insights into the context in which consumers use certain
terms. These frequently co-occurring words are analyzed and compared across
different companies in the ISP industry with the goal of creating a customer-centric
SWOT and a root cause analysis using the negative, neutral, and positive bigrams
and trigrams. Figure 1 provides an overview of the proposed approach.
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S. Rajendran and J. Fennewald
Fig. 1 Methodological Framework
3.1 Web Scraping and Text Pre-Processing
The consumer reviews of the ISPs under study are scraped from several online
customer review sites, and since the customer feedback might not be particularly
centered around any one single aspect of the corporation, reviews are separated into
individual sentences, which are analyzed as independent comments. The reviews
are cleaned prior to doing the text analysis by removing incomplete, duplicate,
and irrelevant reviews. Subsequently, the sentences are tokenized, special characters
and non-English words are eliminated, inflected words are stemmed, characters are
converted to lowercase, and stop and infrequent words are removed.
3.2 Topic Identification Using Bigrams and Trigrams Analyses
After scraping and pre-processing, the most frequent topics discussed in reviews
need to be identified, which is achieved by analyzing the frequent words, bigrams,
and trigrams. A bigram is a combination of two words that are used consecutively in
Improving Service Supply Chain of Internet Services by Analyzing Online. . .
153
relevance to the same topic. Examples pertaining to our study would be “customer
service” or “connectivity issues”. A trigram is a group of three words that are used
consecutively, and examples would be “good customer service” and “connectivity
issues rural”.
This section presents an analysis of bigrams and trigrams, as discussed by
Jurafsky and Martin (2014). If x1 , x2 , . . . , xN are a set of words, then the probability
that these words occur in a sequence is denoted by P(X) as shown in Eq. (1).
P(X) = P (x1 . . . xN )
(1)
The probability of x2 occurring after x1 in a sequence is shown in Eqs. (2) and
(3). Similarly, the probability of x3 occurring after x2 and x1 in a sequence is given
in Eqs. (4) and (5) is derived from Eq. (4).
P (x2 |x1 ) =
P (x1 , x2 )
P (x1 )
(2)
P (x1 , x2 ) = P (x2 |x1) × P (x1 )
(3)
P (x1 , x2 , x3 ) = P (x3 |x1 , x2 ) × P (x1 , x2 )
(4)
P (x1 , x2 , x3 ) = P (x1 ) × P (x2 |x1 ) × P (x3 |x1 , x2 )
(5)
If we extrapolate this expression to N words, the probability of word xN occurring
in a word sequence x1 , x2 , . . . , xN − 1 is given by Eqs. (6) and (7).
P (x1 . . . xN ) = P (x1 ) × P (x2 |x1 ) × P (x3 |x1 , x2 ) . . . P (xN |x1 , x2 . . . xN−1 )
(6)
P (x1 . . . xN ) =
N
Pr (xn |x1 . . . xn−1 )
(7)
n=1
Applying the conditional probability chain rule to the sequences of words under
consideration, Eqs. (8) and (9) give the probability of N words occurring in a
sequence.
P (x1 . . . xN ) = P (x1 ) × P (x2 |x1 ) × P x3 |x12 . . . P xN |x1N−1
P (x1 . . . xN ) =
N
P xN |x1N−1
(8)
(9)
i=1
In Eq. (10), we consider the Markovian assumption for the bigram model.
P xN |x1N−1 = P (xN |xN−1 )
(10)
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S. Rajendran and J. Fennewald
where P(xN | xN − 1 ) is approximated by using the proportion of the bigram count of
xN − 1 and xN (represented by ν(xN − 1 xN )) to the sum of the frequency of all the
bigrams containing xN − 1 ( x ν (xN−1 x)) as shown by Eq. (11).
P (xN |xN−1 ) =
ν (xN−1 xN )
x ν (xN−1 x)
(11)
Constraint (12) uses the trigram model to predict the conditional probability of
the Nth word in a sequence.
P (xN |xN−1 , xN−2 ) =
ν (xN−2 xN−1 xN )
x ν (xN−2 xN−1 x)
(12)
3.3 SWOT Strategic Planning Using Bigrams and Trigrams
Analyses
For each ISP, the reviews are separated based on their ratings as negative (≤2 stars),
neutral (3-stars), and positive (≥4 stars), following which commonly co-occurring
bigrams and trigrams are identified for each category. These bigrams and trigrams
are then used to conduct a SWOT analysis for individual ISPs and the industry as a
whole.
SWOT is a planning tool most commonly used for recognizing the strategic
factors (both internal and external) that are important to the development of an
enterprise (Kurttila et al. 2000). The strengths and weaknesses are identified by
analyzing the internal characteristics of the organization, while opportunities and
threats constitute the external elements, such as competition. The basic framework
of SWOT analysis is shown in Fig. 2. Such a SWOT assessment highlights the
Internal
Weaknesses
ISP offerings that enrich customer’s Qualities of the ISP that hurt customer
experience and lead to positive WOM experience and create a negative
(e.g., fast speeds, helpful technicians)
perception (e.g., poor customer service,
slow speeds in rural areas)
External
Strengths
Opportunities
Potential changes companies can adapt to
enhance reputation (e.g., bundling
packages, better trained customer service
representatives)
Threats
Obstacles that the ISP faces due to
external
environment
(e.g.,
governmental regulation, competition
from other companies)
Fig. 2 Basic Framework of SWOT Analysis based on ISP Customer Experience
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Table 1 Overview of the
ISPs considered
Company
ISP#1
ISP#2
ISP#3
ISP#4
ISP#5
ISP#6
ISP#7
ISP#8
Primary services offered
Wireless Internet Phone services
x
x
x
x
x
x
x
x
x
x
x
155
Television
x
x
critical factors that influence the viewpoint of the corporation and can aid the ISP in
their planning decisions.
4 Case Study
In this study, we have chosen eight companies that control over 70% of the total
market share in the ISP domain. We have de-identified and referred to the ISPs
under consideration as ISP#1 through #8. In total, 23,145 online customer reviews
are considered with the following breakdown of reviews for each company: ISP#12407, ISP#2-4208, ISP#3-937, ISP#4-3446, ISP#5-989, ISP#6-3324, ISP#7-5019
and ISP#8-2815.
Table 1 shows an overview of the ISPs considered and the primary services
offered. As it can be seen, wireless internet is a primary service for every company
except ISP#3 and ISP#6; and ISP#2 and ISP#3 are the only two companies that offer
entertainment media as their primary services.
4.1 Data Description
Table 2 shows the data features and an explanation of each field. The two fields
that are considered in this study are “Rating” and “Review”. Figure 3 shows the
distribution of reviews for the different internet service providers under consideration. We can see that the majority of the time, all companies are being negatively
perceived by customers. More specifically, over 80% of the total reviews that are
posted for ISP#1, ISP#3, ISP#4 and ISP#8 are given less than a 2-star rating. On the
other hand, relatively more positive reviews are observed for ISP#5, whereas neutral
reviews are comparatively more in ISP#6.
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S. Rajendran and J. Fennewald
Table 2 Data fields
Field
Rating
Review
Explanation
Contains a rating on a scale from 1–5 of how much the customer liked the service.
The value “1” represents a highly unsatisfied customer while a “5” represents a
highly satisfied customer
Contains the review body
Fig. 3 Distribution of the Reviews
4.2 Experimental Results
Once the reviews are extracted, the data is cleaned using the procedure discussed
in Sect. “Web Scraping and Text Pre-Processing”. This data pre-processing is done
with the modules in the Python natural language toolkit (NLTK). In addition to the
default English stop words in the NLTK toolkit, we also created a custom set of stop
words to filter unnecessary words that do not add value to our study.
Table 3 gives an overview of the topics that occur most frequently in the
OCR, a description for each topic, and the significant words associated with each
topic. Clearly, ‘customer service’, ‘connectivity’ and ‘speed’ are the key topics
that customer reviews are emphasized on. For the purpose of illustration, the most
frequently occurring bigrams and trigrams related to the ‘service’ Topic is shown
in Fig. 4. Many of these word combinations revolve around customer service or
whether the actual services (wireless internet, TV, phone) are good or bad.
Table 4 shows the list of most positively- and negatively-discussed topics based
on bigram and trigram analyses. Topics related to customer service, connectivity,
compatibility with other devices, billing and payments are several of the most
common.
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157
Table 3 Key topics overview
Topic
Service
Phone
Contract
Rural area
Representative
Speed
Description
The service provided to customers by their
ISP
Use of phone with service from an ISP
The signed agreement between ISPs and
their customers
ISP performance in sparsely populated areas
Employee of an ISP who helps customers
with difficulties
Measure of how quickly service reaches
customers
Significant words
Slow installation, setup time
Coverage, connectivity, cell,
data
Cancel, terminate, switch,
billing
Rural connection, dead zone
House calls, technician,
unhelpful
Fast, slow, download, high
BIGRAMS
(‘customer’, ‘service’), (‘cancel’, ‘service’), (‘phone’, ‘service’), (‘service’, ‘call’),
(‘service’, ‘slow’), (‘tv’, ‘service’)
TRIGRAMS
(‘worst’, ‘customer’, ‘service’), (‘terrible’, ‘customer’, ‘service’), (‘contact’, ‘customer’,
‘service’), (‘poor’, ‘customer’, ‘service’), (‘horrible’, customer’, ‘service’), (‘call’,
‘customer’, ‘service’), (‘customer’, ‘service’, ‘rep’), (‘call’, ‘cancel’, ‘service’)
Fig. 4 Frequently Occurring Bi-grams and Tri-grams for Negative Opinions Related to the Topic
‘Service’
4.3 SWOT Analysis
From the topics discussed in Table 4, SWOT analysis for each ISP can be developed.
A sample SWOT Analysis for ISP#1 is given in Table 5. Following that, we also
propose several managerial insights based on the observations made.
The managerial insights derived from the SWOT analysis pertaining to a specific
company (ISP#1) is presented below. A similar approach can be adopted for other
providers as well.
• Customized Bundle Plans: Develop a bundling plan that can be tailored to
individual consumer’s preferences. In other words, the most effective package
will be the one which allows consumers to pay for specific services which they
value and nothing more (e.g., fast speeds to play games online but limited TV
channels, wide range of TV channels without high-speed internet).
• Employee Training: Ensure that all customer service representatives are well
trained on how to best serve their customer base. This may include (i) educating
them on products, policies, and packages that the company offers, and (ii) clearly
communicating with customers on billing dates and plans.
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S. Rajendran and J. Fennewald
Table 4 Positively and negatively perceived topics by customers for each ISP
ISP
ISP#1
Topics based on positive reviews
Fast speeds internet in cities; versatility;
easy installation
ISP#2
Helpful technician; good streaming for
playing online games
Triple play package; fast speed; wide TV
channel variety
Works well even during bad weather;
good upload and download speeds
ISP#3
ISP#4
ISP#5
ISP#6
ISP#7
ISP#8
Good family plan; cost effective; service
representative speaks in English fluently
Binge data streaming; good service in
Europe; service representatives give good
instructions
Consistent internet service; helpful
salespeople
Helpful technicians; wide TV channel
selection; easy and quick troubleshoots;
providing house calls services
Topics based on negative reviews
Slow service in rural areas; changing
billing dates and switching plans without
prior notification; routers are not durable;
slow speed while streaming media
Wrongful billing; increasing service costs
Hidden fees; disrupted TV streaming
Slow installation; overseas call centers;
fixed 2-year contract; poor transition
from one generation to its next
Unauthorized payments and charges; hard
to cancel contract
Offshore call centers; incompatible with
certain android devices
Poor international service; call center
representatives do not speak English
fluently; poor signal in rural areas; slow
4G LTE
Services are priced high; DVR is
expensive
Table 5 SWOT Analysis for ISP#1
Strengths
– Fast speeds
– Quick installation
– Widespread coverage
– Highly compatible across different devices
Opportunities
– Expand TV channel offering
– More comprehensive training for customer
service representatives
– Improve services in rural areas
Weaknesses
– Routers are not durable
– Irregular payments
– Expensive
– Switching plans and billing dates
– Slow streaming speed of media-services
Threats
– Well marketed triple and quadruple play
packages by other ISPs
– House calls services offered by certain
companies
• Marketing Company Strengths: Market the company’s strongest suits, such as
high-speed internet and widespread coverage. Moreover, as one of the industry
leaders, it is also essential to advertise that ISP#1 possesses a large portion of the
market share.
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4.4 Link and Root Cause Analyses
Figure 5 presents a positive link analysis representing the overall topics that are
commonly discussed in the positive reviews across all ISPs. A larger-sized node
indicates that the topic is spoken more often by customers. Clearly, we can see
that ‘Bundling’, ‘Connectivity’, and ‘Technical Service’ were three of the most
repeated topics in the positive review domain. Under the ‘bundling’ topic, customers
praise the triple and quadruple play packages as well as the family plans due to
their cost savings. The topic ‘Connectivity’ is observed to be positively spoken by
customers of certain companies and negatively for other ISPs. Thus we interpret
that connectivity is a key issue that companies must pay attention to. Customers
prioritize consistent connection over speed, while good online gaming streaming is
also a factor that they consider. During a technical issue, customers expect proper
assistance either by visiting them at home or solving the problem over the phone
(i.e., easy troubleshooting).
Figure 6 depicts the results of the root cause analysis through a fishbone diagram
highlighting the overall causes that lead to customer dissatisfaction in the internet
service sector. The primary causes are ‘Contracts’, ‘Connectivity’, ‘Cell Phone
Service’, and ‘Call Center’. The associated secondary causes are also shown in Fig.
6. Clearly, customers are highly unsatisfied when they are charged with additional
fees without prior notification. Interrupted service and slow loading speed in rural
areas are also some issues that users experience. Moreover, they are not comfortable
with automation, especially studies have proven that the older population is resistant
to automation changes (Vaportzis et al. 2017).
Fig. 5 Positive link analysis
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S. Rajendran and J. Fennewald
Fig. 6 Root cause analysis Ishikawa Diagram
4.5 Managerial Implications
Based on the bigram and trigram analyses, we suggest the following recommendations to the internet service providers as a whole.
• Streamline customer service call process: When customers attempt to contact
ISPs over the phone, they often complain that it is difficult to speak to a human
and that processes are automated. Our recommendation would be for ISPs to
have online live chat programs that can achieve a balance between customer
satisfaction and cost.
• Provide positive work environment for customer service representatives: Many
online reviews mention frustrations with customer service representatives,
specifically for being “rude”, “unhelpful” or “not caring” about the user
problems. Prior studies have proven a positive relationship between employee
job stress, job satisfaction, and customer loyalty (Loveman 1998; Hansemark
and Albinsson 2004; Hill and Alexander 2017). Therefore, we theorize that
the rude behavior of customer service agents is due to the burnout that these
employees experienced from answering customers’ calls monotonously. Hence
it is recommended that ISPs provide a more positive work environment offering
free/discounted health club memberships and conduct regular medical checkups.
Also, ISPs can develop a handbook for call-center representatives providing a
set on instructions on how to handle different types of customer grievances.
• Payment communication: Many consumers are upset that ISPs did not communicate clearly about contracts, extra fees, change in billing dates, etc. Our
recommendation for ISPs is to interact and update customers by different modes
of communication (such as, phone calls, emails, and text messages).
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161
• Rural area connectivity: A large portion of customers complain about poor
internet service in rural areas. We suggest that stronger infrastructure has to
be established in the countryside to better serve their clients and provide faster
speeds.
• Device compatibility: Some consumers criticize that their internet service does
not work with various devices, whether that be their brand of routers, cellphones,
or their gaming console. To avoid this issue, we recommend that ISPs keep device
compatibility as a priority when designing their products and services.
• Improve customer service representative language training: Based on the
reviews, consumers are commonly distressed that the representatives lack
English fluency and hence are not able to entirely comprehend their
grievances. We recommend that ISPs offer extensive language training for their
representatives who are non-native English speakers.
5 Conclusions
In recent years, due to the increasing popularity of service sectors, service supply
chains are being widely investigated to improve service quality. Studies have shown
that online consumer reviews heavily influence consumer’s decisions across many
industries, including ISPs. This research is one of the first to provide managerial
insights for ISPs to improve the wireless services offered to their customers
by analyzing online user reviews. While very few research has been conducted
involving SWOT analysis from an internet provider’s perspective, examining online
customer reviews enables ISPs to understand their consumer needs better. Over
23,000 online reviews were first collected using web scraping, reviews were
subsequently separated based on their star ratings, following which commonly cooccurring words were identified in each category, and a SWOT and root cause
analyses were finally conducted.
We identified six meaningful topics using our approach and conducted a SWOT
analysis to showcase the critical factors that impact consumers’ choice of ISP. We
also proposed the following managerial recommendations to the ISP sector, based
on the results obtained.
•
•
•
•
•
•
Streamline customer service call process
Provide a positive work environment for customer service representatives
Interact with customers using different means of communication
Establish proper infrastructure in rural areas to improve connectivity
Make design changes to ensure device compatibility
Provide language training for representatives
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S. Rajendran and J. Fennewald
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An Integrated Problem of Production
Scheduling and Transportation
in a Two-Stage Supply Chain
with Carbon Emission Consideration
Bobin Cherian Jos, Chandrasekharan Rajendran, and Hans Ziegler
1 Introduction
Sequencing and scheduling involve decision-making that plays a vital role in various
manufacturing and service industries (Pinedo 2012, pp. 1–5). In practice, many of
these industries strive to ensure the effective use of available resources to meet
the organization’s specific objectives by improving the efficiency in scheduling
and sequencing. Supply chain management involves the reduction in operational
costs, improving customer service levels, and establishing a competitive advantage
by integrating and coordinating different functions of the supply chain (Chopra
et al. 2016, pp. 1–10). The scheduling problem in a supply chain may face several
constraints arising from various levels of the chain. Therefore, scheduling theory
assumes great significance in integrated supply chain planning as well as logistic
planning activities in organizations aiming to improve customer service (Azadian
et al. 2015). In this study, an integrated logistics and scheduling problem with the
consideration of carbon emissions in a supply chain system is described, and a
mixed-integer linear programming (MILP) model is proposed.
B. C. Jos ()
Department of Mechanical Engineering, Mar Athanasius College of Engineering,
Kothamangalam, Kerala, India
e-mail: bobincherian@mace.ac.in
C. Rajendran
Department of Management Studies, Indian Institute of Technology Madras, Chennai, India
e-mail: craj@iitm.ac.in
H. Ziegler
School of Business, Economics and Information Systems, University of Passau, Passau, Germany
e-mail: hans.ziegler@uni-passau.de
© Springer Nature Switzerland AG 2021
S. Srinivas et al. (eds.), Supply Chain Management in Manufacturing and Service
Systems, International Series in Operations Research & Management Science 304,
https://doi.org/10.1007/978-3-030-69265-0_6
165
166
B. C. Jos et al.
In the present scenario, customers being the focus of the organization, retaining
them becomes the primary objective of any organization. Retaining customers can
be achieved by meeting and sometimes exceeding their expectations. One of the
main reasons for the loss of customer goodwill is the non-conformance to due
dates (Unlu and Mason 2010). Many times, either due to the non-availability of
enough capacity to process all the jobs or the non-availability of the resource for
manufacturing, many manufacturing firms outsource some of their jobs or orders
to potential subcontractors located geographically away from the manufacturer.
Transportation delay and the associated transportation costs are additional aspects
that need to be considered while preparing the production schedule of the jobs when
outsourcing is chosen as an option (Qi 2008).
In order to reduce the tardiness of jobs, subcontractors usually increase the
number of shipments from their locations. Energy and environmental factors need
to be considered in many such cases. Most of the manufacturing firms focus on
minimizing the cost of production, sometimes by ignoring the resultant carbon
emissions. Because of the growing concerns in climate change, manufacturers
are now forced to reduce carbon emissions. Hence this study also focuses on
minimizing carbon emissions related to the transportation schedules while preparing
the operations plan in a two-stage supply chain with outsourcing options.
2 Literature Review
We have done an extensive survey on the literature which focus on scheduling of
jobs with the consideration of outsourcing options, transportation time, selection of
subcontractors, and reduction in carbon emissions. Our study integrates all these
decisions which have been considered separately in the literature.
The studies of Cai et al. (2005) and Qi (2008) considered transportation time for
the movement of jobs while preparing the production schedule. Cai et al. (2005)
considered a scheduling problem involving third-party machines having specific
time slots, with additional transportation time for the movement of jobs back to the
manufacturer. Qi (2008) considered production scheduling vis-a-vis coordination
of transport when the production or other services are outsourced, and proposed
dynamic programming algorithms. However, Qi (2008) assumed the presence of
only a single manufacturing facility located in-house and a single subcontractor.
Cao et al. (2005) were probably the first to study a parallel machine scheduling
problem associated with the option of machine selection from a set of potential
machines, each having a fixed hiring cost. They modeled their problem to minimize
the sum of total tardiness cost and the machine holding cost. Cao et al. (2005) studied the problem involving subcontractors (mostly treated as parallel machines, as
for the allocation and scheduling of jobs) to fulfill customer interests in time. RuizTorres et al. (2008) observed that many organizations might not have any in-house
production facility; rather they would outsource the manufacturing operations. The
parent organization or central manufacturing firm controls the assignment of jobs to
An Integrated Problem of Production Scheduling and Transportation in a Two-. . .
167
these parallel identical production resources/outsourced centers with its own design
and planning functions. The authors quoted Nike as an example. They proposed
heuristics to solve this problem and conducted computational experiments.
Alidaee and Li (2014) studied a similar problem with an option of choosing a
subset of subcontractors from a set of subcontractors, and to schedule the operations
in all the chosen subcontractors to minimize the sum of costs involving the cost
of holding machines, cost of total machine usage, and total tardiness costs. They
considered identical processing times and a common due date for all jobs, and
proposed dynamic programming algorithms. Mokhtari et al. (2012) presented a
job scheduling problem with outsourcing options and proposed a MILP model
to minimize the cost of mean weighted flow time and cost of processing the
outsourced orders. However, their model did not consider the transportation of
finished products. The study by Wang and Alidaee (2018) considered unrelated
parallel machines and scheduling of jobs with the objective of minimizing the total
workload. Since the objective was not correlated with minimizing tardiness, the
transportation concerns were not significant, and hence they presented mathematical
models and heuristics by ignoring the logistics schedule.
Research on the reduction in carbon emissions has attained great attention
recently because of the growing concerns on climate change. In the report of
the United Nation’s Inter-Governmental Panel on Climate Change (UN IIPC),
Kahn Ribeiro et al. (2007) states that, by 2030, energy use and carbon emissions
from transport are predicted to be 80 percent higher than the current levels. The
transportation sector is responsible for approximately 23% of the global energy
related carbon dioxide emissions (Kahn Ribeiro et al. 2007). Logistics are expected
to make a large contribution to the drastic reductions in CO2 emissions that will be
required by 2050 to contain the global temperature increase within 2 ◦ C by 2100
(McKinnon 2010).
Ritchie and Roser (2017) have consolidated various studies related to CO2
emissions. The statistics presented in their studies on global CO2 emissions show the
significance of reducing such emissions to prevent further increases in carbon emissions. Because of the growing concerns on global warming and climate changes,
many countries have implemented a ‘carbon emissions policy’ as mandatory. From
Fig. 1, it is evident that the transport sector is the second major contributor to
CO2 emissions, following the ‘electricity and heat’ sector. Hence, manufacturers
now include ‘reduction in carbon emissions’ as a key criterion while preparing the
operations plan. In our study, we also consider it as one of the key criteria while
preparing the production schedule.
Few of the recent studies in scheduling problems related to ‘reduction in carbon
emissions’ are given in this section. The reduction of potential carbon emission in an
integrated scheduling problem of ‘picking up and delivering customers to airport’
was studied by Yu et al. (2016). Their study illustrated that integrated scheduling
could reduce carbon emissions to a significant level. The problem of vehicle routing
and scheduling in a similar environment was studied by Yang and Tang (2013)
with the objective of minimizing carbon emissions. They presented a nonlinear 0–1
integer programming model. They assumed that the carbon emission (E) of a vehicle
168
B. C. Jos et al.
Global CO2 Emissions by Sector
16
12
Electricity/Heat
billion tons
14
Transportaon
Manufacturing/Const
rucon
Building
10
8
Land-Use Change and
Forestry
Industrial Processes
6
Bunker Fuels
4
Other Fuel
Combuson
Fugive Emissions
2
0
1990
1995
2000
2005
2010
2015
Source: CAIT Climate Data Explorer
Fig. 1 Global CO2 emissions by sector
is directly related to the fuel consumption (F) through the relation E = r × F,
where r is the carbon emission index parameter. Fang et al. (2011) studied a
scheduling problem in manufacturing for power consumption and carbon footprint
reduction. They assumed that the operation speed could be changed to factor the
peak load and energy consumption. Wang et al. (2019) studied an integrated single
machine scheduling and vehicle routing problem for minimizing the total carbon
emissions, which allows switching on-off the machine in-between the operations.
They proposed a mathematical model and a multi objective tabu search algorithm to
minimize the total carbon emissions.
In the literature, the research attempts on job scheduling with the selection of
outsourced centers have either assumed a common due date for jobs or have not
considered the transportation concerns (related to transportation time, transportation
cost associated with outsourced facility and resultant carbon emissions), while optimizing the specific objective function. However, those researches that considered
transportation concerns assumed a pre-set number of outsourced facilities with
identical job processing times. As seen from the review, none of the works have
incorporated the transportation parameters concerning the outsourced facilities,
while studying the problem of selecting outsourced facilities from the available
facilities. We believe that in a supply chain system, the transportation decisions are
significant while preparing the production schedule of orders with the objective of
minimizing total costs, which includes tardiness costs (Qi 2008).
An Integrated Problem of Production Scheduling and Transportation in a Two-. . .
169
Based on the research gap identified, we study an integrated problem of
logistics and scheduling of jobs in a supply chain with subcontracting decisions
and the associated transportation issues. We present a new mixed-integer linear
programming (MILP) model to solve the problem under study with the objective of
reducing the total supply chain cost, as well as limiting the total carbon emissions
to a prescribed level. Our MILP model is flexible enough to handle scenarios
where the central manufacturing firm may or may not have in-house production
facilities. Hence the model can also be implemented into a virtual enterprise system
as described by Ruiz-Torres et al. (2008).
The main contribution of the study is the proposed solution methodology that
integrates three different issues, (1) selection of subcontractors, (2) scheduling of
jobs, and (3) scheduling of logistics, which have not been considered together in
the literature so far. In addition, this study considers ‘carbon emission aspects’
associated with the shipment of finished jobs that is necessary for making the supply
chain eco-friendly.
From the viewpoint of a supply chain, the external manufacturing facility is an
outsourced center of production. From the viewpoint of scheduling theory, these
outsourced centers can be referred to as machines in general. Hence in this chapter,
the terms ‘manufacturing facility’ (referring to both outsourced and in-house) and
‘machine’ are used interchangeably within the context of our study. In this chapter,
the set of parallel in-house manufacturing facilities is referred to as a central
manufacturing facility or parent manufacturing facility.
3 Problem Description
The integrated logistics and job scheduling problem in a supply chain with nonidentical parallel manufacturing facilities is described as follows. At the beginning
of the planning period, the manufacturer (also called central organization) receives
a set of orders or jobs J = {1, 2, 3, . . . , N} from its customers. It is assumed that all
jobs are available for processing at the beginning of the planning period and need
to be processed either by in-house or outsourced facilities of the organization. The
central manufacturing center is equipped with Min parallel machines, which we refer
to as in-house facilities. In order to minimize the tardiness of jobs (to make an early
delivery), they can outsource some of the jobs to subcontractors, each equipped with
a single machining facility. We consider them as the outsourced manufacturing facilities of the central organization. Machining facilities of subcontractors need not be
identical and are located geographically away from the central organization. Hence,
outsourcing will incur transportation delay and transportation costs, in addition to
the fixed cost (hiring cost) of outsourcing the facility and the associated variable
cost of production. There are Mout potential subcontractors willing to process the
jobs as per the design and schedule given by the central organization. We consider
the availability of Min in-house machines and the Mout subcontractors’ machines
(with each subcontractor having a single machine as the manufacturing facility)
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B. C. Jos et al.
Pool of
jobs
1
Given Machining
facilies in
Parallel
A sample set of
jobs assigned to
each machine
In-house machines
2
{12- 1-7}
3
4
{10-3-11}
5
{8-2}
6
7
8
Outsourced Centers
9
10
{4-6-9}
11
12
{φ}
13
14
{5-14}
15
{13-15}
Fig. 2 Supply chain system with parallel manufacturing facilities both in-house and outsourced
together as a set of available non-identical manufacturing facilities P = {1, 2, 3, . . . ,
M} working in parallel, where n{P} = M = Min + Mout (see Fig. 2).
Some other assumptions with respect to the problem under study are as follows.
Each manufacturing facility (both in-house and outsourced) is capable of processing
any job, but at most one job at a time. Manufacturing facilities are continuously
available, and pre-emption is not allowed. There is no idle time between the processing of jobs on a manufacturing facility, other than the necessary sequence-dependent
setup time between the processing of two consecutive jobs. The processing time of
an order/job and its setup time depend on the manufacturing facility to which the
job is assigned. The resources necessary for processing the jobs are available with
the subcontractors, and jobs can be started at any facility without any delay.
An Integrated Problem of Production Scheduling and Transportation in a Two-. . .
171
All the jobs manufactured at an outsourced center need to be transported back
to the central facility after completion. The number of jobs to be transported in a
batch from the outsourced facility to the central facility may be less than or equal
to the finite capacity of the transportation vehicle. This decision on how many jobs
to be transported per trip depends upon the due date of jobs and also the carbon
emission level. This aspect of the problem is unique and significant in our study,
and is elaborated further.
For each job i ∈ J, the processing time on manufacturing facility m (Pi, m ),
the due date (Di ), and the tardiness penalty per unit time (Wi ) are known at the
beginning of the planning period. Machine- and sequence-dependent setup times on
each manufacturing facility are also known in advance. In this chapter, we hereafter
refer to them as setup times.
(m ∈ P), the machine
∗ For each manufacturing
facility
(i.e., fixed subcontracting
usage cost per unit time Cm
, the fixed hiring cost Cm
cost), the transportation time required to ship the outsourced jobs to the central
manufacturing facility (Tm ) and the associated cost per trip for transportation (Cm )
are also known in advance. There is a fixed amount of carbon emissions per
shipment (Em ) while transporting the finished jobs back to the central facility from
the outsourced facility m. Em is proportional to fuel consumption (Yu et al. 2016),
and we assume Em is, in effect, proportional to the transportation time taken by
the trucks. Hence it is calculated as Em = r × Tm , where r is the carbon emission
index parameter. The trucks available at facility m are assumed to be homogeneous
and unlimited, and each has a finite capacity, defined in terms of Bm , the number
of jobs. Transportation time, transportation cost, fixed hiring cost, and carbon
emissions per shipment are assumed to be
zero for in-house facilities. Note that
∗ is a constant for a given manufacturing
the machine usage cost per unit time Cm
facility, and is independent of the jobs. Hence the total machine usage cost of each
manufacturing facility is proportional to the sum of the corresponding setup times
and the processing times of jobs assigned to that facility. In other words, the total
machine usage cost is given by the product of the machine usage cost per unit time
and the sum of appropriate setup times and processing times of all jobs assigned
to that manufacturing facility. We also assume that a manufacturing facility with
better process capability can process the jobs in lesser time but are associated with
a higher machine usage cost (both cost per unit time and fixed hiring cost) than a
machine with lesser processing capability. However, machine usage cost per unit
time of in-house machines is assumed to be less in comparison to all outsourced
facilities, irrespective of the processing capability.
The decisions to be taken are: (i) to determine the manufacturing facilities (both
in-house and outsourced) to be chosen and engaged for processing; (ii) to determine
the subset of jobs to be processed on the selected manufacturing facilities (both
in-house and outsourced); (iii) to prepare the production schedule of jobs on each
manufacturing facility; and (iv) to prepare the transportation schedule of completed
jobs at each outsourced manufacturing facility.
We present this problem with the objective of minimizing the sum of fixed cost of
hiring manufacturing facilities, their usage cost (variable cost), tardiness penalties
(variable cost), and transportation costs incurred while shipping finished jobs from
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B. C. Jos et al.
outsourced centers (related to fixed cost per trip from each outsourced center to the
central manufacturing firm).
4 Development of the Proposed Mixed Integer Linear
Programming Model
A Mixed Integer Linear Programming (MILP) model is proposed to solve the supply
chain problem under study, and is described in this section.
Parameters:
Pi, m
Di
Wi
Tm
Cm
Cm
∗
Cm
S0, i, m
Si ,i,m
i, i
Bm
Em
EUB
EL
Fm
G
Processing time for job i on manufacturing facility m.
Due date for job i.
Tardiness penalty per unit time for job i.
Transportation time required to transport a job from manufacturing facility
m to the central manufacturing firm /* Tm = 0 if m ∈Min (the set of in-house
machines) */.
Fixed transportation cost required to transport a job from manufacturing
facility m to the central manufacturing firm /* Cm is non-decreasing with
respect to Tm ; Cm = 0 if m ∈ Min */.
= 0 if m
Fixed hiring cost associated with manufacturing facility m /* Cm
∈Min */.
Machine usage cost per unit time for manufacturing facility m.
Setup time for job i on manufacturing facility m, where job i is the first job
to be processed on facility m /* fictitious job i = 0 represents the beginning
of processing on a facility and S0, i, m is a positive integer */.
Setup time for job i processed immediately after job i on manufacturing
facility m.
Indices for jobs, i ∈ J and i ∈ J.
Capacity restriction in a truck or Maximum batch size (given in terms of
the maximum number of jobs transported per trip) from a given facility
m /* it assumes that the capacity of vehicle available at an outsourced
facility can be different from the capacity of vehicle available at a different
outsourced facility */.
Carbon emission associated with each trip from manufacturing facility m
to the central facility.
Upper bound on the total carbon emission, EUB = N × maxm (Em )
Desired limit for the total carbon emissions set by the decision makers.
EL = fr × EUB where fr denotes a fraction (0 < fr ≤ 1)
Capacity of manufacturing facility m (in terms of the number of jobs that
can be processed during the scheduling period).
A sufficiently large positive integer, where
N
N
G≥ N
i=1 maxm pi,m +
i=1 maxi ,m si ,i,m +
i=1 maxm s.0,i,m
An Integrated Problem of Production Scheduling and Transportation in a Two-. . .
173
Note: n{J} = N and n{P} = M, where J is the set of jobs and P is the set of
available manufacturing facilities (both in-house and outsourced facilities included).
Binary Variables:
Δm
Φ i, m
γi ,i,m
γN+m,i
γ
i, N + M + m
θ i, b, m
∂ b, m
a binary variable that takes the value 1 if manufacturing facility m
is selected for processing jobs; 0 otherwise.
a binary variable that takes the value 1 if manufacturing facility m
is selected and job i is assigned to facility m; 0 otherwise.
a binary variable that takes the value 1 if job i is processed
immediately after job i on the manufacturing facility m to which
both jobs are assigned; 0 otherwise.
a binary variable that takes the value 1 if job i is processed as the
first job on the selected manufacturing facility m; 0 otherwise.
a binary variable that takes the value 1 if job i is processed as the
last job on a selected manufacturing facility m; 0 otherwise.
a binary variable that takes the value 1 if order i is assigned to
facility m and transported to the central facility in batch shipment
b; 0 otherwise.
a binary variable that takes the value 1 if any job is assigned to
facility m and transported to the central facility in batch shipment
b; 0 otherwise.
Decision variables:
si
oi
ti
t i, m
∗
tb,m
t i
μm
τi
start time of job i.
occupancy time of job i /* the sum of the appropriate machine- and sequencedependent setup time and the processing time of job i on its assigned
machine at a manufacturing facility */.
completion time of job i.
time at which job i is shipped from machine m (equals its completion time ti
if processed in-house; t i, m ≥ ti for outsourced facility m).
completion time of the last job assigned to batch b of machine m /* time of
shipment of batch b from facility m */.
time at which job i is delivered at the central facility.
total machine usage time of a manufacturing facility m.
tardiness of job i.
Objective function:
Min Z =
N
Wi τi +
i=1
M
Cm
m +
m=1
N M
Cm ∂b,m +
b=1 m=1
M
∗
Cm
μm
(1)
m=1
subject to the following:
M
m=1
i,m = 1
∀i ∈ J
(2)
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B. C. Jos et al.
N
γi ,i,m + γN+m,i
= i,m
∀i ∈ J, ∀m ∈ P
(3)
i =1, i =i
N
γi,i ,m + γ i,N+M+m = i,m
∀i ∈ J, ∀m ∈ P
(4)
i =1, i =i
N
γN+m,i
≤ m
∀m ∈ P
(5)
γi,N+M+m
≤ m
∀m ∈ P
(6)
i=1
N
i=1
N
i,m ≤ N × m
∀m ∈ P
(7)
i=1
N
i,m ≥ m
∀m ∈ P
(8)
i=1
N
i,m ≤ N
i=1
N
N
γN+m,
i
∀m ∈ P
(9)
∀m ∈ P
(10)
i=1
i,m ≤ N
i=1
N
γi,N+M+m
i=1
si ≥ ti − G 1 −
M
∀i ∈ J, i ∈ J and i = i
γi ,i,m
(11)
m=1
si ≤ G 1 − γN+m,
i
oi =
N M
∀i ∈ J, ∀m ∈ P
γi ,i,m Si ,i,m +
i ,i =i m
M
γN+m,
i S0,i,m +
m
M
Pi,m i,m
(12)
∀i ∈ J
m
(13)
ti = si + oi
M
ti,m
≥ ti
∀i ∈ J
∀i = J
(14)
(15)
m=1
N
b=1
θi,b,m = i,m
∀i = J, ∀m ∈ P
(16)
An Integrated Problem of Production Scheduling and Transportation in a Two-. . .
∗
tb,m
≤ G × ∂b,m
∂b,m ≥ ∂(b+1),m
N
175
for b = 1, .., N, ∀m ∈ P
(17)
for b = 1, .., N − 1, ∀m ∈ P
(18)
∂b,m ≤ N × m
∀m ∈ P
(19)
b=1
m ≤
N
∀m ∈ P
∂b,m
(20)
b=1
∂b,m ≤
N
θi,b,m
∀m ∈ P , ∀b = 1, .., N
(21)
∀m ∈ P , ∀b = 1, .., N
(22)
i=1
N
θi,b,m ≤ N × ∂b,m
i=1
∗
≥ tb,m
− G 1 − θi,b,m
ti,m
∀i = J, ∀b = 1, .., N, m ∈ P
(23)
∗
ti,m
≤ tb,m
+ G 1 − θi,b,m
∀i = J, ∀b = 1, .., N, m ∈ P
(24)
ti,m
≤ G × i,m
ti =
M
ti,m
+
m=1
M
∀i = J, ∀m ∈ P
∗
tb,m
≤ G × ∂b,m
τi ≥ t i − Di
μm ≥ ti,m
μm ≤ G × m
i=1
θi,b,m ≤ Bm
∀i = J
(26)
∀b = 2, .., N, ∀m ∈ P
(27)
Tm i,m
m=1
∗
∗
≥ t(b−1),m
− G 1 − ∂b,m
tb,m
N
(25)
∀b = 2, .., N, ∀m ∈ P
(28)
∀i ∈ J
(29)
∀i ∈ J, ∀m ∈ P
(30)
∀m ∈ P
(31)
∀m ∈ P , ∀b = 1, .., N
(32)
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B. C. Jos et al.
N
i,m ≤ Fm
∀m ∈ P
(33)
Em ∂b,m ≤ E L
∀m ∈ P , ∀b = 1, .., N
(34)
i=1
N M
b=1 m=1
i,m ∈ {0, 1} ; γi ,i,m ∈ {0, 1} ; γN+m,
i ∈ {0, 1} ; γ i,N+M+m ∈ {0, 1} ;
m ∈ {0, 1} ; ∂b,m ∈ {0, 1} ; θi,b,m ∈ {0, 1} ; ∀m ∈ P , ∀i ∈ J, ∀i ∈ J and
i = i; and all other variables are ≥ 0.Fm and Bm are integers.
(35)
Equation (1) minimizes the total costs linked with the tardiness of jobs, the total
fixed hiring costs of the manufacturing facilities selected, the total transportation
costs incurred while shipping the jobs processed at outsourced facilities back to the
central organization in batches, and the total machine usage costs of the facilities
selected. Constraint (2) reflects that a job can be assigned to at most only one
manufacturing facility. The feasibility of the sequence is maintained by Eqs. (3) and
(4). Constraints (5) and (6) guarantee that each manufacturing facility, if selected,
can have at most one start job as well as one end job. These jobs are assumed as
fictitious jobs and are named as (N + j) and (N + J + j) respectively. Constraints
(7) and (8) warrant that if at least one job is allocated to a manufacturing facility,
then the facility is active; and that a manufacturing facility is inactive if no job is
allocated to it. Constraints (9) and (10) ensure the allocation of fictitious jobs to a
manufacturing facility if any of the jobs are allocated to that facility. Constraint (11)
guarantees that the start time of any job is greater than or equal to the completion
time of its predecessor. Constraint (12) assigns the start time of the first job on each
manufacturing facility to zero. Equations (13) and (14) compute the completion time
of each job as the sum of the respective start time, setup time, and processing time.
The shipment of any job from a manufacturing facility is possible only after
the completion of that job at the respective facility. This condition is ensured by
constraint (15). Constraint (16) states that shipment of a job in any batch from
a manufacturing facility is possible only if the job is processed in that facility.
Constraint (17) ensures that completion time of the last job assigned to any batch
shipment from a facility can be considered as a positive integer, only if at least one
job is assigned to that batch shipment from the respective facility. ‘Compression
constraint’ (18) ensures that a positive batch-quantity shipment cannot follow a zero
batch-quantity shipment. Constraints (19) and (20) ensure that the batch shipment
from a manufacturing facility is possible only if that facility is active. Constraints
(21) and (22) ensure that a batch shipment from a manufacturing facility is active
only if jobs are assigned to that batch from the respective facility. Constraints (23)
and (24) state that the shipment time of a job from a manufacturing facility is equal
to its corresponding batch’s shipment time from the respective facility. Constraint
(25) ensures that the shipment time of any job from a facility is a positive integer
only if that job is allocated to that facility. Constraint (26) states that the arrival time
An Integrated Problem of Production Scheduling and Transportation in a Two-. . .
177
of a job at the central manufacturing facility is the sum of shipment time from the
manufacturing facility and the associated transportation time. Constraint (27) and
(28) ensure that the completion times of different batches from a facility are in the
non-decreasing order of batch numbers.
Expression (29) determines the tardiness of all jobs. Expressions (30) and
(31) define the total machine usage time of each manufacturing facility selected.
Constraints (32) and (33) maintain the batch capacity and manufacturing capacity
restrictions, respectively. Constraint (34) is used to keep the total carbon emission
from transportation within the desired upper limit. Finally, Expression (35) defines
all the binary variables and continuous variables.
5 A Numerical Example
In this section, we present a numerical example of the problem under study and
its solution using the proposed solution technique. Consider a supply chain system
with number of jobs (N) = 8 and number of machines (M) = 2, where in-house
machines Min = 1 and outsourced centers Mout = 1. This example is meant only for
the illustration purpose, and hence units of the input parameters are not specified.
Processing time of each job i on machine j (Pi, j ), due date associated with job i (Di ),
and tardiness cost/unit time (Wi ) are given in Table 1. Sequence-dependent setup
times (Si ,i,m ) on machines m = 1 and 2 are given in Tables 2 and 3, respectively.
Other input parameters are given in Table 4. The maximum capacity of a truck
from facility m (Bm ) is assumed as 3 jobs and is same for all m, and the maximum
production capacity (Fm ) at a manufacturing facility is 6 jobs and is same for all
m. The carbon emission limit is calculated as 80 using the equation EL = fr × EUB
where fr is assumed to be 0.5. The values of the different parameters are generated
randomly, and is described in section 6.
The given problem instance is solved using the proposed MILP model and using
IBM CPLEX optimization studio 12.7.1. The objective function value of the optimal
Table 1 Input parameters: processing time, due date, and tardiness cost
Job no. (i)
1
2
3
4
5
6
7
8
Processing times on facilities (Pi, m )
Pi, 1
Pi, 2
11
12
6
6
9
10
10
11
9
10
3
3
6
6
9
10
Due date (Di )
38
39
36
37
34
34
37
41
Tardiness Cost (Wi )
12
11
15
12
10
10
12
12
178
Table 2
Sequence-dependent setup
time (Si ,i,m ) on facility
m=1
Table 3
Sequence-dependent setup
time (Si ,i,m ) on facility
m=2
Table 4 Other input
parameters
B. C. Jos et al.
Preceding job (i )
0
1
2
3
4
5
6
7
8
Preceding job (i )
0
1
2
3
4
5
6
7
8
Succeeding job (i)
1 2 3 4 5
4 2 2 6 4
0 3 5 5 5
4 0 2 6 5
4 5 0 5 4
2 2 4 0 6
3 5 4 5 0
4 5 2 6 3
4 5 3 2 3
3 2 3 3 5
6
2
5
6
2
5
3
0
2
5
7
3
2
2
5
6
4
2
0
3
8
2
3
4
2
4
6
4
2
0
Succeeding job (i)
1 2 3 4 5
4 2 2 6 4
0 3 5 5 5
4 0 2 6 5
4 5 0 5 4
2 2 4 0 6
3 5 4 5 0
4 5 2 6 3
4 5 3 2 3
3 2 3 3 5
6
2
5
6
2
5
3
0
2
5
7
3
2
2
5
6
4
2
0
3
8
2
3
4
2
4
6
4
2
0
Manufacturing facility (m)
Transportation time (Tm )
Transportation cost (Cm )
Fixed cost of hiring Cm
∗
Machining cost/unit time Cm
Carbon emission/trip (Em )
1
0
0
0
2
0
2
20
60
240
8
20
solution is obtained as 1019 in a computational time of 54.45 sec. Jobs 2 and 8
are shipped from manufacturing facility 2 in a batch. The machine usage time of
facilities 1 and 2 are 63 and 20, respectively. In the optimum schedule, tardiness
corresponding to jobs 1, 2, and 5 are found to be 11, 1, and 29, respectively. All
other jobs have met their respective due dates. Allocation and the schedule of jobs
on each machine, when selected, are shown in the following Gantt chart (Fig. 3).
6 Computational Experimentation
We designed our experiments to generate problem sets randomly as suggested by
earlier researchers, especially by Potts and Van Wassenhove (1985), Liaw et al.
(2003), Chen (2009) and Oğuz et al. (2010). In this section, we first explain how
An Integrated Problem of Production Scheduling and Transportation in a Two-. . .
179
Indicates the setup process of a job
Facilities\Machines
i
2
8
1
3
5
Indicates the processing of Job i
2
6
10
15
7
20
4
25
30
1
35
40
45
5
50
55
60
Time
Fig. 3 Gantt chart representation of the optimal solution
to generate the problem sets, and then demonstrate the results of the computational
analysis obtained using the proposed solution technique.
The values of the different parameters for the integrated logistics and job
scheduling problem in a supply chain with non-identical parallel manufacturing
facilities are generated as given below. For each order i ∈ J, the processing time
Pi, m on each facility m ∈ P is an integer generated uniformly in the interval [1,
20]. The sequence-dependent setup times Si ,i,m and initial setup times S0, i, m are
also integers generated uniformly in the interval [1, 10]. Triangular inequality is
maintained while generating setup times. We assume that Pi, m depends on the
machining facility at a manufacturing center and a machine performance factor fm
determines the relative performance of the facilities, thereby the processing time of
a job. fm also influences the sequence-dependent setup times at each manufacturing
facility (Chen 2009). We consider the machine performance factor fm as a uniform
random number in the interval [2/3, 3/2]. The following equations are used to
incorporate the machine performance factor while generating the processing times
and setup times to ensure that the parameter values are within the interval
specified
3
earlier: Pi, m = fm × Pi, v and Si,j,m = fm × Si ,i,v where Pi,v = U 2 × 1, 32 × 20
and Si ,i,v = U 32 × 1, 32 × 10 . Here, v denotes a fictitious machine, Pi, v denotes
the processing time of job i on machine v, and Si ,i,v denotes the appropriate setup
times on the machine v.
The due date (Di ) is set to maxi ,m Si ,i,m + max slack, maxm Pi,m +
avgm (Tm ) , where slack is generated uniformly in the interval Pa [1 − Π − R/2
M
2
1 − Π + R/2] where Pa = N
i=1
m=1 Pi,m /M (Liaw et al. 2003). The job due-
180
B. C. Jos et al.
date is designed to avoid tardiness if a given job is assigned as the first job on any
facility without the consideration of transportation time. The parameter Π is called
tardiness factor and R is called due-date range, and both parameters are chosen from
the values 0.3 and 0.7 (Potts and Van Wassenhove 1985).
For each facility m available for
the given jobs (i.e., m ∈ P),
processing
the
∗ , the fixed hiring cost of a facility C ,
machine usage cost per unit time Cm
m
the transportation time necessary for jobs to ship from the outsourced center to
the central organization (Tm ) and the associated cost for transportation (Cm ) are
generated as described. We also assume that manufacturing facilities with advanced
processing techniques can process the jobs in lesser time, but with a higher per
unit machining cost and holding cost. However, machine usage costs of in-house
facilities are assumed to be the least, irrespective of the machine performance.
The transportation time, transportation cost, and the fixed holding cost are
assumed to be zero for the in-house machines. The usage cost of in-house machine
∗ ) is an integer generated uniformly in the interval [1, 5]. The parameter values
(Cin
associated with the outsourced facilities are integers and are calculated as follows.
Transportation time (Tm ) to ship the finished jobs to the central organization is
generated as a uniform random number in the interval [1, 20], corresponding
transportation cost (Cm ) is calculated as Ω × Tm and the usage cost of outsourced
∗ ) is calculated as C ∗ × ∗ . For a specific problem instance, Ω and
facility (Cout
in
fm
Ω ∗ are constants and are generated randomly in the interval [1, 3] and [2, 5],
respectively. This ensures the production cost to be always higher at the outsourced
centers than the in-house center, and also in the increasing order
of the machine
∗ is a constant for
performance factor. Note that the usage cost per unit time Cm
a facility and is independent of the jobs. Fixed cost of outsourced facilities is
= 30 × C ∗ (30 is based on the sum of the upper bounds for
calculated as Cm
m
processing time
and
setup
time).
Tardiness penalty for job i (Wi ) is calculated as
∗
where i is a uniform random number in the interval [1, 2]
i × maxm Cm
associated with each job i. Tardiness penalty increases with the higher value of i .
Hence more jobs will be assigned to outsourced machines, and also more outsourced
facilities will be selected. Carbon emissions per shipment from facility m (Em ) is
calculated as r × Tm × Vavg , where r is the carbon index parameter (the average
amount of carbon emissions per unit distance) for a truck and Vavg is the average
speed of a truck (Yu et al. 2016).
To analyze the effectiveness of the proposed solution technique, problem
instances are generated with three different job sizes: J∈ {8, 10 and 12}. In all
these instances, we consider parallel manufacturing facilities; M = 3 with two
levels of in-house facilities (0 and 1). Three problem instances are generated for
each combination of tardiness factor (Π) and due date range (R) chosen from the
values 0.3 and 0.7. Hence, by considering three sets of jobs (J= 8, 10 and 12) in
conjunction with the two levels of Π and R (each: 0.3 and 0.7), and two levels of
in-house facilities (0 and 1), this study generates a total of 72 problem instances.
The proposed MILP model is tested using the commercial software CPLEX
12.7.1, and on a system with 64 bit Intel i5 3.2 GHz Processor and 8.0 GB of RAM.
A time restriction of 7200 seconds is enforced while executing the MILP model on
An Integrated Problem of Production Scheduling and Transportation in a Two-. . .
181
CPLEX. For each problem instance, we observe the objective function value and the
execution time required for the solver to converge optimally under the given system
environment. If the problem instance has not been converged to optimum within the
stipulated time, we observe the % optimality gap reported by the solver.
Computational analysis of the proposed MILP model is reported in three
dimensions, (i) the average execution time taken to solve the test instances optimally
within the given time limit of 7200 seconds, (ii) the number of problem instances
solved to optimality within the given time limit in a set of 3 problem instances with
similar parameters, and (iii) the average optimality gap (%) reported by the solver
during termination. Results are compiled and given in Table 5.
An analysis of the experimental results given in this section shows the effectiveness of the proposed MILP model. In small problem instances, the proposed model
could give optimal solutions within a short time. When the job size is increased to
12, the model could not converge to an optimum value within the stipulated time
Table 5 Computational analysis
N×M
8×3
Min
0
1
10 × 3
0
1
12 × 3
0
1
Π ×R
0.3 × 0.3
0.3 × 0.7
0.7 × 0.3
0.7 × 0.7
0.3 × 0.3
0.3 × 0.7
0.7 × 0.3
0.7 × 0.7
0.3 × 0.3
0.3 × 0.7
0.7 × 0.3
0.7 × 0.7
0.3 × 0.3
0.3 × 0.7
0.7 × 0.3
0.7 × 0.7
0.3 × 0.3
0.3 × 0.7
0.7 × 0.3
0.7 × 0.7
0.3 × 0.3
0.3 × 0.7
0.7 × 0.3
0.7 × 0.7
Average time (s)a
182
80
140
176
17
32
145
214
–
5534
5307
5555
–
564
5422
3598
–
–
–
–
–
–
–
–
Number of instances
solved to optimality
3
3
3
3
3
3
3
3
0
1
1
1
0
3
1
1
0
0
0
0
0
0
0
0
Average optimality
gap (%)
0
0
0
0
0
0
0
0
26.3
11.9
29.3
20.4
30.5
0
12.3
15.8
50.9
48.2
64.5
68.8
69.6
61.4
65.4
72.7
Character ‘-’ indicates that none of the problem instances reached optimality within 7200 s for
computing average CPU time (i.e., execution time for all instances is above 7200 s)
182
B. C. Jos et al.
results in optimality gaps. In the computational analysis, it is observed that the
presence of in-house machines assigns more jobs to such machines resulting in a
reduction in total transportation cost as well as total carbon emissions. Larger due
dates and more jobs can be assigned to in-house machines because jobs assigned to
in-house machines can meet the due dates easily due to the absence of additional
transportation time. A lower value of the ‘carbon emission limit’ will also assign
more jobs to the in-house machines. However, such an allocation will account for a
greater number of tardy jobs. The due dates depend on the processing times, setup
times, tardiness factor, range factor, and transportation times generated randomly.
Hence, in our experimentation, we observe that a variation in a single parameter
alone cannot make any significant difference in computational time. The more the
tardiness penalty associated with jobs, the more facilities will be selected, and more
jobs will be assigned to outsourcing facilities.
7 Summary
Scheduling theory assumes great significance in the supply chain planning activities
of an organization involved with in-house and outsourced manufacturing facilities.
In this chapter, we have studied an integrated problem of logistics and scheduling
of jobs with options of outsourcing facilities and additional consideration of
transportation decisions related to transportation cost, transportation time, and
carbon emissions.
The present study has integrated three different issues in a supply chain: the
selection of subcontractors, the scheduling of jobs, and the scheduling of logistics.
The main contribution of the study is the proposed solution methodology that
integrates these three different issues which have not been considered together in
the literature so far. In addition, this study considers ‘carbon emission aspects’
associated with the shipment of finished jobs that is necessary for making the supply
chain eco-friendly.
In this supply chain system, we need to choose a set of subcontractors (also called
outsourced manufacturing facilities) from the available subcontractors to process a
part of its orders/jobs, in addition to the manufacturing facilities located in-house, to
maximize the supply chain profitability while considering carbon emissions. All the
completed jobs at the outsourced centers need to be transported back to the central
manufacturing firm. The orders/jobs to be processed are having different processing
times and due dates. We assume that the manufacturing facilities, both outsourced
and located in-house, can be non-identical and are working in parallel.
We have proposed a MILP model capable of giving optimum solutions for small
problem instances (up to job size 10) within a reasonable time. Precedence-based
binary variables are used in the proposed MILP formulation to denote the sequence
of jobs in a manufacturing facility. A numerical example and its solution have been
presented to describe the problem scenario. Computational analysis with sample
problem instances is conducted to test the robustness of the proposed solution
An Integrated Problem of Production Scheduling and Transportation in a Two-. . .
183
methodology. The computational time required to solve the problem instances to
optimality is found to be more for large-sized problem instances.
The proposed model is useful for organizations with make-to-order policies. The
previous scheduling models mainly optimize production-related costs. They ignore
the possibility of reducing the delay in product delivery (tardiness of jobs). The
proposed model not only optimizes the production-related costs, but also helps
to reduce the tardiness of jobs and determine the related transportation decisions
and emission levels in a supply chain. Our MILP model is flexible enough to
handle scenarios where the central manufacturing firm may or may not have inhouse production facilities. Hence the model can also be implemented into a virtual
enterprise system as described by Ruiz-Torres et al. (2008).
Future studies can include the development of heuristics and meta-heuristics for
this integrated problem of logistics and job scheduling with outsourcing options.
Further, the research can also include the development of solution methodologies to
this supply chain problem with the additional consideration of stepwise transportation cost. The optimal solutions obtained for small-sized problems and the lower
bounds obtained for large-sized problems using the proposed solution methodology
can be used in the future to evaluate the performance of heuristics, which might be
developed for similar problems under study.
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A Simulation-Based Evaluation of Drone
Integrated Delivery Strategies for
Improving Pharmaceutical Service
Alexander Jackson and Sharan Srinivas
1 Introduction
Over the past decade, the market has seen increased growth owing to direct-toconsumer (D2C) deliveries; more specifically, e-commerce has grown between 7
and 10 percent in developed markets, and 300% in developing ones (Dayarian
et al. 2020). Shopping in e-commerce is primarily focused on speed, flexibility,
security, and cost of delivery. Notably, most online shoppers are focused on the
speed of delivery while remaining price sensitive. It is estimated that around 50%
of U.S adults are more likely to purchase if same-day delivery is offered, and
40% expect the option to be available (Epps and Jordan 2015). However, 88% of
consumers prefer free shipping over faster shipping (Deloitte 2018). As consumers
balance their demand for cheap and quick delivery, retailers must bear most of
the costs of delivery. This has caused retailers to search for cheap alternatives to
provide a speedy delivery service (Dayarian et al. 2020). Furthermore, this sort
of environment provides a near perfect opportunity for drone applications that can
serve the commercial needs of retailers and consumers.
Traditional delivery systems that employ trucks are subject to various factors
that hurt consumers and limit their effectiveness. Trucks are limited by a region’s
A. Jackson ()
Department of Industrial and Manufacturing Systems Engineering, University of Missouri,
Columbia, MO, USA
e-mail: atjb47@mail.missouri.edu
S. Srinivas
Department of Industrial and Manufacturing Systems Engineering, University of Missouri,
Columbia, MO, USA
Department of Marketing, Trulaske College of Business, University of Missouri, Columbia, MO,
USA
e-mail: srinivassh@missouri.edu
© Springer Nature Switzerland AG 2021
S. Srinivas et al. (eds.), Supply Chain Management in Manufacturing and Service
Systems, International Series in Operations Research & Management Science 304,
https://doi.org/10.1007/978-3-030-69265-0_7
185
186
Table 1 Summary of drone
specifications
A. Jackson et al.
Drone company
Matternet
DHL Parcel
Zipline
Payload (kg)
2
4
1.75
Range (km)
10
65
80
Speed (km/h)
40
130
100
infrastructure as their only way to deliver their products is by road. This increases
delivery times as there is often traffic or congestion blocking the roads. Secondly,
trucks require a high investment cost to purchase the vehicles as a typical delivery
truck costs $50,000 (Hawes 2016). Thirdly, trucks are burdened by high maintenance costs for operation as the average cost of operating a truck is $0.08 cents per
mile (Barnes and Langworthy 2004); Furthermore, they require hundreds of dollars
in terms of fuel costs. Lastly, trucks also have high labor costs associated with them
as each vehicle requires a human operator. UPS drivers are paid up to $25 an hour
(Welch 2015), which translates to $52,000 per year. All these costs can be saved by
considering alternate delivery methods, especially drones.
Drones made by companies such as Matternet are unmanned and batterypowered flying vehicles that use multiple rotors, but drones made by DHL Parcel
and Zipline, while unmanned and battery-powered, are fixed winged and designed
to be like small planes. Their sizes are typically small but can vary depending
on their type and use. Recently, drones have been typically used for surveillance,
photography, entertainment, civilian, and military purposes. Innovations in drone
hardware, software, and networks have allowed for the continued exploration of
drone utilization. Furthermore, the different payloads, range, and speeds of different
types of drones currently in use, as shown in Table 1, adapted from Scott and Scott
(2017), Edenhofer (2018), Zipline (2020), have allowed these advances to serve
market demand.
Drones have the potential to lower labor costs, decrease delivery times, and
reduce maintenance costs as, in general, they are less expensive to maintain and can
operate autonomously (Dorling et al. 2016). Furthermore, drones are not limited to
traditional infrastructure such as roads; hence, face less obstacles in their delivery
networks. This allows for a speedy and cost-effective way for goods to be delivered.
Although commercial delivery with drones has many uses, commercial utilization
is not the only service that drones can provide to society.
Medication delivery for healthcare can greatly benefit from the implementation
of drone technology. Especially during emergencies, drones can deliver vital
supplies such as food, water, and medications to disaster areas by flying over
damaged infrastructure (Dorling et al. 2016). The timely delivery of medications,
blood, and vaccines is critical in healthcare, and some innovative organizations
have begun to use drones for healthcare services. Matternet is one such company
that uses autonomous drones to deliver medical supplies (Scott and Scott 2017).
Although emergencies are a viable application for drones, they do not occur enough
to warrant the quick implementation of drones. Pharmacy prescriptions, on the other
hand, offer a unique opportunity for possible drone delivery as quick delivery can
A Simulation-Based Evaluation of Drone Integrated Delivery Strategies for. . .
187
better serve the community, and 47% of consumers are interested in a drone-based
delivery service (ReportLinker 2016). The most significant challenge facing drone
distribution networks is the optimization of different drone delivery strategies. Every
region has different variables that can affect the strategy implementation for drone
deliveries such as infrastructure, urban density, and terrain. However, one strategy
utilized by Matternet is the “truck-drone tandem” delivery system.
A truck-drone tandem delivery system focuses on the drone and delivery truck
working together to deliver their goods. Since the drone has a limited battery life
and range, the delivery truck enables the drone to be launched from the truck at
locations from where the drones can effectively complete their deliveries. This
method allows for long-distance deliveries as it relies on the truck for the initial
leg of the delivery, and only allows the drone to take off from the truck for the
“last-mile” of the delivery. Furthermore, this method allows for more packages to
be delivered farther away from the distribution centers as the truck can hold multiple
packages. However, many issues also arise from the tandem method. The initial leg
of the trip is still subjugated to all the problems of a traditional delivery method
such as poor infrastructure and traffic, high maintenance and labor costs, and high
delivery times. Therefore, the tandem method may be optimal in certain scenarios,
but other methods need to be considered to have the most effective delivery system
with regards to delivery time and cost.
A drone delivery method that solely utilizes drones for the delivery of goods can
also be simulated. The Drone-Only method bypasses the limitations of the truck
entirely; therefore, it can be more optimal in certain regions. A Drone-Only method
will eliminate the limitation of infrastructure, long delivery times, and reduce the
capital requirements, maintenance, and labor costs associated with trucks. However,
a greater number of drones will be required to serve market demand, and the delivery
distances will be restricted by the drone’s limited battery life and range.
This paper seeks to determine the most optimal drone delivery method for
the delivery of medical prescription drugs with regards to delivery time and
cost. A discrete-event simulation is utilized for a variety of scenarios, and the
different delivery methods are compared. Furthermore, the optimal number of
drones required for different areas are determined, and a cost-benefit analysis is
performed against traditional delivery trucks. This will provide the information
needed for municipalities to determine the validity of a drone integrated delivery
system.
2 Literature Review
Research has been conducted on multiple fronts for the implementation and application of drone technology for delivery services. Drone routing studies such as Rabta
et al. (2018), Ha et al. (2018) focus on developing mathematical models to explore
the delivery networks where drones work in tandem with trucks. While they focus
on different aspects of the Truck-Tandem strategy, they also build upon research
that has not been conducted before. Furthermore, Rabta et al. (2018) focuses on the
188
A. Jackson et al.
application of drone delivery in healthcare. The chapter discusses different drone
technologies that various companies have developed and also compares them with
each other. Furthermore, the chapter also discusses a Truck-Tandem strategy where
a truck will deliver to a “drone nest”, from where the drones will perform the lastmile of the delivery. The goal is to minimize the time it takes for the truck and
the drone to complete their operation. Next, Dayarian et al. (2020) introduces an
innovative technique of having a drone to re-supply a delivery truck while making
a delivery. This system proposes a drone to land on a truck’s roof to re-supply
it during operations; therefore, eliminating the need for the truck to return to the
depot to gather more items to deliver. Thirdly, Murray and Chu (2015) discusses the
benefits of utilizing drones to deliver relief items in disaster-affected areas. Drones
avoid poor or non-existent infrastructure to deliver life-saving items to those in need.
The paper focuses on an optimization model to deliver multiple light weight items
in disaster areas and recharge stations to extend the operating range of the drones.
Furthermore, Hong et al. (2017) introduces an optimization model that focuses on
urban environments while also implementing recharging stations in the cities. Since
drones are not permitted to fly over tall buildings or skyscrapers, route planning is
essential for the operational efficiency of the drones. As the drones weave between
buildings, their operational range reduces due to the extended distances needed to
travel. However, many research articles do not discuss energy consumption within
their articles. Research such as Dorling et al. (2016) fills in the gap by discussing
vehicle routing problems with regards to energy consumption and hidden costs.
They conclude that optimizing battery weight is an important variable to consider
when evaluating optimal drone strategies. This strategy is utilized to supplement
other cost and delivery time focused research. Lastly, Welch (2015) discusses the
application of drone technology to be used for Amazon. A cost-benefit analysis
is conducted on Amazon Prime Air to determine if drone delivery is more costeffective than traditional truck delivery. The paper notes current FAA regulations
against commercial drone use and concludes that drone delivery is more costeffective than traditional truck.
3 Problem Statement
Prescription drugs are life-saving medications that treat people with a variety
of diseases, and, currently, prescriptions filling requires customers to come to
the pharmacy to pick up the drugs. However, different regions have different
infrastructure and variables that affect commute times for customers and can lead to
customer dissatisfaction. While customer dissatisfaction may not be blamed on the
pharmacies themselves, a pharmacy offering prescription drug delivery may attract
new customers or increase customer loyalty. Belfast, Northern Ireland of the United
Kingdom, was named UK’s worst city for traffic congestion in 2016 as citizens
spend an extra 87% of their actual travel time stuck in traffic (Index, TomTom Traffic
2016). Therefore, Belfast provides a unique opportunity for pharmacies to service
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their customers with prescription drug deliveries. On average, Northern Ireland
pharmacies fill 315 prescriptions a day (United Kingdom 2019), and different
delivery strategies are studied to explore possible methods for drug deliveries to
enhance customer satisfaction.
4 Methodology
4.1 System Description
A discrete-event simulation model is developed utilizing data gathered from pharmacies to accurately compare different methods for delivering prescription drugs.
The model builds upon previous research and focuses on traditional Truck-Only,
Truck-Tandem, and Drone-Only delivery methods.
This model considers different scenarios in which customers do not travel to their
local pharmacy, but the pharmacies provide delivery services for all their customers.
However, healthcare is considered confidential and adheres to strict privacy laws.
As such, patient data is closely guarded, and patient addresses are not provided
by any pharmacy. Thus, certain assumptions are made. The prescription drop-off
locations are randomized, but it is also assumed that the demand locations are
around the pharmacy. Furthermore, prescription demand data is gathered elsewhere.
The United Kingdom publishes pharmaceutical data on an open government license
for public sector information. As such, pharmaceutical data of Belfast, Northern
Ireland and United Kingdom is utilized to model the demand. Pharmacies operate
on a 13-h workday, and the delivery vehicles are sent out 4 times a day as opposed
to a continuous operation. The 13-h workday is similar to U.S. pharmacy operating
hours, and the limited delivery operations ensure there is a batch of prescriptions
that can be delivered at once. The pharmacies were found to fill, on average, 315
prescriptions a day. Therefore, the model demand follows a Poisson distribution
with an arrival rate of 1/λ with λ = 315 prescriptions.
Secondly, as the model compares different delivery methods, it is assumed that
all trucks and drones are the same types with similar wear and tear. The drones are
autonomous and do not require a human operator. Furthermore, the model does not
consider battery or weight constraints on neither the truck nor the drone. However,
energy, fuel, maintenance, and depreciation costs are considered for both the truck
and drone. Lastly, the package release method from the drone is not considered in
this model, and truck speed is reduced to reflect traffic on the roadways. Further,
1.5 min is given to each delivery to simulate the time it takes for the package to
leave the vehicle and reach its destination. Figure 1 illustrates the process map the
delivery vehicles must follow to deliver the prescriptions.
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Start
Prescriptions
are filled
No
Is it time for a
batched to be
released
Yes
Prescriptions
are loaded
onto vehicle
Release
drones to
deliver
Last-Mile
Vehicle travels to
demand point or
cluster center point
No
Is location
a demand
point
Yes
Deliver
Prescription
Yes
No
Does vehicle
hold more
prescriptions
Yes
No
Vehicle
returns to
pharmacy
Are there prescriptions
left to
be delivered
No
Was the previous
batch the
last batch of the day
Yes
End
Fig. 1 Process map of prescription delivery service
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4.2 Sequence of Events
4.3 Data Collection and Analysis
Pharmacy prescription demand is collected from the U.K. government (United
Kingdom 2019). The data set provides monthly prescription orders from 2014 to
2018. A sample of demand during January from each year is taken and analyzed to
provide average daily prescription orders and the growth trend shown in Fig. 2.
Secondly, speed limits of Belfast are gathered, and congestion data are used to
provide an average speed for a delivery truck in Belfast. The speed limit within the
city is around 64.4 km/h, but citizens in Belfast spend an extra 87% of their time in
traffic. Therefore, vehicles travel roughly half of the speed limit while in the city.
Drones are not encumbered by speed limits, but by their speed capabilities. Data
collected from previous research suggests that a Matternet drone can travel around
40 km/h (Scott and Scott 2017). Lastly, demand points are randomly generated
within the simulation software Simio with the constraint being the range for the
drones and the city for the trucks. Distances are calculated for the vehicles through
the Simio Software. Table 2, below, shows the different parameters utilized in the
model and can be replicated for further research.
January Daily Orders
360
350
340
330
320
310
300
290
280
270
2014
2015
2016
Daily Orders
Fig. 2 Daily orders in the month of January from 2014 to 2018
2017
2018
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Table 2 Model parameters
Name
Prescription arrivals
Drone speed
Truck speed
Drone range
Truck range
Demand point locations
Drone capacity
Work day
Batch distribution
Delivery buffer
Distribution
Poisson
Triangular
Triangular
NA
NA
NA
NA
NA
Equally distributed
NA
Parameter
λ = 315 prescriptions
(0,40,72) km/h
(0,32,72)km/h
10 km
386 km
Randomly generated
1 package
13 h
4 batches a day
1.5 min
4.4 Model Parameters
5 Discrete Event Simulation
Three scenarios are developed: Truck-Only, Truck-Tandem, and Drone-Only, to
showcase possible delivery methods that also incorporate new technology. The necessary parameters are incorporated into the scenarios, and the scenario’s operational
efficiency and effectiveness are compared with regards to batch delivery time and
cost of implementation.
The objective of the scenarios is to minimize the cost of a pharmaceutical
delivery system while remaining capable of delivering the prescription batches with
the given number of trucks, drones, and truck capacity being the decision variables.
The baseline is established by taking the number of prescriptions to be filled on the
workday and dividing it by the number of batch deliveries. A throughput constraint
is placed to ensure all the prescriptions are delivered. The truck capacity variable
is added to determine how loaded the trucks need to be to balance speed and
economy. As shown in Table 3 Barnes and Langworthy (2004), Edwards (2011),
Hawes (2016), Kim (2016), Rabta et al. (2018), Matternet: delivery drones that are
delivering now (2015), United States Energy Information Administration (2019),
United Postal Service (2017), Welch (2015), Figliozzi (2018), Eisenbach Consulting
(2020), Wei and Figliozzi (2012), Australian Taxation Office (2019), costs are
measured by capital, maintenance, labor, fuel, energy, and depreciation. Delivery
Table 3 Cost comparison
Investment
cost
Truck $50,000.00
Drone $5000.00
Maintenance
($ per km)
$0.08
$0.03
Labor
cost ($ per
hour)
$25.00
$0
Fuel cost
($ per
Gallon)
$2.69
$0
Energy
cost ($ per
kWh)
$0
$0.03
Depreciation
(% per year)
8.33%
50%
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trucks depreciate at 8.33% a year, and drones lose roughly 50% of their value each
year (Australian Taxation Office 2019), and so drones must be replaced frequently,
increasing their costs.
5.1 Verification and Validation
Multiple functions are in place to ensure that the model works properly. “Model
trace” provides the live steps the model takes in its logic processing. The trace allows
the user to follow along as the model runs its processes and can precisely pinpoint
where errors occur. Secondly, a “pause” function is built within the model to pause
the model whenever a minor error occurs. These errors only interrupt a small portion
of the model, and the model can circumvent them to continue operation. Lastly, the
model has a “stop” function that completely stops the model when a major error
occurs. These errors affect the model as a whole and typically involve flaws in the
logic processes. However, all errors can be pinpointed through the model trace, and
a solution can be implemented accurately.
Validation
The t-statistic is used to determine a 95% confidence interval. A small random
sample is taken from the pharmacy demand and is found to have a mean of 315
prescriptions a day with a standard deviation of 20.18. The 95% confidence interval
is calculated to be 291,341 prescriptions in a day. A t-test (α = 0.05) was conducted
to validate the demand arrival. The actual demand had a mean of 315 prescriptions
a day, and the model had a mean of 309 prescriptions. This resulted in a p-value of
0.674, which is greater than 0.05, thus confirming that the demand is not statistically
different. Given that pharmaceutical delivery and drone technology are relatively
new, a t-test cannot be performed on delivery times as there is no real data to
compare.
6 Alternative Scenarios
6.1 Scenario 1: Truck-Only
As shown in Fig. 3, the demand points are randomly generated through the software
within the limitations of the city. The trucks start from the pharmacy and take
the shortest path to visit every demand point before returning to the pharmacy.
Before that, the trucks wait for the prescriptions to be batched together. When a
batch is ready, the prescriptions are loaded onto a truck, and the truck departs for
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Fig. 3 Illustration of demand drop off locations for scenario 1
delivery. The truck navigates the road to its appropriate demand points. Once the
truck reaches a demand point, 1.5 min is allotted for the truck to make the delivery,
and then the truck calculates the next demand point that needs service and sets
out to make its delivery. The truck repeats the process until it has delivered all the
prescriptions and then returns to the pharmacy. After returning to the pharmacy, the
truck checks to see if more prescriptions in the batch need to be delivered; if not,
the truck waits for the next batch to be ready.
6.2 Scenario 2: Truck-Tandem
As shown in Fig. 4, the demand point clusters are randomly generated through the
software within the limitations of the city. The trucks start at the pharmacy and wait
for the prescriptions to be batched together to make their deliveries. When a batch
is ready, the truck travels to a designated cluster center where it launches the drone.
The drone makes the last-mile flight to the demand point to complete the delivery.
Once the drone takes off, the drone flies above obstacles and in a direct line to a
cluster’s demand points for quick delivery, where 1.5 min is allotted. The drone
delivers the product at the demand location and returns to the truck. After the drone
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Fig. 4 Illustration of demand point clusters for scenario 2
has returned to the truck, the truck proceeds to calculate the next cluster that it needs
to serve. The truck determines the quickest route to the next cluster center point and
travels by road to reach it. Upon reaching the center point, the drone delivery process
is repeated until all demand points in the cluster are served. The truck continues this
operation until it has delivered all its prescriptions and then returns to the pharmacy.
After the truck has returned to the pharmacy, it determines if more prescriptions
from the batch need to be delivered. If so, it will set out again to deliver the order,
but if not, it will wait until the next batch is ready.
6.3 Scenario 3: Drone-Only
As shown in Fig. 5, the demand point locations are randomly generated within the
limitations of the drone range. The drones begin at the pharmacy and fly a direct
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Fig. 5 Illustration of the demand point for scenario 3
route to each demand point location to complete the delivery before returning to the
pharmacy. Before leaving the pharmacy, the drones wait for the prescriptions to be
batched together to make their deliveries. When a batch is ready, the prescriptions
are loaded onto the drones, and the drones depart for delivery. Drones will be loaded
only with 1 prescription to be delivered to a demand point. The drone then takes off
and travels in a straight path to the demand point. Upon arrival, the drone will deliver
the prescription and then return to the pharmacy to pick up another prescription. The
process is repeated until all prescriptions are delivered in a batch.
7 Results
The parameters of the models are implemented in Simio OptQuest with a warm-up
period of 100 h. 300 simulations with 5 replications are used to achieve the results
with a 95% confidence interval. OptQuest experimental setup in Simio considered
1–15 trucks with capacities ranging from 1–80 prescriptions for scenario 1, 1–15
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Table 4 Truck only results
Truck only
Number
trucks
13
3
Number
drones
0
0
Truck
capacity
6
30
Investment
cost
$ 650,000
$ 150,000
Yearly
cost
$ 557,870
$ 364,607
Batch
delivery
time (h)
0.93
2.82
trucks with capacity ranging from 1–80 prescriptions and 1–5 drones for Scenario
2, and 1–30 drones for Scenario 3 to achieve its results. Note that the capacity of
drones is considered as 1 prescription per delivery.
7.1 Scenario 1: Truck-Only
As shown in Table 4, the delivery method consisting of 3 trucks with capacities of
30 prescriptions each is determined to be the cheapest feasible option. The batches
are delivered in 2.82 h and require an investment of $150,000. However, the cost per
year is $364,607, which includes labor, maintenance, fuel, and depreciation for the
three trucks.
7.2 Scenario 2: Truck–Tandem
As shown in Table 5, the delivery method with 1 truck, with a capacity of 79
prescriptions, equipped with 5 drones proves to be the cheapest feasible option with
a batch delivery time of 2.17 h. This would require an investment of $75,000 and
carries a yearly cost of $162,960, mostly due to the depreciation of the drones.
7.3 Scenario 3: Drone-Only
As shown in Table 6, the delivery method with 4 drones proves to be the cheapest
feasible option. The batch delivery time for 4 drones is 2.69 h and requires an
Table 5 Truck-tandem results
Truck-tandem
Number
trucks
7
1
Number
drones
35
5
Truck
capacity
12
79
Total
investment
cost
$ 525,000
$ 75,000
Yearly
cost
$ 521,848
$ 162,960
Batch
delivery
time (h)
1.32
2.17
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Table 6 Drone only results
Number of drones
Drone only 30
4
Total investment cost Yearly cost Batch delivery time (h)
$ 150,000
$ 113,558 0.39
$ 20,000
$ 49,602.5 2.69
investment of $20,000. However, yearly cost accounts for $49,602.5, mostly due
to the depreciation of the drones.
7.4 Comparable Results
As shown in Table 7, it is evident that the strategies can deliver prescriptions in a
timely manner and offer cheap feasible options. However, the most effective option
is also determined, given additional vehicles and capacity constraints. A DroneOnly strategy proves to be the most efficient. With enough drones, a pharmacy can
deliver its first batch of prescriptions within 0.39 h while maintaining low yearly
costs compared to other methods. Furthermore, the most cost-efficient method,
Drone-Only delivery with 4 drones, only accrues an investment cost of $20,000
while delivering the batch in 2.69 h. It is important to note the significant drop
in both capital and yearly costs that comes with the implementation of drones.
However, Truck-Tandem is the most effective in delivering prescription drugs given
the baseline. Using Simio OptQuest, the most effective strategy is also determined
Table 7 Comparative results
Number of
Number of
Truck
Trucks
Drones
Capacity
TruckOnly
13
0
6
Cost
$
$
650,000
557,870
$
$
364,607
0
30
150,000
$
$
7
35
12
525,000
521,848
$
$
75,000
162,960
$
$
113,558
1
5
79
DroneOnly
Cost
Yearly
3
TruckTandem
Total
Investment
0
30
0
150,000
$
$
0
4
0
20,000
49,602
Batch
Delivery
Time (hrs)
0.93
2.82
1.32
2.17
0.39
2.69
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for each scenario to illustrate the cost increase and batch delivery time reduction
with additional vehicles.
7.5 Sensitivity Analysis
The initial analysis determined the least cost option while remaining feasible and the
most effective options given additional vehicles. However, as demand grows with
time, the impact on delivery time due to demand variation needs to be determined.
Figure 6, below, shows the impact on batch delivery time for both the most effective
and low-cost option for each scenario and includes a 3.25-h limitation to ensure that
the daily orders can be completed. For the most effective option, variation below
the current average has little to no impact. However, as the demand increases, batch
delivery times for Truck-Tandem increase dramatically, and the current settings is
no longer the most effective. The option now requires an adjustment in the number
of trucks and truck capacity to become the most effective again. Furthermore, as the
demand increases, the batch delivery times for all scenarios in the low-cost option
increase steadily. However, both Truck-Only and Drone-Only become infeasible and
will require an additional truck or drone.
Lastly, as shown in Fig. 7, the impact on batch delivery times due to variation
in the number of batches a pharmacy decides to operate with is determined. For the
most effective option, an increase in the number of batches above the current setting
has little impact. However, decreasing the number of batches, vastly increases the
delivery times for both Truck-Only and Truck-Tandem. For the low-cost option,
decreasing the number of batches vastly increases the delivery time for Truck-Only
and Drone-Only, and will require additional trucks or drones to remain effective.
However, increasing the number of batches, steadily decreases the batch delivery
Low Cost Option
Batch Order Time (hrs)
Batch Order Time (hrs)
Most Effective Option
3
2.5
2
1.5
1
0.5
0
215
265
315
365
415
4
3.5
3
2.5
2
1.5
1
0.5
0
215
Daily Demand
Truck-Only
Truck-Tandem
265
315
365
415
Daily Demand
Drone-Only
Truck-Only
Truck-Tandem
Drone-Only
Fig. 6 Comparing the impact of daily demand variation on batch delivery times with the 3.25-h
limitation for the 3 scenarios. The red line represents the upper limitation of batch delivery time to
complete the daily delivery
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Low Cost Option
3
Batch Delivery Time (hrs)
Batch Delivery Time (hrs)
Most Effective Option
2.5
2
1.5
1
0.5
0
2
3
4
5
6
6
5.5
5
4.5
4
3.5
3
2.5
2
1.5
1
0.5
0
2
Truck-Only
Truck-Tandem
Drone-Only
3
4
5
6
Number of Batches
Number of Batches
Truck-Only
Truck-Tandem
Drone-Only
Fig. 7 Comparing the Impact of the number of batches on batch delivery time with 3.25-h
limitation between the 3 scenarios. The red lines illustrate the batch delivery time limitation for
each number of batches
times for all scenarios. Nevertheless, the low cost option for Truck-Only no longer
becomes feasible as the trucks are no longer able to deliver the entire batch before
the next delivery batch time.
7.6 Discussion
In this section, the different scenarios are compared with respect to their cost, ease
of implantation, performance, risk, and coverage. These comparisons are formed
into a ranking system that a pharmacy manager can utilize for the decision-making
process. As each scenario has potential benefits and problems, it is to the pharmacy
manager to decide which is best for their pharmacy.
Cost Each scenario requires costs to operate, and scenario 3 requires the least. Due
to the relatively low costs of drones, scenario 3 is the cheapest option, followed by
scenarios 2 and 1. Scenario 1 requires the most investment as trucks require a high
investment and accrue higher costs as they are used. Scenario 2 is a combination of
a truck and drone delivery system, and it serves as a middle ground between the two.
Scenario 2 requires fewer capital expenditures as its assets are shifted from trucks
and into drones. Furthermore, its costs are reduced as its labor and fuel costs are cut,
and maintenance cost is reduced.
Ease of Implementation Each scenario will require planning to implement, and
scenario 1 contains the least number of obstacles to overcome. Trucks are vehicles
that have been a part of society for decades, and a Truck-Only system will be the
easiest to implement. Scenario 1 can be implemented almost immediately; however,
scenario 3 will be the most difficult. As drone technology is relatively new, politics
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and regulations offer substantial hurdles for companies to overcome. There are
many concerns regarding drones that have led regulatory government bodies such
as the Federal Aviation Administration to prevent the implementation of drones
until further research could be completed. Scenario 2 again would serve as a middle
ground between scenario 1 and 3 as it is a mixture of the two. However, scenario
2 will run into the same regulatory hurdles as Scenario 3; therefore, its ease of
implementation is still behind Scenario 1.
Performance Each scenario can deliver batch orders promptly; however, scenario
2 is the quickest with regards to its low-cost option that remains feasible. However,
scenario 3 becomes the best performing if there are large amounts of drones.
As drones are not limited by infrastructure or traffic, they can fly over many
obstacles that would normally inhibit trucks. Therefore, scenario 3, with many
drones, provides the quickest delivery times for a pharmacy. Due to the relatively
low cost of operating drones, significantly more drones can be purchased, reducing
the delivery times. Furthermore, scenario 2 is limited by space in the truck and
cannot hold a large number of drones needed to increase its performance, but it will
remain more stable than scenario 1 as the demand grows. Lastly, scenario 1 requires
more trucks to be competitive with scenario 2.
Risk Scenario 3 contains significant amounts of risk as drones are still developing,
and the risk is exacerbated with additional drones. There is a safety risk as drones
can cause mass damage if not operated appropriately, especially around airports.
Furthermore, citizens are concerned about the risk drones pose to their privacy as
drones are capable of circumventing traditional barriers. Moreover, it also faces
a loss of property risk as armed citizens may destroy the drones they suspect of
wrongdoing, or drones could malfunction, causing a crash. Political risk is abundant
as regulatory agencies are unsure of allowing drones to operate. Lastly, operational
risk is prevalent as technology and code are prone to malfunction, and packages
may be delivered to the wrong location. However, many of these risks can be
mitigated as technology and public education improves, and regulations become
up-to-date. Scenario 1 bears the least amount of risk as trucks are a well-regulated
technology and are confined to a predetermined path with its own regulations.
However, scenario 1 bears some risk as driving conditions may be unpredictable
and human error too is a factor. Scenario 2 bears the most risk as it contains both
trucks and drones; therefore, combining the risks.
Coverage Scenario 3 covers the least amount of territory as drones are limited in
range. Due to their battery lives, drones are restricted to serve a small area around
a pharmacy and require frequent recharges. Scenario 1 and 2 operate in a more
extensive territory as the ranges are dependent on trucks. However, Scenario 2
covers the most range as it can launch a drone if a demand location is beyond the
max range of a truck.
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Table 8 Rankings (3 being best to 1 being worst)
Scenario
1: Truck-only
2: Truck-tandem
3: Drone-only
Cost
1
2
3
Ease of implementation
3
2
1
Performance
1
2
3
Risk
3
1
2
Coverage
2
3
1
7.7 Managerial Implications
The model scenarios provide insights into how a drone delivery service can be
implemented at pharmacies. However, it is up to the pharmacy managers to determine which scenario is more suitable for them. Pharmacy managers are assumed
to know what is best for their pharmacy, especially as they may have knowledge of
unknown variables not incorporated into this model. However, rankings can be given
for the manager to use to support them in decision making. As shown in Table 8, no
scenario is dominant. Each scenario has its benefits and drawbacks, and it is up to
the pharmacy manager to prioritize the rankings.
Managers prioritizing a simple fix can utilize scenario 1 and avoid scenario 3.
Scenario 1 requires the least number of obstacles to overcome, and scenario 3 will
require the most effort to implement. Furthermore, if a manager requires the least
amount of risk, scenario 1 can be utilized, and scenario 2 should be avoided. As
trucks are the simplest to implement, they contain the least amount of risk. However,
trucks bear some risk, and Scenario 2 adds that to the significant amount of risk
drones carry. If a manager wishes to cover the most amount of territory, Scenario
2 can be utilized, and scenario 3 should be avoided. The combination of truck and
drone allows Scenario 2 to cover the most territory, and the limitations of drone
range confine Scenario 3 to a small area. Lastly, if a manager has a limited budget
or wants the best performing system, Scenario 3 should be adopted, and Scenario 1
needs to be avoided. Drones perform the best within their range, and their low costs
allow multiple drones to be purchased while remaining under budget. However,
trucks require high capital expenditures and accrue high costs due to labor, fuel,
and maintenance.
8 Conclusion and Future Work
The application of drone technology to delivering pharmaceuticals can greatly
benefit customers. In cities, like Belfast, with high levels of congestion, the
introduction of drones can reduce the number of vehicles on the road by eliminating
the need for people to travel to pharmacies to collect their prescription drugs.
Furthermore, different methods for incorporating drones into delivery strategies
offer different benefits. Three different scenarios: Truck-Only, Truck-Tandem, and
Drone-Only, are researched and compared to determine the most optimal scenario
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with regards to delivery time and cost. The model suggests that a Drone-Only
system is the most effective if there are a high number of drones, and the system
still remains cheap and feasible. While each scenario can fulfill the demand, the
different scenarios offer unique benefits and drawbacks that balance each other out.
A pharmacy manager will be required to prioritize the rankings and determine which
scenario is the best fit for them.
This study has its limitation, and future work can improve the current model.
Factors considering truck routing and drone features are not considered. Truck
routing can be optimized with regards to the shortest path between demand points to
calculate the true delivery times considering truck routing strategies. Furthermore,
partnering with a pharmacy to gather real patient locations can make the model
reflect real-world data more adequately. Next, factors such as package weight can
be researched to determine how the weight will affect drone speed and battery life.
Furthermore, different batteries can also be researched to determine the interval time
between recharges.
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Pro-Active Strategies in Online Routing
Stefan Bock
In this paper, we summarize the basic ideas of recent approaches for the efficient
controlling of urgent delivery processes in real-time. As in real-world applications
the requests to be serviced the same day are usually unknown and will occur
dynamically, such delivery processes possess a considerable degree of dynamism
that has to be handled by a real-time approach. For this purpose, the transportation
plan that is already in execution is continuously adapted by applying suitable
optimization approaches according to all decisions that are not implemented. Due
to the assumed urgency of the considered requests, the aim of these approaches is to
minimize the total weighted request response times in order to minimize resulting
customer inconveniences. Through the exploitation of past request data, stochastic
knowledge about future request occurrences is derived that enables significant
reductions of the request response times if the given data possesses a certain degree
of diversity. Furthermore, empirical analyses reveal that the degree of structural
diversity enables reliable forecasts concerning the positive impact of integrating
stochastic knowledge derived from the given past request data. More recently, this
approach has been extended for identifying recurring request patterns in real time
in order to improve the quality of forecasts concerning future request arrivals. In
addition to that, it has been shown that frequent en route diversions executed during
iterative plan adaptations can be strongly limited without significantly worsening
the attained request response times. Note that an unlimited exhaustive usage of en
route diversions may cause an increase of accidents due to driver distractions.
S. Bock ()
Business Computing and Operations Research, Schumpeter School of Business and Economics,
University of Wuppertal, Wuppertal, Germany
e-mail: sbock@winfor.de
© Springer Nature Switzerland AG 2021
S. Srinivas et al. (eds.), Supply Chain Management in Manufacturing and Service
Systems, International Series in Operations Research & Management Science 304,
https://doi.org/10.1007/978-3-030-69265-0_8
205
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S. Bock
1 Introduction
This paper considers the well-known Vehicle Routing Problem (VRP). To the best
of the author’s knowledge (see also Pillac et al. (2013)), the VRP was originally
introduced as a generalization of the Traveling Salesman Problem by Dantzig
and Ramser (1959). Usually, it is defined on a graph G = (V, E, C), with V =
{v0 , v1
v1 , . . . , vn and the depot v0 ),
, . . . , vn} (the
set of all customer vertices
E = vi , vj | vi , vj ∈ V × V ∧ i = j (the set of transportation edges), and
C : E → R (the costs of using a transportation edge). For reasons of simplicity, the
cost function C is frequently defined as a matrix Ci,j (with 0 ≤ i, j ≤ n and i = j) such
that Ci,j gives the costs for traveling from vi to vj . In order to service all n customer
vertices, a fleet of K homogeneous vehicles is available. Hence, the aim of the VRP
is to find K cyclical tours that start and end at the central depot v0 while together
visiting all customer vertices at minimum total transportation costs. The literature
provides various extensions of this basic formulation (see Pillac et al. (2013) or
Ferrucci (2013), pp. 32–38). For instance, the Capacitated-VRP (CVRP) extends the
VRP by limiting the loading of each vehicle by imposing a maximum capacity while
each customer has a predetermined demand. Moreover, the urgency or availability
of some customers is mapped by integrating time windows at the customer vertices.
As a consequence, a vehicle tour cannot service a customer before the opening of
the respective window while servicing a customer after the end of the time window
is either forbidden (hard time windows) or penalized (soft time windows). Hence,
the respective variants of the VRP are denoted as the VRPTW or as the VRPSTW.
This paper considers routing problems that deal with the delivery of urgent
goods or services. Hence, material or immaterial goods (e.g., spare parts, repair
services, specific technical support, or a combination of these examples) have to be
delivered while an immediate delivery is of utmost importance to the customer. For
instance, such deliveries become necessary after a customer complaint due to an
unsuccessful first delivery or if this first delivery contains erroneous parts. In these
exemplary cases, only an instant subsequent delivery of the needed goods or services
in real time may limit the occurred customer inconvenience. Hence, in the literature
these processes are frequently denoted as the distribution of perishable goods and
the respectively considered real-world processes are named Real-Time Distribution
of Perishable Goods (RDOPG) applications (see Ferrucci (2013), pp. 1–2). As a
consequence, Ferrucci et al. (2013) propose the usage of a specifically defined
objective function that pursues the finding of tour plans ζ minimizing the customer
inconvenience z(ζ ). For each request i with a predefined weight wi , this customer
inconvenience is operationalized as a function F(ti ) of the resulting request response
time ti . This response time ti measures the time between request occurrence and
request delivery. According to the results published by Davis and Maggard (1990)
and by Kristensen et al. (1992), Ferrucci et al. (2013) model customer inconvenience
as a linear as well as a quadratic function of the resulting request response times.
The exemplary courses of both functions are sketched by Fig. 1.
Pro-Active Strategies in Online Routing
207
Fig. 1 Illustration of the two customer inconvenience functions (linear2X and quadratic) modelled
and analyzed by the approach of Ferrucci et al. (2013) (see Ferrucci (2013), p. 82)
In order to additionally map a maximum response time Rmax that is communicated in advance to the respective customers, the inconvenience is significantly
increased by Rpen after reaching this threshold. Thus, requests that are delivered
after time Rmax are denoted as late requests. Furthermore, in order to ensure that
those late requests are not delivered arbitrarily late in the linear case, the steepness
of the increase of the linear objective function is doubled after reaching the threshold
of the maximum response time Rmax . Note that this modification is not necessary
for the quadratic case since this function weights each increase of the response time
over-linearly such that late requests obtain a larger priority anyway. All in all, the
objective function measuring the total inconvenience for delivering all non-serviced
requests that constitute the set RτU is defined by:
Minimize z (ζ ) =
i∈RτU
wi · F (ti ) + R pen · Θ ti − R max ,
with (x) = 1 for x > 0 and 0 otherwise see Ferrucci et al. (2013 p. 132).
Another important issue, whose integration allows for covering various practical
applications by the Vehicle Routing Problem, deals with the restricted or delayed
availability of information. In contrast to the standardized deterministic VRP where
all information is available, static, and reliable throughout the ongoing transportation process, dynamic variants of the VRP (abbreviated as DVRP) distinguish
themselves by the fact that a considerable amount of data is not reliably given
beforehand, but becomes available or changes during the execution of the transports.
This complicates an efficient execution of the process as unexpected events like
new requests or congestions may occur and have to be handled in real time and
simultaneous to the execution of the ongoing transports. As illustrated in Fig. 2,
the service of dynamically incoming requests has to be integrated into the existing
vehicle tours that are already in execution. For this purpose, the existing and
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S. Bock
Fig. 2 Illustration of the Dynamic Vehicle Routing Problem with newly incoming requests
Fig. 3 Possible information flow within a real-time control approach (see Bock (2010), p. 734)
already executed tour plan has to be adapted. Note that the significant progress of
communication and information technologies makes it possible to control a fleet of
vehicles in real time with a centralized approach that is continuously informed about
the current situation in the network in order to handle incoming events by necessary
plan adaptions.
These adaptations are in turn communicated to the respective vehicles. The
possible information flow within a real-time control concept is depicted by Fig.
3.
The task of the central database component is to map the current situation of the
controlled transportation process. For this purpose, recent events affecting the state
of the vehicle fleet or of the transportation network are transferred by respective
services and are directly integrated into the mapped situation. As a result, a static
optimization problem is derived that defines the current state of the ongoing process
by respective parameters (e.g., current vehicle positions, vehicle states, or known
requests), whereas all changeable decisions constitute the variables of this model.
In order to handle incoming events and to ensure an efficient process execution, the
Pro-Active Strategies in Online Routing
209
real-time control has to continuously adapt the plan that is already in execution.
Note that the concurrency of plan execution and adaptation requires a sophisticated
update handling as – due to the complexity of the considered optimization problem –
plan adaptations cannot be done in zero time, but require substantial computational
time.
One main characterizing aspect of each DVRP-instance that significantly drives
the complexity of efficiently controlling the considered routing process is the
ratio of unexpectedly occurring events and the time pressure under which these
events have to be handled. Depending on the specific setting of the respectively
considered DVRP, the literature provides different so-called degrees of dynamism
(dod) measuring this aspect. According to Lund et al. (1996), the dod is originally
defined by δ = nd /ntot . In this computation, nd is the number of unexpected
dynamically occurring requests and ntot defines the total number of all requests
(static and dynamic ones) to be satisfied by the tour plan. In order to additionally
integrate the time pressure under which the dynamically incoming requests have to
be handled, Larsen (2000) extended the dod δ to the effective dod that is defined by
ria
nd
a
δe =
i=1 T ·ntot . In this calculation, ri ≤ T gives the arrival time of request
i, while T provides an upper bound of all request arrival times. If, in addition,
time windows exist, Larsen (2000) recommends applying the effective dod with
time windows δ etw instead. This measure sums up the difference between T and
the remaining response time before the closing of the respective time window ril
T − r l −r a
1
d
i
i
over all dynamically incoming requests, i.e., it holds δ et w = ntot
· ni=1
.
T
Hence, this definition measures the time pressure for handling each event by the
maximum remaining time before reaching the end of the respective time window.
2 A Reactive Real-Time Approach
This section briefly sketches a reactive real-time approach that is also used (in a
slightly modified variant) for the pro-active concept introduced later. It applies a
basic concept that has been evaluated in terms of being able to efficiently control
various dynamic, unreliable real-world processes in production and logistics (see
Gendreau et al. (1999), Ichoua et al. (2000), Bock et al. (2006), Bock (2010),
Ferrucci and Bock (2014)). Before starting the delivery process, a first transportation
plan (denoted as the first relevant plan P0r ) for servicing the initially known requests
is generated by applying a suitable optimization procedure. Subsequently, at time 0,
this first relevant plan P0r is set in execution. In what follows, we denote Pτr as the
relevant plan executed at time τ . As the Vehicle Routing Problem is an N P-hard
problem, the finding of efficient tour plans is a complex task and cannot be done
in zero or almost zero time. Hence, simultaneous to the execution of the ongoing
transportation process, the real-time control approach continuously tries to improve
all not implemented decisions of a currently stored theoretical plan Pτt . During this
improvement process, the best performing theoretical plan Pτbt is kept for a possible
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exchange with the current relevant plan. In other words, the entire execution time
of the ongoing process is used for improving all still theoretical plan decisions. Due
to the concurrency of plan execution and possible plan adaptations, the real-time
concept ensures a correct plan update by iteratively solving static snapshots of the
ongoing process within predetermined time limits. These time limits are denoted as
anticipation horizons and separate the continuous time lapse into discrete intervals
of customizable length ta .
Specifically, with the beginning of a new anticipation horizon at time τ , all
requests that have occurred and buffered during the preceding horizon are integrated
into the three stored plans, namely into the relevant plan, the theoretical plan, and
into the best theoretical plan. Subsequently, the best performing plan of these three
plans is taken as the new relevant plan. In order to ensure that the theoretical
transportation plan that is modified by the applied optimization procedure during the
considered anticipation horizon starting at time τ is applicable after the elapse of this
horizon at time τ + ta , all decisions that will be taken by the relevant plan during this
horizon are pre-simulated. Hence, after this pre-simulation, the theoretical plan only
contains decisions that will be taken after time τ + ta . Therefore, the optimization
procedure is applied to a static problem instance that maps a situation in the delivery
network that will occur in the future, namely at time τ + ta . As a consequence, with
the beginning of a new anticipation horizon, all decisions to be taken during this new
horizon become fixed and are assumed to be taken as defined by the relevant plan.
For further details of the applied plan adaptation methods, we refer to Bock (2010)
and Ferrucci (2013). Figures 4 and 5 illustrate the described update handling of a
Fig. 4 Continuously adapting the current relevant plan by iteratively solving static snapshots of
the ongoing process at the adaptation level (cf. Bock 2010)
Pro-Active Strategies in Online Routing
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Fig. 5 Continuously adapting the current relevant plan by iteratively solving static snapshots of
the ongoing process at the adaptation level (cf. Bock 2010)
sophisticated reactive real-time control approach proposed in Bock (2010). Figure 4
depicts the situation in which new requests have arrived during the last anticipation
horizon.
These pending requests are iteratively integrated into the three stored plans by
finding least cost insertion positions. As these modifications can be conducted in
almost zero time, the modified plan can be instantly set in execution. However,
as these instant modifications and the respective cost consequences are frequently
of limited quality, this plan is subsequently adapted by applying the Tabu Search
approach. Hence, as indicated by Fig. 5, these subsequent optimization processes
often attain significant improvements of the current relevant plan. Note that, as the
focus of this paper is solely on the design of the presented real-time approaches,
all details of the applied Tabu Search procedure are neglected. For these important
algorithmic details, we refer the reader to Ferrucci et al. (2013) and in particular to
Ferrucci (2013, pp. 185–203).
As Bock (2010) shows, a continuous optimization of the current relevant plan
may result in significant improvements also in comparison to an event-oriented
variant that solely conducts a plan adaptation by applying the insertion heuristic
followed by a Tabu Search algorithm for the subsequent anticipation horizon after
the occurrence of a dynamic event (e.g., the occurrence of a new request, a street
congestion or street blockage, or technical vehicle problems). In contrast to this,
Ferrucci (2013) reports that both approaches attained comparable results for the
tested dynamic instances of the Vehicle Routing Problem. These differences can
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be mainly attributed to the fact that the problem instances of the extended Pickup
and Delivery Problem considered in Bock (2010), which integrates transshipments
and external service providers, are considerably more complex. Therefore, more
substantial adaptations are frequently necessary to attain efficient process executions
and to allow for an adequate handling of the various incoming events. This makes
a continuous application of the optimization process reasonable. In contrast to this,
most of the considered dynamic VRP-instances were efficiently adapted in a single
adaptation horizon by the applied Tabu Search algorithm.
However, Ferrucci (2013) reports that the continuous optimization outperforms
the event-oriented one for larger sized instances. In order to reduce the time
effort necessary for conducting the various computational simulations, all real-time
simulations conducted by Ferrucci et al. (2013) apply solely the event-oriented
variant.
Note that the reactive real-time approach applies a so-called idle vehicle waiting
strategy. This strategy requires that each idle vehicle, i.e., a vehicle that does not
possess an assigned non-serviced request, has to wait at its current location of the
network until a new request is assigned to be serviced. As a consequence, if the
majority of requests occur regularly in specific local areas, this positioning of idle
vehicles is quite reasonable as future requests can be expected close by their current
locations. If, however, a vehicle possesses further requests in its tour (determined by
the current relevant tour plan) this vehicle starts traveling to the next request location
directly after completing the service of the preceding one.
3 Exploiting Past Request Data and Building the Stochastic
Knowledge
Empirical tests reveal that the aforementioned reactive real-time approach is able
to ensure an efficient execution of the routing processes even if a high degree of
dynamism has to be handled. This can be ascribed to the fact that by continuously
optimizing all remaining decisions of the current relevant plan in execution, this
approach is able to attain the necessary adaptability. As a consequence, increased
response times, caused by necessary instant plan adaptations conducted due to
incoming events, can be frequently mitigated due to a later finding of improved
plans (see Bock (2010), Ferrucci (2013), pp. 220–224). Specifically, Ferrucci (2013)
reports that, due to the fast improvements attained by the applied Tabu Search
procedure, its optimization process can be restricted to 10 s after each dynamic
event as, after this time limit, the applied meta heuristic is able to attain almost
optimal tour plans.
However, although these results are promising, closer analyses also reveal that
sometimes vehicles have to conduct considerable detours to service dynamically
incoming requests that are located far away while the tours of other vehicles have
visited these areas earlier. Hence, if it would be possible to forecast some future
Pro-Active Strategies in Online Routing
213
requests and integrate this information into the tour plan, response times may
be further reduced. For this purpose, Ferrucci et al. (2013) propose to exploit
available past request data in a preceding offline step that derives forecasts of
future requests. By doing so, the reactive real-time approach is turned into a
proactive one. However, in contrast to many other pro-active approaches, this
method does not assume or apply any knowledge about existing distributions or
patterns defining the dynamism of the routing processes without proving their
existence. Rather, stochastic knowledge is derived solely from past data while all
the required attributes needed for deriving the respective forecasts are checked.
3.1 Derivation of Stochastic Knowledge by Applying
a Specifically Designed Cluster Analysis
In order to derive stochastic knowledge about the occurrence of future requests, past
data is separated into segments. As illustrated in Fig. 6, each segment s represents all
incoming requests that occur in a quadratic spatial area with side length DCse during
a specific time interval of length DCte . The incoming requests of each segment are
modeled by a time-space Poisson process ps with a request arrival rate λ(ps ) that is
calculated by the average number of requests arriving in the spatial-time assignment
of segment s during the past nf days.
Ferrucci et al. (2013) report that the applied setting DCse = 2.5 km and
DCte = 1 minute leads only to low rate values λ(ps ). These low rates are frequently
not significant enough to justify the rerouting of a vehicle at a specific time to the
defined spatial area. Hence, after generating the segments, neighboring segments
are iteratively combined into so-called clusters in order to fulfill given restrictions.
Specifically, clusters are generated by the combination of segments that are located
close to each other such that the following criteria are met:
• The cluster does not exceed a predetermined maximum spatial (DCmse ) and temporal extension (DCmte ), as otherwise, its forecast information is not significant
(the occurrence area is too large).
• The sum of the rate values of the assigned segments is at least DCminλ , i.e., a
minimum total request occurrence probability is guaranteed in this cluster.
• Further quality criteria are fulfilled (see, for details, Sect. 3.2).
Fig. 6 Definition and combination of neighboring segments
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S. Bock
Clearly, one may assume that the finding of such clusters can be done by applying
one of the well-known standardized clustering approaches such as, for instance, one
k-means clustering variant (see Arthur and Vassilvitskii (2007) or Jain et al. (1999)).
However, these approaches require the predetermination of the number of clusters
to be built. Moreover, the fulfillment of the itemized quality measures cannot be
controlled in these standardized approaches. Hence, Ferrucci et al. (2013) develop
a new specifically designed clustering method that solves a defined Mixed Integer
Problem (MIP) by applying the CPLEX standard solver. In order to map the entire
set of possible clustering alternatives, all clusters that are theoretically generatable
and fulfill the defined quality criteria are built and integrated into the defined MIP.
Note that this includes overlapping and therefore mutually excluding clusters.
3.2 Ensuring Two Further Cluster Quality Criteria
After being generated, each (still theoretical) cluster ctemp is checked as to whether it
fulfills the following two quality criteria: (i) a maximum average travel time inside
the cluster that is necessary for servicing the respective requests and (ii) that the
request arrivals fulfill, at least to a customizable extent, basic requirements of a nonhomogeneous time-space Poisson process. Note that the fulfillment of the second
criterion is assumed and used throughout the approach. In what follows, some
details concerning both criteria are depicted (cf. Ferrucci (2013), pp. 175–177):
To criterion (i): As the actual location of a dynamically incoming request may differ
from the derived cluster location, travel times inside a cluster are an issue of
assessing the quality of a generated cluster. Namely, as the intention of deriving
a cluster is to pro-actively guide a vehicle to the corresponding request-likely
area at the specified time in order to reduce request response time, the travel time
inside a cluster has to be kept strongly limited. For this purpose, the connectivity
of the barycenter (derived for the requests occurring in considered cluster ctemp
during the last nf days) inside the real-world street network is checked first.
This is done by applying the Dijkstra algorithm (cf. Dijkstra (1959)) to find,
within a predetermined travel time radius DCradiusTT , the nearest location on
a road of a class possessing a maximum speed limit (i.e., a road categorized
in the street network allowing for the fastest travel). If such a location cannot
be found within the prescribed time distance, a location in this area is chosen
instead that is located on a road with maximum speed limit. In both cases, this
node is denoted as nctemp and represents the location the corresponding dummy
customer is assigned to if the respective cluster is chosen for being integrated
into the stochastic knowledge. After determining the location nctemp (i.e., the
avgT T
location that vehicles are pro-actively rerouted to) the average travel time ct emp
is computed to all past request locations that occurred in ctemp within the last
avgT T
nf days. If ct emp exceeds the predefined threshold DCmaxAvgTT , the temporary
cluster ctemp is discarded.
Pro-Active Strategies in Online Routing
215
To criterion (ii): Each generated potential cluster (that may become a dummy
request) is checked as to whether it represents basic attributes of a nonhomogeneous time-space Poisson process, at least, to a certain extent. Note
that these attributes are used for the updating of the remaining occurrence
probabilities of future requests in the generated clusters that are needed by the
applied Tabu Search approach to weight the dummy requests throughout the
ongoing computations. For this purpose, N(t) is denoted as a stochastic counting
process that measures the incoming requests in the considered cluster ctemp until
time t. According to Ross (2019), the following three criteria have to be checked
for N(t). First, it holds that N(0) = 0. As this criterion is obviously fulfilled, it
does not have to be checked for cluster ctemp . Second, for time length t, N(t) is
Poisson distributed with mean t · λ(ctemp ) as λ(ctemp ) denotes the rate parameter
of the Poisson process. Third, the numbers of incoming requests in disjoint time
periods are independent. In order to check the second criterion for a possible
cluster ctemp, Ferrucci et al. (2013) apply the Chi-Square Goodness-of-Fit test
with a customizable type I error α according to the guidelines described by
Kvam and Vidakovic (2007, p. 156). Specifically, by this test, it is analyzed
whether substantial deviations exist between the actual number of occurred
requests in the respective cluster ctemp during the past nf days and those that
would be expected by applying a Poisson distribution with the derived parameter
λ(ctemp ). For this purpose, the sample X1 , . . . , Xnf is considered as the number
of observed request arrivals in cluster ctemp during the past nf days. For the details
of this test, the reader is referred to Ferrucci (2013, pp. 175–177). The authors
applied the Chi-Square Goodness-of-Fit test for different values of α. Although
in the literature very small values of α = 0.05 are frequently proposed (cf., e.g.,
Israel (2008, p. xxii) or Sapsford and Jupp (2006, p. 217), Ferrucci (2013) reports
that, in the considered application, these quality thresholds were much too low to
guarantee the presence of Poisson process characteristics with a sufficiently high
reliability. Hence, best results were achieved by applying the threshold α = 0.4.
This value leads to an average Poisson quality in the clusters of more than 0.6 for
the generated data sets as well as for the real-world data. Ferrucci (2013) states
that although further increasing α improved this value, setting α to 0.4 guaranteed
an efficient compromise between the conflicting goals of ensuring an acceptable
minimum Poisson quality in the clusters and reducing erroneous rejections of
clusters that possess a sufficient Poisson quality. The third criterion of Poisson
processes that requires that the requests occur independently for disjoint time
intervals (cf. Ross 2019) can also be checked by using an appropriate ChiSquare Goodness-of-Fit test. However, due to the small temporal extension of a
cluster, Ferrucci et al. (2013) refrain from performing this test. This is because the
required accuracy in request arrival times is frequently not fulfilled by practical
request data sets and therefore would lead to an unfavorable rejection of clusters
(for further details, see Ferrucci (2013, pp. 175–177).
Only if both checks are successfully completed, is the considered cluster ctemp
kept for being integrated into the MIP formulation for finding a maximum number
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of non-overlapping clusters. In other words, this cluster is a candidate for generating
a later dummy request that has be integrated into some vehicle tour plan by
the reactive real-time approach. All those candidates constitute the cluster set
C. The potential dummy request that arises from the considered cluster ctemp is
characterized by the following parameters:
W eight of the dummy request : wi = P (X ≥ 1) = 1 − P Sλ(ctemp ) (0).
avgT T
Service time of the dummy request : si = R st + ct emp · λ ct emp .
As P Sλ(ctemp ) (0) provides the probability that no request will occur in ctemp , the
defined weight-parameter wi gives the probability that at least one request will occur
in ctemp. As this parameter discounts the contribution (the customer inconvenience
as a rated response time) of the request, this weight parameter gives the priority
of the dummy request within the reactive real-time control. Moreover, the derived
service time for traveling and servicing the expected number of requests in ctemp is
used for estimating the time that a vehicle is kept at the respective location of the
derived dummy request nctemp of cluster ctemp.
3.3 Selection of the Clusters
As stated above, the preceding steps have led to the constitution of a set of all
potential (not necessarily non-overlapping) clusters C that are considered for being
chosen to define additional dummy requests to be additionally integrated into the
tour plans by the reactive real-time approach. In order to compute a suitable set of
non-overlapping clusters out this set C, the following MIP is defined. In this MIP,
the binary variable xi for i ∈ C is used that is one (xi = 1) if and only if the cluster
i ∈ C is selected and zero otherwise. Furthermore, the parameter cist art T L gives the
temporal threshold at which cluster ci starts and M defines a big number dominating
all other values.
M· xi − cist art T L · xi
Maximize
i∈C
s.t. (1) ∀s ∈ SC :
i∈C
ci,s · xi ≤ 1 and (2)
xi ≤ C max.
i∈C
The above MIP pursues the maximization of the number of generated clusters.
However, if this number is identical, solutions with earlier starting clusters are
preferred. Restriction (1) ensures that chosen clusters are non-overlapping. This is
ensured by using the parameter ci, s that is one if and only if segment s ∈ SC belongs
to cluster i. Hence, if there is some overlapping in the cluster selection, there exists
a segment that is assigned to more than one chosen cluster. This is excluded by
restriction (1). Moreover, restriction (2) defines an upper bound of the number of
Pro-Active Strategies in Online Routing
217
minλ
chosen clusters. This bound is initially set to C max =
λ
/DC
(p
)
s
s∈SC
with SC defining the set of all segments belonging to the clusters of set C. Thus,
a maximum number of clusters can be obtained by the total number of expected
requests over all segments divided by the minimum number of expected requests
required for building a cluster. The bound defined in restriction (2) is used for
technical reasons as it enables the applied CPLEX solver to derive tighter bounds.
These tighter bounds enable a reduction of the solution space that has to be explored
by the conducted examination process. By solving the MIP through the application
of the CPLEX solver, the set of generated clusters is determined. Each of these
clusters defines a dummy request with the aforementioned parameter setting that is
added to the set of real requests. Therefore, it has to be integrated into the tour plans
computed and controlled by the reactive real-time approach.
4 Transforming the Reactive Real-Time Approach into
a Pro-active One
The applied reactive real-time approach is able to attain a high adaptability that
handles incoming requests in an efficient way even when a high degree of dynamism
is given. However, response times of urgent requests can be further reduced if these
requests can be reliably forecasted and this stochastic knowledge is integrated into
the tour scheduling process. For this purpose, the generated clusters allow for the
additional definition of so-called dummy requests possessing a specific parameter
setting that distinguishes them from ordinary requests. While ordinary requests have
the weight one (occurrence is deterministic), each dummy request is discounted
with its occurrence probability riw ≤ 1 (defined in Sect. 3.2). Each ordinary request
has a uniform fixed service time of 60 s, whereas the service time of a dummy
request is set to the total time necessary on average for servicing all requests that
are expected in the respective time-spatial area (see Sect. 3.2). Each dummy request
i being defined by cluster ci is integrated into the set of requests that have to be
delivered by the reactive real-time control approach. However, while the ordinary
requests require a delivery (or a pick-up) of the respective goods (or persons), the
service of a dummy request by the reactive approach has a modified intention. It
reroutes some vehicle to the defined spatial area at the specified time cist art and keeps
it there until time ciend . Both dummy request dependent parameters result from the
segments belonging to ci . Note that the integration of dummy requests requires a
special handling by the reactive real-time control approach during the execution
of the transportation process. As long as this derived interval is not reached by
the current time τ + , all aforementioned parameters of the dummy request i remain
unchanged. Specifically, if τ + ≤ cist art holds, we have wi (τ + ) = wi and si (τ + ) = si .
If, however, the end of the current anticipation horizon τ + exceeds cist art , i.e., if it
holds that τ + > cist art , the parameter values of the corresponding dummy requests
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S. Bock
change dynamically with the lapse of time. For this purpose, Ferrucci et al. (2013)
apply the following update handling for the dummy requests:
The beginning of the time window (ei ):
ei τ + = cist art τ + + ciend − cist art τ + ·
∞
1
n=1 P Sλ(ci ,τ + ) (n)· n+1
1 − P Sλ(ci ,τ + ) (0)
The idea behind this updating scheme is to compute at time τ + the expected
arrival time of the first future request if altogether n = 1, 2, . . . , ∞ additional
requests will occur in the remaining segments (i.e., assigned segments that start at
time τ + or later) of cluster ci . If n > 0 requests occur, there are n + 1 intervals
before and after these arrivals that are assumed to be equally distributed over the
1
entire remaining interval. Thus, ∞
n=1 P Sλ(ci ,τ + ) (n)· n+1 gives the proportion of
the remaining interval ciend − cist art τ + that is expected to elapse until the first
request arises. As this calculation assumes that at least one additional request will
occur, it has to be divided by the probability that no request occurs, namely, it has
to be divided by 1 − P Sλ(ci ,τ + ) (0).
By multiplying the sum of the constant service time Rst and the average travel
avgT T
time ci
with the number of requests still expected
at time τ + in the cluster ci ,
+ st
avgT T
the updated service time si is obtained. si τ = R + ci
· λ ci , τ +
As each dummy request is weighted (i.e., is discounted) with the probability that
at least one additional request will occur in the remaining segments of cluster ci
and this probability continuously changes when τ + > cist art holds, this weight wi is
updated by the formula:
wi τ + = P (X ≥ 1) = 1 − P Sλ(ci ,τ + ) (0)
A dummy customer i is finally removed from the set of requests to be serviced
when it becomes unlikely that a further request will arrive in the remaining segments
of the considered cluster, i.e., if λ(ci , τ + ) < DCλrem holds. Clearly, this time point
tcrem
can be computed in advance.
i
The integration of dummy requests requires the application of an extended
waiting strategy that is briefly described in what follows: Whenever a dummy
request i becomes the next request that has to be serviced in a vehicle tour, it
may be possible that, due to the defined time window, this service is in the far
future. In such a situation, a direct visit of dummy request i is not reasonable as
the respective vehicle would arrive much too early in a remotely located area and
therefore would not be available for other, currently more urgent requests, in central
regions. Therefore, in such a situation, the travel to the defined location of a dummy
request is delayed until the point in time cist art minus the necessary travel time to
the location nci of the dummy request i is reached. In other words, the vehicle waits
at its current location until the last point in time when an instant travel to nci allows
Pro-Active Strategies in Online Routing
219
for a timely arrival at nci . Clearly, during this waiting time, the respective vehicle is
available for servicing other requests.
5 Computational Evaluation
In order to evaluate the proposed approach, Ferrucci et al. (2013) conduct a
series of various computational experiments. The proposed approach was tested
for two different request data classes, namely, SREAL and SGEN . While the daily
instances of SREAL stem from a real world subsequent delivery process of a German
newspaper publishing company, all experiments of the request data class SGEN were
additionally generated according to the main request occurrence characteristics of
SREAL. Note that the considered subsequent delivery process is characterized by a
high dynamism as on average there are 150 request arrivals per day, while these
dynamic arrivals mainly occur during the 4 h between 7 and 11 am. Hence, the
simulation of each day conducted by Ferrucci et al. (2013) solely maps the process
of this most challenging 4 h period. At first, this section introduces the generation
process of the data class SGEN . Subsequently, the measured results of the pro-active
approach for the real world data class SREAL and for the various test sets of the
generated data class SGEN are presented and analyzed.
5.1 Generating the Instances of the Data Class SGEN
All experiments simulate the transportation process in real time on the realworld road network of the city of Dortmund (a medium-sized German city). For
this purpose, a discrete-event based simulator was developed and applied. The
algorithms were implemented in Delphi and the experiments were conducted on
Personal Computers equipped with Pentium D 2.8 GHz CPU and 2.5 GB Ram.
The considered region covers an area of 22.5 km × 20 km = 450 km2 . In order
to test different scenarios with individual capacity demands, fleet sizes of 8, 10,
and 12 were simulated. At the beginning of each experiment, an initial tour plan
was generated by applying the Tabu Search procedure for 120 seconds. This plan
assigns all known real requests and dummy requests to vehicle tours.
In what follows, we describe the main aspects of the instance generation process
that is applied by Ferrucci et al. (2013) to constitute the set SGEN . Note that this
generation is comparable to the method applied by Ichoua et al. (2006). The used
parameter values in all generated instances of SGEN are empirically derived in
preliminary tests and given in Table 1.
In order to simulate various settings with individual distributions of dynamically
incoming requests, the considered service area is divided into a set of P disjunctive
uniformly-sized quadratic subregions denoted as a1 , . . . , aP . Moreover, the time
lapse is separated into Q time slices of equal uniform length. For each defined
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S. Bock
Table 1 Values of the main parameters applied during the computational experiments
Parameter
nf
DCse × DCse
DCte
DCmse
DCtse
DCradiusTT
DCmaxAvgTT
DCminλ
DCλrem
RD
TD
Q
P
nd /ntot
Srd
Rpen
Rmax
F(Rmax )
Brief description
Number of past days used
Spatial extension of each segment
Temporal extension of each segment
Maximum spatial cluster extension in both
directions
Maximum temporal cluster extension
Maximum travel time radius within a
cluster
Maximum average travel time radius
within a cluster
Minimum occurrence probability of a
request controlled by a predetermined
minimum λ-value
Threshold for removing a request
RegionDiversity
TimeDiversity
Number of time slices
Number of subregions
Proportion of dynamically incoming
requests
Size of each request data set
Penalty value for late requests
Maximum response time
Largest inconvenience value for a non-late
request
Value/values
60 days
2.5 km × 2.5 km
1 min
2 segments
15 min
300 s
650 s
1.0, 1.2, 1.5,1.8,2.0
(i.e., probabilities are 0.6321,
0.6988, 0.7769, 0.8347, 0.8647)
0.25, 0.5 (i.e., probabilities are
0.2212, 0.3945)
0, 0.25, 0.5, 0.75, 1.0
0, 0.25, 0.5, 0.75, 1.0
5
18
0.9
30 instances
100
3600 s
1.0
subregion, a specific time-space Poisson process is applied to generate the arrival
times and locations of incoming requests. Specifically, the parameter λ(ai , tj ) gives
for subregion ai in time slice tj the respective request arrival rate. Hence, in order
to
generate incoming requests for each time interval tj , the corresponding sum λ tj =
P
i=1 λ ai , tj provides the time-dependent arrival time of the next request. The
subregion where the request occurs (i.e., is assigned to) is randomly drawn while
the probability of choosing subregion ai amounts to p(ai, tj ) = λ(ai , tj )/λ(tj ). Finally,
the position within subregion ai is randomly drawn. In order to obtain individual
streams of random numbers, extended versions of the random number generator
proposed by Park and Miller (1988) and Park et al. (1993) are applied (see, for
further details, Ferrucci (2013, pp. 211–217).
The main reason for separating the spatial area into P subregions a1 , . . . , aP
stems from the cognition that the performance of the applied pro-active elements
of the approach are substantially dependent on the occurring variances within the
request arrival patterns over the simulated days. In other words, the positive impact
of using the stochastic knowledge exploited from the request arrivals of the past days
Pro-Active Strategies in Online Routing
221
Fig. 7 Linear averaging exemplarily applied to the individual request arrival probabilities within
the subregions a1 , . . . , aP . (see Ferrucci (2013), p. 213)
depends on the extent by which the spatial assignments of dynamically incoming
requests change throughout the day. In order to be able to analyze this important
issue, the aforementioned setting allows for suitable customizations of the request
data within SGEN . For this purpose, Ferrucci et al. (2013) introduce the terms
RegionDiversity (RD) and TimeDiversity (TD) that measure the structural diversity
of the dynamically incoming requests. Specifically, TimeDiversity specifies the
variance in each defined subregion over the time lapse that is separated into Q
time slices t1 , . . . , tQ . Analogously, the RegionDiversity gives the variance between
different subregions for each time slice. In order to systematically generate different
instances with various diversity levels, the generation of SGEN starts with a setting
that possesses maximum diversity values. This is attained (and therefore defined)
by giving each subregion ai in each time slice tj an individual request arrival rate
λ(ai , tj ). This initial scenario is characterized by the parameter setting TD = 1.0 and
RD = 1.0. Further settings are generated by reducing these diversities in predefined
steps through the application of linear averaging. As illustrated in Fig. 7, in each step
of this process, the variety of the assumed request occurrence probabilities either for
a given subarea along the time lapse or for a given time slice between different
subareas is reduced by restricting the interval between maximum or minimum
deviations from the average value.
Specifically, in case of reducing RD, a time slice is kept and the variety of
the defined request occurrence probabilities of the subregions is reduced. In case
of reducing TD, the same is done for a kept subregion along the time lapse. As
shown in Fig. 7, this averaging is done such that altogether five different states
of RegionDiversity and TimeDiversity arise. These are denoted as RD/TD = 1.0,
RD/TD = 0.75, RD/TD = 0.5, RD/TD = 0.25, and RD/TD = 0 By combining
these five states, we obtain altogether 25 settings for evaluating the impact of the
existing diversity within the request occurrences on the performance of the proactive approach. However, note that the RD- and TD-measures do not define the
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S. Bock
structural diversity in a general way, but allow for a relative comparison of generated
instances. While instances of set SGEN generated with the setting RD/TD = 0 do not
possess any diversity (i.e., the dynamism does not change spatially nor with the
time lapse) a considerable dynamism can be expected from instances of set SGEN
generated with the setting RD/TD = 1.0.
As depicted in Table 1, during the experiments, the number of subregions P
was set to 18 while the number of time slices Q was set to 5. Further details of
the instance generation process executed with one of the 24 settings are given by
Ferrucci (2013, pp. 211–217).
5.2 Measured Results for the Instances of the Data Class
SREAL
The proposed reactive and the pro-active real-time approach were applied to 30
instances of the real-world data class SREAL. For comparison reasons, a Greedy
heuristic was also applied that handles dynamically incoming requests by finding a
least cost insertion position in the currently executed transportation plan. Note that
this heuristic is also applied by the sophisticated reactive real-time approach whenever new requests occurred during the preceding anticipation horizon. However,
to find further plan improvements, the reactive real-time approach subsequently
applies the Tabu Search heuristic for 10 seconds. Table 2 gives the average
improvement rates attained by the reactive real-time approach in direct comparison
with the greedy heuristic.
Comparable to the results reported by the studies of Gendreau et al. (1999),
Ichoua et al. (2000), and Bock (2010), the reactive approach substantially outperforms the greedy heuristic by between 9% (fleet of 12 vehicles) and 32.45% (fleet of
8 vehicles) for the linear objective function and by between 19% (12 vehicles) and
almost 52% (8 vehicles) for the quadratic objective function. These improvements
underline that an efficient control of such kind of dynamic processes requires the
application of a sophisticated real-time approach that is able to continuously adapt
Table 2 Improvements attained for data class SGEN by the reactive real-time control approach in
direct comparison with the greedy heuristic according to the average customer inconvenience and
the number of late requests
Objective function Linear2X
8 vehicles
10 vehicles
12 vehicles
Objective function quadratic
8 vehicles
10 vehicles
12 vehicles
Improvement vs. Greedy heuristic
32.45% / 11 vs. 175 late
14.33% / 0 vs. 9 late
9.21% / 0 vs. 0 late
Improvement vs. Greedy heuristic
51.96% / 14 vs. 158 late
30.65% / 0 vs. 5 late
19.28% / 0 vs. 1 late
Pro-Active Strategies in Online Routing
223
Table 3 The average number of clusters that are attained by applying CPLEX for solving the
MIP to optimality (see Sect. 3.1) in dependence of the tested DCminλ -values (see Ferrucci (2013,
p. 232)
Number of clusters generated in dependence of the quality threshold DCminλ
DCminλ = 1.0
DCminλ = 1.2
DCminλ = 1.5
DCminλ = 1.8
DCminλ = 2.0
67.7
44.6
24.4
13.1
9.4
the ongoing transportation plan according to the incoming dynamic events. This is
particularly true if, due to a smaller fleet size, the number of available vehicles is
strongly limited. Obviously, such adaptability is not reachable by applying a simple
least cost insertion greedy heuristic. In order to additionally apply and evaluate the
pro-active approach, stochastic knowledge has to be exploited from SREAL in an
offline step as described in Sect. 3.1. For this purpose, aside from the 30 days used
for the performance assessment, a further 60 days are selected. These exploitations
were repeated with different threshold values of DCminλ determining the minimum
request occurrence probability in a cluster. For these evaluated DCminλ -values, Table
3 gives the average number of clusters (and therefore dummy requests) generated
by applying CPLEX. It is worth mentioning that the finding of optimal solutions
of the MIP are reasonable as these solutions provide a substantially increased
number of generated clusters. Ferrucci (2013) reports that, in direct comparison
with an alternatively applied heuristic, optimal solutions of CPLEX provide between
7.7% (for DCminλ = 2.0) and 13.4% (for DCminλ = 1.0) more clusters. Note
that the number of generated non-overlapping clusters constitutes the stochastic
knowledge of a predetermined quality exploited from the given data set. Therefore,
the improvements attainable by the application of the pro-active approach solely
depend on them.
By analyzing the results attained by the pro-active real-time approach in comparison to the reactive one (see Table 4), no clear dominance can be identified. The
pro-active approach uses the stochastic knowledge efficiently only in cases of larger
fleet sizes (10 or 12 vehicles) and by applying the quadratic customer inconvenience
objective function.
In these cases, and in contrast to the linear customer inconvenience function, the
quadratic customer inconvenience function prevents larger delays of urgent requests
in favor of dummy requests that require larger detours. Moreover, due to the larger
fleet sizes, falsely forecasted requests have a milder effect as the available vehicle
capacity is not that scarce. However, the positive effects of pro-actively guiding
vehicles to uncovered areas where forecasted requests actually occur, were observed
only rarely. This can be ascribed to the fact that further analyses of the instances
of the data set SREAL reveal that these instances possess a limited diversity. For
instance, Ferrucci (2013) shows that the request arrival rates in the 72 subregions
are quite stationary in so far that the subregions obtaining the majority of incoming
requests remain quite stable during the day. Hence, as the reactive approach keeps
idle vehicles at their last customer location, scenarios are quite unlikely where
these positions are far away from future request occurrences. Due to this limited
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S. Bock
Table 4 The average improvement rates attained by the pro-active approach in comparison with
the reactive one
Results of the pro-active real-time approach in comparison with the reactive one forSREAL
Vehicles Predetermined minimum quality of stochastic knowledge (Parameter DCminλ )
1.0
1.2
1.5
1.8
2.0
Linear customer inconvenience objective function
8
−11.5% (28) ↑ −7.66% (21) ↑ −3.12% (18) ↑ −0.27% (18) ↑ 1.6% (10) ↑
10
−4.86% (22)
−1.42% (20)
−0.68% (17)
1% (16)
0.01% (16)
12
−0.67% (15)
−0.14% (16)
1.7% (13)
0.64% (11)
0.88% (11)
Quadratic customer inconvenience objective function
8
−19.91% (27) ↑ −7.52% (21) ↑ 0.21% (14) ↑
1.17% (15) ↑
1.13% (13) ↑
10
−3.99% (20)
0.7% (12)
2.76% (9)
2.79% (10)
0.71% (14)
12
6.59% (9)
7.93% (5)
6.78% (9)
7.31% (8)
6.08% (8)
Values in bold print are the results for the best choices of DCminλ . The value in brackets gives the
number of instances with worse results. Entries with “↑” indicate that the number of late requests
increased by applying the pro-active approach (see Ferrucci (2013), p. 236)
diversity in SREAL , the attained improvements of applying the pro-active approach
are strongly limited. In order to analyze this aspect in more detail, the data class
SGEN was additionally considered.
5.3 Measured Results for the Instances of the Data Class SGEN
A first indicator of the substantial impact of a varying diversity in the considered
data set on the resulting performance of the pro-active approach is provided by Table
5. This table gives the number of generated clusters for 5 of the 25 generated settings
of RegionDiversity and TimeDiversity combined with five values of DCminλ . Note
that these clusters represent the stochastic knowledge that can be exploited form
the past data in order to extend the reactive real-time approach. Specifically, as
indicated by Table 5, for each tested DCminλ value, it can be observed that the
number of exploitable clusters significantly increases for data sets that possess a
larger diversity.
Table 5 Number of
generated dummy requests in
dependence of the level of
generated diversity and the
required occurrence
probability DCminλ (see
Ferrucci et al. (2013))
Diversity level
RD
TD
0
0
0.25 0.25
0.5
0.5
0.75 0.75
1
1
DCminλ
1.0 1.2
2
0
17
3
36 20
54 38
80 65
1.5
0
0
7
24
36
1.8
0
0
0
15
35
2.0
0
0
0
11
29
Pro-Active Strategies in Online Routing
225
Fig. 8 The attained improvement rates of the pro-active approach for the linear customer
inconvenience objective function and a fleet size of 10 vehicles
Fig. 9 The attained improvement rates of the pro-active approach for the quadratic customer
inconvenience objective function and a fleet size of 10 vehicles
This can be ascribed to the fact that the increased time and spatial variety in the
data allows for the finding of clusters that fulfill the required quality issues. Hence,
if, for instance, the maximum values DCminλ = 1.8 or DCminλ = 2.0 are applied, no
cluster is found for the settings of lower diversity RD = TD = 0, RD = TD = 0.25,
and RD = TD = 0.5. In contrast to this, clusters of this higher quality are only found
for the diversity settings RD = TD = 0.75 and RD = TD = 1. In accordance with
these results are the measured improvement rates attained by the pro-active real-time
approach and highlighted by Figs. 8 and 9 for the two simulated objective functions,
respectively. These two figures indicate that the pro-active approach outperforms its
reactive counterpart for all 30 instances of SGEN if the full diversity (generated by
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S. Bock
the setting RD = TD = 1) is given. Specifically, an average improvement of 22.95%
for the linear customer inconvenience function and of 39.33% for the quadratic
customer inconvenience function can be observed. These improvements are also
quite stable and significant for the diversity settings where both parameters possess
a value of at least 0.75. Note that while Figs. 8 and 9 provide only the results
attained by using a fleet size of 10 vehicles, comparable results are attained for 8
or 12 vehicles (see Ferrucci et al. (2013), p. 138). However, it is worth mentioning
that a reduction of the available fleet size reduces the attained improvement rates
of the pro-active approach, whereas an increase of the available vehicles leads to
even higher improvements. Specifically, for the setting RD = TD = 1 with the
highest diversity, the attained improvement rates with 8 vehicles are 19.51% in the
linear case and 29.86% for the quadratic customer inconvenience function. But,
if 12 vehicles are available, these improvement rates increase to 26.72% in the
linear case and for the quadratic case even to 47.24%. The observed impact of the
size of the fleet can be explained by the fact that in case of scarce resources the
negative consequences caused by a falsely rerouted vehicle are more substantial.
Consequently, it is reasonable to require larger DCminλ -values for these settings.
Conversely, if a larger fleet size is available, it is promising to reduce the minimum
cluster quality (i.e., by defining smaller DCminλ -values) to some extent.
Hence, owing to the measured results, it can be concluded that there are two
main criteria for a successful integration of the derived stochastic knowledge into
the reactive real-time approach (cf. Ferrucci et al. (2013), p. 137). The first criterion
is a sufficient diversity in the request data. As indicated by the measured results,
this diversity enables the finding of a significant number of clusters of a minimum
required quality. Hence, the presence of a sufficiently high level of diversity allows
for the generation of reliable stochastic knowledge about future request occurrences.
Moreover, due to the variety that is significant for highly diversified request data,
this knowledge has a substantial value for the real-time distribution process.
This results from the fact that the given variety of the request arrivals reduces
the efficiency of the vehicle positioning strategy applied by the reactive approach.
When idle vehicles wait at the location of the last service, a large variety within
the occurrences of future requests requires that these vehicles have to be frequently
guided to areas that are located at a substantial distance. This underlines the value of
possessing reliable knowledge about these locations where a future service is needed
at the beginning of the process. The second criterion that substantially influences the
efficiency of replacing the reactive approach by the pro-active one is the available
fleet size. As mentioned above, larger fleet sizes are best handled by applying
smaller DCminλ -values as the negative impacts of incorrectly forecasted dummy
requests diminish with a more relaxed capacity situation. Conversely, for smaller
fleet sizes, it is reasonable to require a larger minimum cluster quality. Therefore,
depending on the size of the available fleet size, a suitable value for the parameter
DCminλ has to be derived.
One further significant finding of the study of Ferrucci et al. (2013) is the
definition and evaluation of the degree of structural diversity (the dosd) as a
general offline measure. It allows for reliable estimations of the practicability of
Pro-Active Strategies in Online Routing
227
Fig. 10 Illustration of the computation of the degree of structural diversity (dosd)
integrating stochastic knowledge into the reactive real-time control in order to
reduce the resulting request response times. The basic idea of this measure is to
derive a normalized value that reveals the movement of the spatial barycenter of
the incoming requests over time. For this purpose, the daily time lapse is divided
into T time periods of m minutes each. For each time period t ∈ {1, . . . , T}, the
y
spatial barycenter of nt incoming requests is computed as a tuple btx , bt ∈ Gx,y
of geographical positions according to the x- and y-axis with the domain of possible
positions G = {(g1 , g2 ) 0 ≤ g1 ≤ x ∧ 0 ≤ g2 ≤ y }. Hence, the dosd measure sums
up the distances between the resulting barycenters over all time periods (see Fig.
10).
Each distance is normalized by dividing it by the trivial upper bound value
maxDist =
x 2 + y 2 . Consequently, we obtain the following mathematical
definition of the dosd that is illustrated in Fig. 10:
dosd =
T −1
t =1 nt +1 ·
!
2 y
y 2
btx+1 − btx + bt +1 − bt
maxDist·
T −1
t =1 nt +1
Note that this definition omits the movement
position of the barycenter
to the
y
of the first time period, i.e., the movement to b1x , b1 . This is done as the initial
position would be the central depot where all vehicle tours start from. Therefore,
this first time period is used to obtain instance-dependent vehicle positions. But,
as the number of requests occurring during the first period n1 is kept small in
the considered experiments, the omission of the first movement does not have a
significant influence on the computed dosd-values (for details, see Ferrucci (2013),
pp. 249–254). Ferrucci et al. (2013) evaluate the practicability of the proposed
dosd by analyzing the attained improvements rates for all conducted experiments
(possessing a comparable number of serviced requests) depending on the dosd-
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S. Bock
value calculated for the respectively given request data set. In order to keep the
numbers of serviced requests per vehicle comparable, these analyses were generated
for different sizes of the used vehicle fleets. In order to assess best or almost best
results for all experiments, the highest improvement rates over all tested parameter
settings (i.e., the most suitable value for DCminλ ) were taken as the respective result.
In order to derive the correlation between the attained improvement rates and
the dosd of the request data, Ferrucci et al. (2013) apply six linear regression
models for each of the applied inconvenience functions. By applying the Pearson
product-moment correlation coefficient r according to the description given by
Rodgers and Nicewander (1988), convincing values between 0.9161 and 0.9647
were attained depending on the fleet sizes or assumed inconvenience functions
(linear or quadratic). This proves a strong correlation between the dosd and the
reduction of the resulting response time attainable by using stochastic knowledge
exploited from the available request data. Ferrucci et al. (2013) therefore conclude
that the diversity in a request data set with a dosd ≥ 0.6 enables the proposed
pro-active approach to attain considerable reductions of the request response time.
Clearly, these conclusions also depend on the applied exploitation methods (applied
for finding the clusters) and on the definition of the dosd. But, as the reactive
approach keeps idle vehicles at the location of their last service, a successful
use of pro-active vehicle relocation strategies basically depends to some extent
on a minimum existing diversity in the data. Therefore, it can be stated that the
derived dosd constitutes a suitable offline measure that improves the applicability
of the proposed real-time approach. Namely, it allows reliable conclusions for
suitably customizing the pro-active methods and whether significant reductions of
the attainable response times of the reactive real-time approach are possible by
integrating stochastic knowledge concerning future request occurrences. For further
details, we refer to the more detailed evaluations and descriptions provided by
Ferrucci (2013).
6 Efficiently Controlling en Route Diversions
Many real-time approaches that adapt an existing tour plan simultaneous to its
execution use en route diversions. Such an en route diversion defines a situation
where a vehicle is diverted from its current travel to a request destination by
a plan modification conducted and communicated in real time. This en route
measure is possible due to advances in communication technologies integrated in the
exemplary information flow of a real-time control depicted by Fig. 3. Specifically,
these systems allow for locating vehicles in real time as well as for an instant
communication of new orders to the drivers (see Giaglis et al. (2004), Larsen et
al. (2008), or Ferrucci and Bock (2015)). By doing so, the adaptability of a realtime control and therefore the efficiency of the tour execution can be considerably
improved. For instance, if the delivery or pick-up location of a newly incoming
request is in the direct vicinity of the position of a vehicle currently traveling to
Pro-Active Strategies in Online Routing
229
another request destination, an instant diversion of this vehicle may significantly
reduce the response time of the respective request if other vehicles are positioned
farther away. Thus, due to these positive effects, to the best knowledge of the author
of this paper, aside from the approach of Ferrucci and Bock (2015), no real-time
control approach considers limiting the usage of en route diversions. However,
while the positive consequences of en route diversions are quite obvious and lead
to considerable efficiency improvements, an exhaustive application may also have
negative consequences. First of all, vehicles have to be equipped with the technical
equipment required while drivers have to be taught how to operate the devices in
an efficient way. But, despite the possible effort invested in the training of drivers,
numerous en route diversions to be conducted under the tight time restrictions of
a real-time control are still quite demanding. Hence, the resulting stress level of
the driver may cause substantial visual and/or cognitive distractions with longer
and more frequent off-road glances (see Owens et al. (2010) or Kaber et al. (2012)).
This, in turn, could be a significant cause of accidents (see Ferrucci and Bock (2015)
or Young et al. (2013)).
Ferrucci and Bock (2015) (see p. 81) distinguish between three different kinds of
en route diversion that may occur during the application of the proposed pro-active
real-time approach.
1. Diversion from a real customer (abbr. Dr ): While traveling to the location of
a real request either another real request or a dummy request is assigned to
the considered vehicle, or a waiting command is received. The latter forces the
vehicle to wait at the current position for further instructions. Note that this
covers a switching between different dummy requests.
2. Diversion from a dummy customer (abbr. Dd ): In this case, the travel to a dummy
customer location is stopped due to the assignment of a real request.
3. Diversion from an outdated dummy customer: This special case occurs if a
vehicle is traveling to the location of a dummy customer request and in the
meantime, this request becomes outdated as the remaining occurrence probability
of a request falls below the predetermined threshold.
Note that, due to the absence of dummy requests, the second two cases cannot
occur when applying a reactive real-time approach. Moreover, the first two cases
are actively brought about by plan modifications while the last case occurs by the
removal of dummy requests. Since these removals are known in advance, drivers
can be informed to avoid the aforementioned negative consequences. As the third
kind of en route diversion occurs only rarely, it is not considered further by Ferrucci
and Bock (2015).
In order to rate the remaining two kinds of en route diversions, two different
driver profiles are considered. The first driver profile is denoted as a “job enrichment” orientation (abbreviated as JE) and assumes that a driver prefers to service
real requests. As a consequence, such a driver appreciates changes from a dummy
request to a real request. Hence, only the en route diversions of the first type
negatively affect such a driver. In contrast to this, drivers of the profile “change
avoidance” are negatively affected by all unforeseen changes (abbreviated as CA).
230
S. Bock
Thus, both kinds of en route diversion are interpreted as negative and should be kept
limited.
In order to be able to control the number of conducted en route diversions in
the real-time approach, a penalty cost function D(ζ ) is integrated into the pursued
inconvenience objective z(ζ ). By charging a customizable penalty rate p for each
conducted en route diversion, a considered tour plan ζ that causes n(ζ ) en route
diversions has to account for an additional penalty D(ζ ) = n(ζ ) · p. Note that n(ζ ) is
computed dependent upon the assumed driver profile (JE or CA). As a consequence
of this extension, the reduced costs attained by the best of the two tour plans Pτt and
Pτbt that are generated during a considered anticipation horizon (starting at time τ )
has to compensate for the additional costs being charged for a potentially increased
number of planned en route diversions. Otherwise, the real-time approach does not
change the relevant plan Pτr that is currently in execution. In other words, depending
on the customizable cost rate p, additional en route diversions are only integrated
into the tour plan if the resulting cost reductions are substantial enough that they
exceed these penalties. The attained cost reductions are reductions of the resulting
response time and can be computed depending upon the given objective function
(linear or quadratic) and the defined cost rate p (see Table 6). This allows for a
customizable control of using en route diversions through determining a threshold
of necessary response time reductions. For practical applications, the cost rate p may
be derived from a required minimum reduction of the resulting response time that
should compensate for the each additionally caused en route diversion. In order to
evaluate suitable values for p and to analyze the consequences of resulting en route
diversions and customer inconvenience values, Ferrucci and Bock (2015) conduct
various computational experiments.
For a detailed depiction of the measured results, we refer the reader to the paper
of Ferrucci and Bock (2015). However, the main results of this study can be made
clear by considering Tables 7 and 8 that show the effect of the different penalty cost
rates p for the two simulated objective functions (linear and quadratic, respectively)
and the driver profile “change avoidance”.
Table 6 Response time thresholds and penalty cost rates for en route diversions (see Ferrucci
and Bock (2015), p. 82)
Response time threshold (s)
0 (unrestricted case)
36
72
180
360
720
1800
3600
Complete prohibition
Value for p dependent upon the pursued objective function
Linear
Quadratic
0
0
0.01
0.0001
0.02
0.0004
0.05
0.0025
0.1
0.01
0.2
0.04
0.5
0.25
1.0
1.0
1,000,000
1,000,000
Pro-Active Strategies in Online Routing
231
Table 7 Percentage changes of the total linear customer inconvenience attained in comparison to
the unrestricted case by applying various values for the penalty cost rate p > 0 and using a fleet
size of 10 vehicles
Profile
CA
Obj. val.
#Dr
#Dd
Distance
Values
p=0
0.2698
54.2
27.6
609,074
Percentage reductions/increases with the penalty cost rate p=
0.01
0.02
0.05
0.1
0.2
0.5
1.0
1,000,000
−1.4
−0.6
0.4
2.2
8.1
18.2
25.6
27.2
−23.4 −33.5 −57.9 −74.1 −87.6 −97.8 −99.8
−100
−8.8 −15.1 −27.3 −37.4 −61.4 −90.1 −98.1
−100
0.2
0.4
1.9
4.3
6.1
8.4
9.6
9.7
The simulated driver profile is CA
Table 8 Percentage changes of the total quadratic customer inconvenience attained in comparison
to the unrestricted case by applying various values for the penalty cost rate p > 0 and using a fleet
size of 10 vehicles
Profile
Values
CA
0
Obj. Val. 0.094
#Dr
52.3
#Dd
30.1
Distance 620,405
Percentage reductions/increases with the penalty cost rate p=
0.0001 0.0004 0.0025 0.01
0.04
0.25
1.0
1,000,000
0.1
0
−0.1
2.0
3.7
29.7
56
61.8
−1
−0.5 −12.4 −36.4 −69.8 −96.4 −99.8
−100
−0.7
−5.8
−2.3 −12.5 −26.0 −76.7 −98.5
−100
0.2
−0.4
0.2
1.8
3.3
7.6
9.8
9.8
The simulated driver profile is CA
By considering these results for both mapped inconvenience functions, it
becomes obvious that small penalty cost rates do not significantly increase the
resulting customer inconvenience, but substantially reduce occurring en route
diversions.
All in all, due to the attained results, Ferrucci and Bock (2015) state the following
managerial implications depending on three different practical settings:
• Setting 1—The minimization of customer inconvenience is the only objective
to be considered: Hence, in this case, one may deactivate the penalty cost
extension. However, throughout the measured results, it can be observed that the
smallest penalty values may result in further decreases of the resulting customer
inconvenience. Hence, applying the en route diversion cost rate with those values
is reasonable.
• Setting 2—The minimization of both objectives (customer inconvenience and
the number of en route diversions) are relevant: In this case, the application of
moderate penalty values seems to be useful. For instance, by applying a penalty
value p = 0.05 in the linear case and p = 0.04 in the quadratic case, the number of
occurring diversions is reduced by more than 50%. Simultaneously, the customer
inconvenience is only slightly increased.
• Setting 3—The minimization of the number of occurring en route diversions is the
only objective to be considered: In this case, it is reasonable either to apply the
prohibitive cost rate p = 1,000,000 or to consider using a still large penalty value
of 1.0. In direct comparison with the prohibitive value, the latter substantially
232
S. Bock
reduces the resulting customer inconvenience, but allows only a very moderate
number of diversions.
Due to the derived cognitions of the computational evaluation, it can be stated
that the extension of the real-time approach proposed by Ferrucci and Bock (2015)
allows for a suitable control of en route diversions for the first time. Hence, the
aforementioned negative consequences of this measure can be suitably covered.
7 Identifying Multiple Profiles in the Past Request Data
A second considerable extension of the pro-active real-time approach is proposed in
the paper by Ferrucci and Bock (2016). It allows for the identification of multiple
request patterns in the past data in order to improve the quality of the derived
stochastic knowledge and its efficient application. In contrast to this, the previously
considered approaches (Ferrucci et al. (2013) and Ferrucci and Bock (2015)) assume
that the request occurrences on each day follow a more or less identical pattern. As
a consequence, no distinction of groups of past days is done and the approaches
derive a single profile from the past request data to anticipate future request arrivals.
In contrast to this, by grouping similar past days, the approach of Ferrucci and
Bock (2016) allows for the identification of more than one profile. Specifically, it
is assumed that the request occurrences of each day follow specific attributes that
stem from an unknown set of patterns. As illustrated in Fig. 11, these patterns are not
observable, but recur over time in a random fashion while influencing the request
arrivals on the respective day.
Hence, as patterns cannot be directly observed and identified, Ferrucci and Bock
(2016) try to derive characterizing attributes of the structure of days by grouping
similar past days. This is done by applying a suitable clustering algorithm. For this
purpose, similarity between days and/or groups of days is defined by a specifically
defined distance measure. By separating the time lapse of each day into time slices
of predetermined length (for details, see Ferrucci and Bock (2016)), this measure
calculates the total sum over all these time slices according to the following three
quadratically weighted criteria:
1. Euclidean distance between the barycenters of occurred requests, i.e., for each
time slice, the barycenter of requests occurring during this time period is
computed for each day or group of days. Subsequently, the quadratic sum of
the Euclidean distances between these barycenters of the compared (groups of)
days is generated.
2. Difference of the number of occurred requests, i.e., for each time slice, the
quadratic difference of the number of requests occurring during this time period
is computed.
3. Difference of the average request distances to the barycenter, i.e., for each time
slice, the distribution of requests occurring during the respective time period
within each day (or group of days) is compared. For this purpose, the Euclidean
Pro-Active Strategies in Online Routing
233
Fig. 11 Illustration of the basic assumptions of the multi-pattern approach
distance of each request occurring in the respective time period to the respective
barycenter of the day (or group of days) is computed.
In order to compute the sought profiles as groups of similar past days, the
k-means++ approach proposed by Arthur and Vassilvitskii (2007) is applied.
By using a sophisticated seeding, this approach provides a competitive heuristic
solution to this NP-hard clustering problem within a short time limit. Moreover,
by incorporating some randomness, it produces various profiles in repeated runs.
Hence, Ferrucci and Bock (2016) apply this clustering algorithm 1000 times.
Subsequently, the best assignment is selected that possesses the minimum total
distance of days to their assigned profiles.
The main idea of the approach of Ferrucci and Bock (2016) is to improve the
quality of the derived and applied stochastic knowledge by filtering out the past
days that belong to a profile whose request occurrence structure is similar to the one
observed for the ongoing day. In other words, dummy requests should be generated
solely from those past days that are assumed to possess an identical or comparable
pattern as that observed for the current day. Hence, after generating the profiles
in an offline step, the real-time approach of Ferrucci and Bock (2016) analyzes
the structure of incoming requests in order to identify similar known profiles, i.e.,
the respectively assigned days, in real time. Only these days should be used to
derive suitable dummy requests. However, due to the significant computational
complexity of the MIP that has to be solved for deriving these dummy requests (see
Sec. 3), this computation is done beforehand in an effortful offline step. Therefore,
after generating the respective profiles as sets of past days, the dummy requests
are computed for each subset of existing profiles. Specifically, for each subset of
234
S. Bock
profiles, to derive the respective dummy requests, the stochastic knowledge building
procedure is applied that is described in Sect. 3. Hence, for k existing profiles, this
procedure is applied 2k − 1 times by the offline procedure. Clearly, this exponential
complexity limits the number of profiles which can be generated.
One may think that a significant drawback of applying a k-means clustering
algorithm for identifying existing profiles in the data set consists of the fact that this
approach requires a predetermination of the number of profiles k to be built. Indeed,
this is a significant prerequisite of the clustering algorithm. However, after starting
with a two-group clustering by setting k :− 2, this can be handled by iteratively
increasing the parameter k by one (setting k :− k + 1) until the new clustering with
k + 1 groups no longer significantly outperforms the former one possessing only k
groups.
In order to identify in real-time the subset of profiles that are relevant in time slice
t (note that these time slices are identical with the time slices used in the profile
definition) for the ongoing day, Ferrucci and Bock (2016) propose two different
rules:
• Standardized Rule (SR): This rule keeps a profile p ∈ P as relevant if its total
distance to the current day over the elapsed times slices (i.e., the time slices
1, . . . , t) does not exceed by more than 20% the maximum distance of a day
within profile p to the profile itself over the time slices 1, . . . , t. I.e., SR keeps a
profile for the current day at time slice t if it holds that Δ (d c , p) < 1.2· Δmax
t,p =
1.2· maxΔt (i, p). In this computation, the abbreviation Dp gives the set of days
i∈Dp
assigned to profile p ∈ P and t (i, j) defines the total sum of distance measure
values for the periods 1, . . . , t for two sets of days i and j. Note that a factor of
1.2 is assumed to cover some additional acceptance tolerance.
• Extended Rule (ER): While the standardized rule rates all profiles independently
of each other, the extended rule applies a rating that is relative to the profile
that best matches the current day at the considered time. For this purpose,
at first, for the periods 1, . . . , t, a best matching profile p∗ is identified,
i.e., a profile fulfilling Δt (d c , p∗ ) = minΔt (d c , p). Then, accept the subp∈P
#
"
with pt =
set of profiles p ∈ P | Δt (d c , p) < (1.5 − 0.05· t) · pt · Δmax
t,p
max
c
∗
Δt (d , p ) /Δt,p∗ . The extended rule becomes restrictive if profile p∗ matches a
current day well as, in this case, pt becomes small. This fast orientation towards
a well matching profile is the main intention of the rule. By subtracting 0.05 · t
max
pt · Δmax
t,p from the threshold 1. 5 pt · Δt,p , this orientation is strengthened when
the day unfolds.
Ferrucci and Bock (2016) evaluate the new approach by various computational
experiments. As one intention of these experiments is to assess the ability of the
proposed approach to identify underlying multiple patterns, the data class SGEN is
no longer suitable. As a consequence, the days of a newly generated data class are
built according to predetermined patterns, while these patterns (i.e., specifications
that function as templates for the generation of days with prescribed subregion-
Pro-Active Strategies in Online Routing
235
dependent request arrival probabilities) are built analogously to the days of the data
class SGEN introduced in Sect. 5.1. Following the findings of the study of Ferrucci et
al. (2013) concerning specific structures of request arrivals supporting a successful
application of the pro-active approach, all patterns are generated such that they
possess a high diversity. In other words, it is ensured that the regions that possess
the most incoming requests change from time slice to time slice. Hence, the arrival
probabilities of the 18 subregions differ significantly from time slice to time slice,
while the expected request arrival rate in each time slice is identical in all patterns.
Subsequently, by applying each generated pattern, 60 days are respectively built.
In what follows, the abbreviations 2p, 3p, and 4p are used and denote a data set of
120 days based on two patterns (2p), a data set of 180 days based on three patterns
(3p), and a data set of 240 days that is based on four patterns (4p).
In what follows, the original approach (proposed in Ferrucci et al. (2013)) that
uses only a single profile is identified with an extension “SP”, while the extended
version that applies 2, 3, 4, or more profiles obtains the extension “MP”. In order
to decide about the decision rule used for filtering out the relevant profiles in real
time, Table 9 gives the improvement rates attained by replacing the pro-activeSP-approach by the pro-active-MP approach for different settings of the number
of generated patterns, assumed profiles, and applied objective functions (linear or
quadratic customer inconvenience).
These improvement rates indicate a substantial outperformance of SR by ER
that is further increased for settings being generated by using more patterns. This
clear dominance of ER can be attributed to the fact that ER is able to identify the
relevant profiles much faster than SR. Specifically, by comparing each profile with
the best matching profile, potentially existing outliers in some profiles do not harm
the identification process. This can be particularly observed while applying SR to
settings where the number of profiles coincides with the number of patterns (i.e.,
if k = 2 is applied to 2p, if k = 3 is applied to 3p, or if k = 4 is applied to 4p).
Table 9 Averagely attained improvement rates by applying the extended profile acceptance rule
(ER) (instead of SR) within the pro-active approach for 2, 3, or 4 profiles, best DCminλ -values, and
a fleet size of 10 vehicles
Number of patterns
2p
3p
4p
2p
3p
4p
Number of generated profiles
k=2
k=3
k=4
k=5
Linear objective function – objective values
4.8%
4.5%
3.9%
3.1%
8.8%
12.5%
4.1%
4.0%
2.0%
7.7%
12.7%
8.9%
Quadratic objective function – objective values
12.6%
9.9%
8.0%
13.2
18.4%
25.0%
12.2%
12.0%
4.7%
16.0%
23.5%
18.1%
k=6
3.5%
2.7%
9.2%
9.3%
10.5%
19.0%
Values in bold print are the largest improvement rates attained by ER for two, three, and four
patterns, respectively (see Ferrucci and Bock (2016), p. 367)
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S. Bock
Table 10 Average improvement rates in comparison to the standardized aproach with a single
profile attained by applying k = 2, 3, 4, 5, and 6 profiles for the linear inconvenience objective
function (see Ferrucci and Bock (2016), p. 366)
Linear
Number of
vehicles
8
10
12
Number of generated profiles
Applied patterns
2p
3p
4p
2p
3p
4p
2p
3p
4p
k=2
6.7%
5.2%
0.7%
7.0%
10.0%
0.9%
8.6%
10.7%
0.8%
k=3
5.0%
7.6%
4.2%
6.0%
13.9%
6.8%
8.8%
17.5%
9.4%
k=4
4.4%
8.0%
7.6%
6.0%
14.4%
12.4%
7.2%
16.9%
17.6%
k=5
6.2%
9.3%
6.9%
7.5%
13.9%
11.9%
9.2%
18.0%
16.3%
k=6
4.4%
7.8%
9.3%
6.4%
13.5%
12.8%
7.9%
16.1%
16.9%
Table 11 Average improvement rates in comparison to the standardized aproach with a single
profile attained by applying k = 2, 3, 4, 5, and 6 profiles for the quadratic inconvenience objective
function (see Ferrucci and Bock (2016), p. 366)
Quadratic
Number of
vehicles
8
10
12
Number of generated profiles
Applied patterns
2p
3p
4p
2p
3p
4p
2p
3p
4p
k=2
10.7%
14.2%
1.9%
11.4%
15.8%
4.0%
16.9%
19.7%
2.6%
k=3
9.8%
20.6%
6.5%
11.5%
23.1%
15.4%
15.8%
31.8%
16.0%
k=4
10.2%
19.8%
16.0%
8.6%
22.0%
23.9%
14.2%
31.1%
30.1%
k=5
13.7%
17.8%
14.3%
12.3%
22.5%
23.7%
17.5%
31.6%
30.9%
k=6
8.5%
15.3%
15.8%
12.2%
20.2%
25.6%
18.0%
29.4%
30.4%
As in these settings some profiles contain outliers, a large value of Δmax
t,p prevents
the rejection of these profiles. This reduces the quality of the applied stochastic
knowledge and leads to most significant improvements attained by using ER instead
of SR. As ER determines the rejection of a profile by comparing it to the best
matching profile, these outliers do not have a substantial effect (see Ferrucci and
Bock (2016) for more detailed analyses of the progression of identified profiles
done by SR and ER). Due to these results, the following tests are solely conducted
by applying the pro-active real-time variant that uses ER. Tables 10 and 11 give
the attained improvement rates of applying the pro-active-ER approach assuming
k = 2, 3, 4, 5, and 6 profiles in comparison to the original single profile approach
while applying the linear and the quadratic customer inconvenience function. The
improvement rates attained for the two objective functions strongly indicate that
the approach proposed by Ferrucci and Bock (2016) is able to identify existing
Pro-Active Strategies in Online Routing
237
patterns in the past data to derive stochastic knowledge of higher quality. While the
original approach is not able to derive suitable dummy requests from the past data
due to interfering effects, the fast identification of a relevant subset of useful profiles
done by the pro-active-ER approach leads to substantial reductions of the resulting
request response times. This is particularly true if the number of assumed profiles
k reaches the actual number of patterns. Increasing k to this value enables the proactive-ER approach to delete interferences by non-relevant days almost completely.
Moreover, changes of the request arrival structure that may occur throughout the
ongoing day are efficiently handled by updating the set of relevant profiles for
each new time slice. As the attained improvement rates are quite stable when k is
further increased, an efficient practical application of the approach does not require
to exactly estimate the number of existing patterns.
Rather, it is sufficient not to significantly underestimate this number while
overestimation (within a reasonable range) does not significantly reduce the attained
performance. This improves the practicability of the proposed approach.
8 Brief Summary
This paper provides a brief overview of some recent pro-active real-time routing
approaches that are designed for the delivery processes of urgent goods or services.
These pro-active real-time approaches are characterized by the fact that stochastic
knowledge about future request arrivals that is used for guiding vehicles into
request-likely areas is derived from past data during a prior offline step. By combining time-spatial segments of past days, future request occurrences are forecasted.
By doing so, no unverified assumptions are made concerning the availability of
a specific distribution. This improves the practical applicability of the proposed
approaches. In comparison to a reactive real-time approach that keeps each idle
vehicle at its last service location, the pro-active approach of Ferrucci et al. (2013)
guides vehicles to request-likely areas and keeps them there for a specific amount of
time. It is shown that this approach leads to substantial improvements of the request
response times if the request arrival data possess a minimum degree of diversity.
By defining the so-called degree of structural diversity as a general measure, it is
possible to reliably estimate the attainable performance of additionally using the
derived stochastic knowledge. Subsequently, extensions of this pro-active approach
are described. In order to efficiently control en route diversions that may increase the
number of accidents occurring, the approach of Ferrucci and Bock (2015) integrates
a customizable penalty cost value into the objective function. By doing so, additional
en route diversions have to compensate the defined penalty costs with corresponding
reductions of the request response times. It turns out that, depending on the pursued
objectives, reasonable definitions of the penalty cost value can be derived. Finally,
the pro-active approach can be extended to be able to identify multiple patterns in
past request data. By doing so, the approach is capable of filtering out past days
that are relevant for the current ongoing day in real time. As these selections avoid
238
S. Bock
the unwanted interfering effects of past days that are irrelevant to the current day,
the quality of the derived stochastic knowledge is frequently significantly improved.
This may lead to further substantial reductions of request response times.
Acknowledgments I thank DDS Digital Data Services GmbH in Karlsruhe, Germany, for
generously providing us with excellent real-world road network data.
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Prescriptive Analytics for Dynamic Real
Time Scheduling of Diffusion Furnaces
M. Vimala Rani and M. Mathirajan
1 Introduction
Semiconductors are required by many industries such as information technology
(IT), IT enabled services, office automation, industrial machinery and automation,
and engineering. Thus, the sales of semiconductors keep on increasing (SGSR
2020), making the semiconductors industry a crucial player in the industry sectors.
Semiconductor manufacturing (SM) processes are broadly classified into the
front-end process and back-end process. Wafer fabrication (Wafer fab) and wafer
probing together constitute the front-end process. The assembly and final testing
constitute the back-end process. In SM industry, supply chain management (SCM)
problems are very complex, and this is due to long cycle times, high levels of
stochasticity, and non-linearity in the manufacturing process (Sun and Rose 2015;
Mönch et al. 2018). Further, in SM industry, the capability of meeting delivery
commitment given to the customer as well as minimizing the cycle time are the
most important issues in meeting the competition in the global market (Mönch
et al. 2013). One of the ways to address this supply chain issue is to propose
optimal/efficient scheduling, and this involves (i) maximizing the resource(s) usage,
(ii) delivering the product on-time, and (iii) facing the increased demand and
addressing the continuous competition in the marketplace. Researches in scheduling
have addressed different areas of the SM industry (Koo and Moon 2013; Monch
M. Vimala Rani ()
Vinod Gupta School of Management, Indian Institute of Technology, Kharagpur, Kharagpur, India
e-mail: vimala@vgsom.iitkgp.ac.in
M. Mathirajan
Department of Management Studies, Indian Institute of Science, Bangalore, India
e-mail: msdmathi@iisc.ac.in
© Springer Nature Switzerland AG 2021
S. Srinivas et al. (eds.), Supply Chain Management in Manufacturing and Service
Systems, International Series in Operations Research & Management Science 304,
https://doi.org/10.1007/978-3-030-69265-0_9
241
242
M. Vimala Rani and M. Mathirajan
et al. 2011; Mathirajan and Sivakumar 2006). This study addresses a scheduling
problem in the manufacturing of wafer fab, which is the first phase process in SM.
Scheduling is very important in wafer fab due to complex operations involving
multiple types of expensive machines, re-entrant systems, and time-consuming
processes. Thus, this study is concerned about scheduling in wafer fab, particularly
diffusion operation. The diffusion operation is carried out in a diffusion furnace
(DF), which is the batch processor (BP). In BP, more than one job is processed
simultaneously (with the same start and finish times) as a batch subject to the
capacity of the BP. The efficient scheduling of diffusion furnaces would significantly
improve the performance of the SM industry’s supply chain, as the diffusion process
is the lengthiest process (9–10 h) in the wafer fab area. Because of the long
processing time required for the diffusion operation, the production rate, as well
as due-date compliance of the product, are highly dependent on the efficient way of
scheduling DF(s).
Further, in reality, there are real-time events (RTE) dynamically occurring
associated with job(s) (such as change in due-date, change in arrival time, change
in priority, job cancellation) and/or resource(s) (e.g., machine breakdown, operator
illness, tool failure, shortage of material, defective material) (Wang and Fei 2014).
Due to this, in general, the production managers are finding difficulty in meeting
the commitment given to the customer for delivering the product with the existing
schedule. So, the production managers not only need to generate high-quality
solutions, but they also have to address the real-time dynamic event in a costeffective manner. Hence, this study focuses on dynamic real-time scheduling
(DRTS) of diffusion furnace(s). Further, as the processing time required at the
diffusion furnace accounts for nearly 75% of the processing time required for the
wafer fab, we considered different due-date based objectives.
The organization of the chapter is as follows. The description of the research
problem is given in the next section. Section 3 reviews the relevant previous research
done in the scheduling of diffusion furnaces. In Sect. 4, the proposed mathematical
model is discussed. Seven different Apparent Tardiness Cost (ATC) based greedy
heuristic algorithms (GHA) are proposed and the same are discussed in Sect. 5.
Analyses of the proposed seven different ATC-GHA are presented in Sect. 6. In the
final section, the conclusion and potential future research directions are discussed.
2 Problem Description and Assumption
The diffusion furnace available for scheduling may be a single diffusion furnace,
identical parallel diffusion furnaces, or non-identical parallel diffusion furnaces.
Further, due to technical reasons, some jobs can be processed only in a particular
diffusion furnace available in the shop floor (this refers to machine eligibility
restriction in scheduling). This study focuses on both single diffusion furnace and
non-identical parallel diffusion furnaces with machine eligibility restrictions. The
diffusion furnace simultaneously processes 6 to 12 standard jobs as a batch.
Prescriptive Analytics for Dynamic Real Time Scheduling of Diffusion Furnaces
243
There are ‘N’ jobs available. Each job belongs to a different family, and
each family has a different processing time. However, the jobs having the same
processing time requirement belong to the same family. Due to the distinct nature
of the chemical process in each family (incompatible-job families), a batch can be
formed by considering a set of jobs from the same family. Further, jobs are having
different arrival time, different due-date, and they are non-agreeable. That is, if the
arrival time of job ‘i’ is less than the arrival time of job ‘j’ then it is not-implied
that due date of job ‘i’ is less than the due date of job ‘j’. Further, the random
occurrences of real time events with respect to job(s)/resources(s) while scheduling
diffusion furnace(s) are considered.
With this problem description, the objective of this study is to optimize eight
different customer perspectives objectives/criteria: Total Tardiness (TT), Total
Weighted Tardiness (TWT), Number of Tardy (NT) Jobs, Weighted NT (WNT)
Jobs, On Time Delivery (OTD) rate, Total Earliness and lateness (TEL), Total
Weighted Earliness and Lateness (TWEL), and Maximum Lateness (Lmax). These
criteria are defined as follows:
N
TT =
Tj
j=1
TWT =
N
Wj ∗ Tj
j=1
NT =
N
UPj
j=1
WNT =
N
Wj ∗ UPj
j=1
OTD Rate =
N
OTDj/N
j=1
N
CTj − DDj TEL =
j=1
TWEL =
N
Wj ∗ CTj − DDj j=1
Lmax =
max Lj.
j =1 t o N
244
M. Vimala Rani and M. Mathirajan
Where,
CTj :
DDj :
Tj :
Wj :
UPj :
OTDj :
Lj :
Completion time of job ‘j’
Due-date of job ‘j’
Tardiness of job ‘j’ = (CTj - DDj ) if CTj > DDj ; 0 Otherwise
Weight of job ‘j’
Unit penalty of job ‘j’ = 1 if CTj > DDj ; 0 Otherwise
On time delivery of job ‘j’ = 0 if CTj > DDj ; 1 Otherwise
Lateness of job ‘j’ = (CTj - DDj )
This study makes the following assumptions for developing prescriptive analytics
model(s)/methodologies:
• Other than RTE, which occurs randomly during the schedule planning period, we
assumed the deterministic situation.
• Every job must pass through the diffusion operation, and it is not dependent on
other jobs
• For each diffusion furnace, the number of jobs in a batch should be less than or
equal to the maximum limit of the corresponding diffusion furnace.
• Preemption is not allowed
3 Related Literature Review
Among the four phases of SM, wafer fab is the highly complex and capitalintensive phase (Li et al. 2017). The importance of scheduling in wafer fab has been
increasing steadily over the past few decades (Sarin and Shikalgar 2001). This area
takes a total of 3–15 weeks in comparison with the required overall processing time
of 8–30 weeks for SM. The longer processing times often belong to batch processing
operations (diffusion, oxidation, deposition, etc.,) in wafer fab. Scheduling of batch
processing machines causes higher machine utilization, lower work-in-process
inventory, shorter cycle time, and greater customer satisfaction (Pinedo 2012). This
study particularly focuses on the scheduling of diffusion furnaces, a batch processor,
where the diffusion operation takes up to 10 h (Mönch et al. 2006). Furthermore,
around 30% of the total WIP (Work in Process) in a wafer fab lie in diffusion stages,
due to long process times (Jung et al. 2014).
Based on the type of data, diffusion furnace(s) scheduling can be grouped into
deterministic and stochastic. This study focuses on the deterministic scheduling of
diffusion furnace(s) as data tracking is not a difficult one in the SM industry, where
the whole shop floor is computerized. However, readers interested in stochastic
scheduling of BPM can refer to Park and Banerjee (2011).
Some of the researchers (e.g. Pirovano et al. 2020) in deterministic scheduling of
diffusion furnace(s) consider one or more upstream and/or downstream operations
along with the diffusion operation. This study concerns about single bottleneck
operation: diffusion operation only. The diffusion furnace(s) considered for research
Prescriptive Analytics for Dynamic Real Time Scheduling of Diffusion Furnaces
245
in wafer fab may be grouped into single diffusion furnace, identical parallel
diffusion furnaces, non-identical parallel diffusion furnaces, diffusion furnaces with
machine eligibility restrictions, and diffusion furnaces without machine eligibility
restrictions. This study focuses on both single and non-identical parallel diffusion
furnaces with machine eligibility restrictions.
Further, diffusion furnace(s) scheduling can be grouped into static scheduling
(e.g. Mönch and Roob 2018) and dynamic scheduling. Various DF studies addressing the dynamic scheduling of DF(s) consider the future arrival of jobs related
to the dynamic situation. However, in reality, there is another situation related to
the dynamic situation, i.e., RTE. Hence, this study considers both the dynamic
situations: dynamic job arrival and the occurrences of RTE randomly.
Based on objectives, the studies on deterministic and dynamic scheduling of
diffusion furnace(s) are further classified into (i) completion time-based objectives
(related to organization perspective), and (ii) due-date based objectives (related
to customer perspective). Because the present study considers customer perspectives objective, the organizational perspectives objectives (completion time-based
objectives) (e.g. Rocholl et al. 2018) are not discussed here. Finally, based on the
solution methodologies, the existing research can be broadly classified into mathematical programming-based approaches, greedy heuristic approaches, metaheuristic
approaches, and simulation.
Based on the classifications presented here, a brief review of closely related
earlier studies are summarized in Table 1. Table 1 clearly indicates that the
research problem configuration considered (last row of Table 1) in this study:
“dynamic real-time scheduling of (a) single diffusion furnace, and (b) non-identical
parallel diffusion furnaces with machine eligibility restrictions, dynamic jobarrivals, incompatible-job families, non-agreeable release times & due-dates, and
unexpected RTE, to optimize eight different due-date based scheduling objectives”
is a new scheduling problem associated with batch processor in general, particularly
diffusion furnaces.
4 Mathematical Model for Dynamic Scheduling of Diffusion
Furnaces
The proposed mathematical models for dynamic scheduling (DS) of single diffusion furnace (SDF) and non-identical parallel diffusion furnaces with machine
eligibility restrictions (NPDF-MER) to optimize eight different objectives/criteria
are explained in this section. In the proposed mathematical models, only the
future arrival of the jobs is considered under dynamic scheduling, whereas the
randomly occurring real-time events and dynamic arrival of jobs are considered in
the proposed ATC based greedy heuristic algorithms. Further, this section discusses
the validation of the proposed mathematical model.
Kurz and
Mason
(2008)
Chiang
et al.
(2008)
Cheng et
al.
(2008)
Malve
and
Uzsoy
(2007)
Mönch
et al.
2006
Dirk and
Monch
(2006)
Mönch
et al.
(2005)
Kurz
(2003)
Uzsoy
(1995)
Author
√
√
√
√
√
√
√
√
√
√
√
√
√
√
With
machine
Eligibility
√
Without
machine
Eligibility
√
With
machine
Eligibility
Non-identical
Parallel machine
With
future
arrival
of jobs
With
With job resource
related
related
RTE
RTE
Only
Only
Dynamic Scheduling
√
Without
machine
Single
Eligibilmachine ity
√
Identical Parallel
Machine
Diffusion furnace as
Table 1 A closely related review
√
√
√
√
√
√
√
√
√
√
Mathematical
Programming
√
√
√
√
√
√
√
√
√
√
√
√
√
√
√
√
√
√
(continued)
√
√
√
OTD
TT TWT NT WNT rate TEL TWEL Lmax
Scheduling objective
Wt. of
Greedy Meta
onHeuris- Heuris- time
tic
tic
jobs
Release time & due-date Solution methodologies
With Job
and
resource
Nonrelated
Agreeable agreeable
RTE
Vimala Rani and
Mathirajan
(2016b)
Vimala Rani and
Mathirajan
(2016a)
Vimala Rani and
Mathirajan
(2015)
Mansoer and
Koo (2015)
Bilyk et al.
(2014)
Jia et al. (2013b)
Chen et al.
(2013)
Jia et al. (2013a)
Mathirajan and
Vimalarani
(2012)
Li et al. (2010)
Kim et al. (2010)
Guo et al. (2010)
Chiang et al.
(2010)
Bar-Noy et al.
(2009)
Li et al. (2009)
Li and Qiao
(2008)
Table 1 (continued)
√
√
√
√
√
√
√
√
√
√
√
√
√
√
√
√
√
√
√
√
√
√
√
√
√
√
√
√
√
√
√
√
√
√
√
√
√
√
√
√
√
√
√
√
√
√
√
√
√
√
√
√
√
√
√
√
√
√
√
√
√
√
√
√
√
√
√
√
√
√
√
√
√
√
√
√
√
√
√
√
√
√
√
√
√
√
√
√
√
√
√
√
(continued)
√
Proposed
research
study
Vimala Rani
and
Mathirajan
(2020b)
Vimala Rani
and
Mathirajan
(2020a)
Fidelis and
Arroyo
(2017)
Vimala Rani
and
Mathirajan
(2016c)
Author
√
√
√
√
√
√
With
machine
Eligibility
√
Without
machine
Eligibility
√
With
machine
Eligibility
Non-identical
Parallel machine
With
future
arrival
of jobs
With
With job resource
related
related
RTE
RTE
Only
Only
Dynamic Scheduling
√
√
Without
machine
EligibilSingle
machine ity
√
Identical Parallel
Machine
Diffusion furnace as
Table 1 (continued)
√
√
√
√
√
√
√
√
√
√
√
√
√
Mathematical
Programming
√
√
√
√
√
√
√
√
√
√
√
√
√
√
√
√
√
√
√
√
√
√
√
√
√
OTD
TT TWT NT WNT rate TEL TWEL Lmax
Scheduling objective
Wt. of
onGreedy Meta
Heuris- Heuris- time
jobs
tic
tic
Release time & due-date Solution methodologies
With Job
and
resource
related
NonRTE
Agreeable agreeable
Prescriptive Analytics for Dynamic Real Time Scheduling of Diffusion Furnaces
249
4.1 (0-1) MILP Model for DS-SDF
The notations used for the proposed mathematical model for DS-SDF are discussed
as follows.
Notations:
Sets:
J Jobs
F Families
B Batches
Index:
j 1 . . . N for jobs
b 1 . . . K for batches
f 1 . . . G for families
Parameters:
A First time availability of the DF
BC Batch capacity of DF
RTj Release time of a job ‘j’
DDj Due-date of a job ‘j’
Wj Priority (or) Weight of a job ‘j’
PTf Processing time of a family ‘f’
FAjf Family association for a job, equals 1 if job ‘j’ belong to family ‘f’; 0
otherwise
Decision variables:
Xjb
Yfb
1 if job ‘j’ is processed in a batch ‘b’; 0 Otherwise
1 if family ‘f’ is processed in a batch ‘b’; 0 Otherwise
Dependent variables:
CTBb Completion time of a batch ‘b’
RTBb Release time of a batch ‘b’
PTBb Processing time of a batch ‘b’
CTj Completion time of job ‘j’
Tj Tardiness of job ‘j’
ELj Earliness lateness of job ‘j’
UPj Unit penalty of job ‘j’
TT Total tardiness of ‘N’ jobs
TWT Total weighted tardiness of ‘N’ jobs
NT Number of tardy jobs among ‘N’ jobs
WNT Weighted number of tardy jobs
OTDRate On time delivery rate among ‘N’ jobs
TEL Total earliness lateness among ‘N’ jobs
250
M. Vimala Rani and M. Mathirajan
TWEL Total weighted earliness lateness of ‘N’ jobs
Lmax Maximum lateness among ‘N’ jobs
Formulation of (0-1) MILP Model for DS-SDF
Minimize T T
(1)
Subject to
G
Yf b ≤ 1
∀b ∈ [1, K]
(2)
∀b ∈ [2, K]
(3)
f =1
G
Yf,b−1 ≥
f =1
G
Yf b
f =1
Xj b ∗ FAjf ≤ Yf b
∀j ∈ [1, N] ; ∀b ∈ [1, K] ; ∀f ∈ [1, G]
(4)
N
Xj b ∗ FAjf ≥ Yf b
∀f ∈ [1, G] ; ∀b ∈ [1, K]
(5)
Xj b = 1
∀j ∈ [1, N]
(6)
Xj b ≤ BC
∀b ∈ [1, K]
(7)
RTB b ≥ A
b=1
(8)
RTB b ≥ RTj ∗ Xj b
∀j ∈ [1, N] ; ∀b ∈ [1, K]
(9)
RTB b ≥ CTB b−1
∀b ∈ [2, K]
(10)
∀b ∈ [1, K]
(11)
∀b ∈ [1, K]
(12)
j =1
K
b=1
N
j =1
P TB b =
G
PTf ∗ Yf b
f =1
CTB b = RTB b + P TB b
Prescriptive Analytics for Dynamic Real Time Scheduling of Diffusion Furnaces
CTj ≥ CTB b − (BigM) ∗ 1 − Xjb
251
∀b ∈ [1, K] ; ∀j ∈ [1, N]
(13)
Xj b ∈ {0, 1}
∀j ∈ [1, N] ; ∀b ∈ [1, K]
(14)
Yf b ∈ {0, 1}
∀f ∈ [1, G] ; ∀b ∈ [1, K]
(15)
Computation of Due-date based Scheduling Criteria
Tj ≥ CTj − DD j
TT =
N
J =1
T WT =
N
∀j ∈ [1, N]
(16)
(17)
Tj
Wj ∗ Tj
(18)
j =1
UPj ≥ min 1, Tj
NT =
N
∀j ∈ [1, N]
UPj
(19)
(20)
j =1
N
W NT =
Wj ∗ UPj
j =1
⎛
OT D Rat e = ⎝N −
N
(21)
⎞
UPj ⎠ /N
(22)
j =1
ELj ≥ CT j − DD j T EL =
N
ELj
∀j ∈ [1, N]
(23)
(24)
j =1
T W EL =
N
Wj ∗ ELj
(25)
j =1
Lmax =
max Tj
j =1 t o N
(26)
252
M. Vimala Rani and M. Mathirajan
The objective function (Eq. 1) seeks to minimize the total tardiness of ‘N’ jobs.
Constraint (2) ensures that no more than one family is assigned to a batch. Constraint
(3) takes a responsibility to construct a batch sequentially. Constraint (4) states that
a job can be processed in a batch only if the corresponding family is processed in
that batch. Constraint (5) conditions that empty family (that is, a family without any
jobs) cannot be assigned to any batch. Constraint (6) is used to assign the job to any
one batch without splitting it. Constraint (7) ensures that the number of jobs in any
batch does not exceed the batch capacity of the diffusion furnace.
Constraint (8) guarantees that the first batch can start after the first-time
availability (that is the given, ‘Ath’ hour) of the diffusion furnace. Moreover, the
starting time of every batch should be greater than the release time of jobs in that
batch and the completion time of the previous batch. Constraint (9) and (10) ensure
this.
Constraint (11) states that the processing time of a batch is equal to the processing
time of the corresponding family. Constraint (12) computes the completion time of
each batch by adding its starting time and processing time. Constraint (13) computes
the completion time of the job. Constraint (14) and Constraint (15) assign binary
values to decision variables. Constraint (16) and (17) are used to compute tardiness
and total tardiness, respectively.
For primary scheduling objective represented in constraint (1), Constraints (18)
to (26) are introduced to compute the value of other scheduling objectives/criteria.
Accordingly, Constraint (18) calculates total weighted tardiness. Constraint (19)
assigns ‘1’ to jobs that are completed after their due-date. Constraint (20) and
(21) calculate the total number of tardy jobs and the total weight of all tardy
jobs, respectively. Constraint (22) is used to calculate on time delivery rate. A
Constraint (23) calculates the earliness lateness as the absolute difference between
the completion time of a job and its due-date. Constraint (24) calculates the
total earliness lateness, and Constraint (25) calculates the total weighted earliness
lateness of the job. Finally, Constraint (26) calculates maximum lateness among ‘N’
jobs.
Like minimizing total tardiness (TT) of ‘N’ jobs as a primary scheduling
objective, we can optimize any one the following scheduling criterion/objective as
a primary scheduling criterion/objective as follows:
1. To minimize TWT, we need to have the Constraints (2) to (16) and Constraint
(18) along with Constraint (1) as a new objective function: Min TWT
2. To minimize NT, we need to have the Constraints (2) to (16), Constraint (19),
and Constraint (20) along with Constraint (1) as a new objective function: Min
NT
3. To minimize WNT, we need to have the Constraints (2) to (16), Constraint (19),
and Constraint (21) along with Constraint (1) as a new objective function: Min
WNT
4. To maximize OTD rate, we need to have the Constraints (2) to (16), Constraint
(19) and Constraint (22) along with Constraint (1) as a new objective function:
Max OTD rate
Prescriptive Analytics for Dynamic Real Time Scheduling of Diffusion Furnaces
253
Table 2 A numerical instance
Job
J1
J2
J3
J4
J5
J6
J7
J8
J9
J10
Family
2
1
4
3
5
1
1
3
1
5
Release time
1
6
4
1
4
8
6
2
6
6
Due-date
26
31
23
16
31
29
28
30
36
26
Priority (or weight)
6
2
3
6
3
2
6
1
2
1
5. To minimize TEL, we need to have the Constraints (2) to (15), Constraint (23),
and Constraint (24) along with Constraint (1) as the new objective function: Min
TEL
6. To minimize TWEL, we need to have the Constraints (2) to (15), Constraint (23),
and Constraint (25) along with Constraint (1) as the new objective function: Min
TWEL
7. To minimize Lmax, we need to have the Constraints (2) to (16) and Constraint
(26) along with Constraint (1) as the new objective function: Min Lmax
Validation of the Proposed (0-1) MILP Model for DS-SDF:
To validate the proposed (0-1) MILP model, numerical data (Table 2) representing the research problem is generated. Further, this study assumed that maximum of
two jobs could be assigned to a batch, and the furnace is available from the second
hour onwards. This numerical example is given as input to the LINGO set code for
generating the proposed (0-1) MILP model and solved using the LINGO solver. The
optimal solution obtained is presented in Table 3. The analysis of this table confirms
the right mathematical representation of the DS-SDF.
4.2 (0-1) MILP Model for DS-NPDF-MER
The additional/modified notations required towards the proposed model for DSNPDF-MER are given here.
Additional Notations
Sets:
M
Machines (Diffusion furnaces)
Index:
m
1 . . . L for machines
Parameters:
254
M. Vimala Rani and M. Mathirajan
Table 3 The optimal solution
Batch
B1
B2
Job
Batch (B)
RT PT CT
2
4
6
6 10 16
J1
J4
J8
B3
J2
16
2
J6
B4
J7
18
2
J9
B5
J5
20 20
J10
B6
J3
40 16
Total tardiness
B1
J4
2 10
J8
B2
J2
12
2
J7
B3
J6
14
2
J9
B4
J1
16
4
B5
J5
20 20
J10
B6
J3
40 16
Total weighted tardiness
B1
J1
2
4
B2
J4
6 10
B3
J8
16 10
B4
J6
26
2
J7
B5
J2
28
2
J9
B6
J5
30 20
J10
B7
J3
50 16
Number of tardy jobs
B1
J4
3 10
B2
J6
13
2
J7
B3
J1
15
4
B4
J8
19 10
B5
J2
29
2
J9
18
20
40
56
12
14
16
20
40
56
6
16
26
28
30
50
66
13
15
19
29
31
Job
DD
26
16
30
31
29
28
36
31
26
23
W
6
6
1
2
2
6
2
3
1
3
16
30
31
28
29
36
26
31
26
23
6
1
2
6
2
2
6
3
1
3
26
16
30
29
28
31
36
31
26
23
6
6
1
2
6
2
2
3
1
3
16
29
28
26
30
31
36
6
2
6
6
1
2
2
T
0
0
0
0
0
0
0
9
14
33
56
0
0
0
0
0
0
0
9
14
33
E
L
WT
UPJ
WNT
EL
WEL
0
0
0
0
0
0
0
27
14
99
140
0
0
0
0
0
0
0
1
1
1
3
0
0
0
0
0
0
0
(continued)
Prescriptive Analytics for Dynamic Real Time Scheduling of Diffusion Furnaces
255
Table 3 (continued)
Batch (B)
Job
RT PT CT DD W T
B6
J5
31 20 51 31
3
B7
J3
51 16 67 23
3
B8
J10 67 20 87 26
1
Weighted number of tardy jobs
B1
J1
2
4
6 26
6
B2
J4
6 10 16 16
6
B3
J8
16 10 26 30
1
B4
J6
26
2 28 29
2
J7
28
6
B5
J2
28
2 30 31
2
J9
36
2
B6
J5
30 20 50 31
3
J10
26
1
B7
J3
50 16 66 23
3
On time delivery rate (10- unit penalty)/10
B1
J4
2 10 12 16
6
J8
30
1
B2
J5
12 20 32 31
3
J10
26
1
B3
J2
32
2 34 31
2
J6
29
2
B4
J7
34
2 36 28
6
J9
36
2
B5
J1
36
4 40 26
6
B6
J3
40 16 56 23
3
Total earliness lateness
B1
J4
2 10 12 16
6
J8
30
1
B2
J3
12 16 28 23
3
B3
J6
28
2 30 29
2
J7
28
6
B4
J1
30
4 34 26
6
B5
J2
34
2 36 31
2
J9
36
2
B6
J5
36 20 56 31
3
J10
26
1
Total weighted earliness lateness
B1
J1
2
4
6 26
6
B2
J4
6 10 16 16
6
J8
30
1
B3
J3
16 16 32 23
3
Batch
Job
E
L
WT
UPJ
WNT
3
3
1
7
EL
WEL
0
0
0
0
0
0
0
1
1
1
0.7
4
18
0
0
0
0
0
0
0
0
0
0
1
6
3
5
8
0
14
33
4
18
0
0
0
0
0
0
0
0
0
0
5
1
2
8
5
0
25
30
4
18
1
6
3
5
8
0
14
33
92
24
18
15
2
12
48
10
0
75
30
234
0
0
0
9
(continued)
256
M. Vimala Rani and M. Mathirajan
Table 3 (continued)
Batch
Job
B4
Batch (B)
RT PT CT
32 20 52
J5
J10
B5
J6
52
2
J7
B6
J2
54
2
J9
Maximum lateness
54
56
Job
DD
31
26
29
28
31
36
W
3
1
2
6
2
2
T
E
L
21
26
25
26
25
20
26
WT
UPJ
WNT
EL
WEL
Am First time availability of the machine ‘m’
BCm Batch capacity of machine ‘m’
MAjm Machine association for a job, equals 1 if job ‘j’ can be processed in
machine ‘m’; 0 otherwise
Decision variables:
Xjbm
Yfbm
1 if job ‘j’ is processed in a batch ‘b’ in a machine ‘m’; 0 Otherwise
1 if family ‘f’ is processed in a batch ‘b’ in a machine ‘m’; 0 Otherwise
Dependent variables:
RTBbm
PTBbm
CTBbm
Release time of a batch ‘b’ in a machine ‘m’
Processing time of a batch ‘b’ in a machine ‘m’
Completion time of a batch ‘b’ in a machine ‘m’
Formulation of (0-1) MILP Model for DS-NPDF-MER
Concept wise all the constraints in the proposed model for DS-SDF are like that
of the model required for DS-NPDF-MER, except the Constraint (6) presented in
Sect. 4.1. Furthermore, the constraints required to compute the value of TT, TWT,
NT, WNT, OTD rate, TEL, TWEL, and Lmax are independent of the number of
furnaces.
Hence, the constraints presented in Sect. 4.1 for computing scheduling criteria/objectives are exactly the same for DS-NPDF-MER. Accordingly, the proposed
model with the scheduling objective of minimizing TT for DS-NPDF-MER is as
follows:
Minimize T T
Subject to
G
f =1
Yf bm ≤ 1
(27)
∀b ∈ [1, K] ; ∀m ∈ [1, L]
(28)
Prescriptive Analytics for Dynamic Real Time Scheduling of Diffusion Furnaces
G
Yf,b−1,m ≥
f =1
G
Yf bm
∀b ∈ [2, K] ; ∀m ∈ [1, L]
257
(29)
f =1
Xj bm ∗ F Ajf ≤ Yf bm
∀j ∈ [1, N ] ; ∀f ∈ [1, G] ; ∀b ∈ [1, K] ; ∀m ∈ [1, L]
(30)
N
Xj bm ∗ FAjf ≥ Yf bm
∀m ∈ [1, L] ; ∀f ∈ [1, G] ; ∀b ∈ [1, K]
j =1
(31)
K L
Xj bm ∗ MAj m = 1
∀j ∈ [1, N]
(32)
∀b ∈ [1, K] ; ∀m ∈ [1, L]
(33)
RT B bm ≥ Am
b = 1; ∀m ∈ [1, L]
(34)
RT B bm ≥ RT j ∗ Xj bm
∀b ∈ [1, K] ; ∀m ∈ [1, L] ; ∀j ∈ [1, N]
b=1
N
m=1
Xj bm ≤ BC m
j =1
(35)
RT B bm ≥ CT B b−1,
P T B bm =
G
m
P T f ∗ Yf bm
∀b ∈ [2, K] ; ∀m ∈ [1, L]
(36)
∀b ∈ [1, K] ; ∀m ∈ [1, L]
(37)
∀b ∈ [1, K] ; ∀m ∈ [1, L]
(38)
f =1
CT B bm = RT B bm + P T B bm
CT j ≥ CT B bm − (BigM) ∗ 1 − Xj bm ∀j ∈ [1, N] ; ∀b ∈ [1, K] ; ∀m ∈ [1, L]
(39)
Xj bm ∈ {0, 1}
∀j ∈ [1, N] ; ∀b ∈ [1, K] ; ∀m ∈ [1, L]
(40)
Yf bm ∈ {0, 1}
∀f ∈ [1, G] ; ∀b ∈ [1, K] ; ∀m ∈ [1, L]
(41)
Tj ≥ CT j − DD j
∀j ∈ [1, N]
(42)
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M. Vimala Rani and M. Mathirajan
Validation of the Proposed (0-1) MILP Model for DS-NPDF-MER: The same
numerical problem presented in Table 2 is used to validate the model for DS-NPDFMER. In addition, three non-identical parallel diffusion furnaces: MC1, MC2 and
MC3 are considered for the numerical example with capacity as 2 jobs, 3 jobs and
4 jobs, respectively. This study assumes that the first-time availability of these three
non-identical parallel diffusion furnaces is to be 1st hour, 2nd hour, and 3rd hour,
respectively. Further, jobs that belong to family 3 (f3) are processed only in MC2,
out of three non-identical parallel diffusion furnaces considered here. The same
numerical example, given in Table 2, along with required additional data presented
here, is given as input to the LINGO set code for generating the proposed (0-1)
MILP model and solving using the LINGO solver. The optimal solution reports,
similar to Table 3, are prepared. Due to the brevity of this chapter, the reports are
not presented here. However, by analyzing the reports prepared, the proposed model
for DS-NPDF-MER has been validated.
Since the mathematical model for DS-SDF to minimize TWT is computationally
intractable (Vimala Rani and Mathirajan 2016a), the proposed mathematical model
for DS-NPDF-MER is also computationally intractable. So, we focus on GHA based
on dispatching rules, particularly Apparent Tardiness Cost (ATC) rule, and the same
is presented in the next section.
5 ATC Based GHA for Scheduling Diffusion Furnaces
Dispatching rules are widely used in the semiconductor manufacturing industry
(Hildebrandt et al. 2010). The popularity of dispatching rule based GHA are derived
from the fact that they (a) are efficient in a wide range of scheduling problems,
(b) are generally easy to understand and computerize, (c) require very meagre
computational time to provide solution, and (d) can deal with dynamic changes
easily and quickly.
Out of various due-date based dispatching rules, the ATC rule based GHA provides the most efficient solution (Vimala Rani and Mathirajan 2020a). Accordingly,
this study also proposes the dispatching rule, ATC based GHA for DRTS of SDF as
well as NPDF-MER, and the same is discussed in this section.
5.1 ATC-GHA for DRTS of SDF
The details of ATC-GHA for DRTS of SDF are given below:
Step 1: At the time of deciding a batch (batch selection) for scheduling a DF, (a) if
any of the job(s) related RTEs occur, then modify the corresponding job-data on
the work-in-process (WIP), and (b) if any of the resource(s) related RTEs occur
Prescriptive Analytics for Dynamic Real Time Scheduling of Diffusion Furnaces
259
then modify the DF’s available time as T = T+ delay time due to resource related
RTE.
Step 2: At the time of deciding a batch for scheduling a DF, capture all the
characteristics of the jobs which are waiting for diffusion operation. Further,
capture the DF capacity (B) and the available time (T).
Step 3: Cluster the jobs waiting in front of the DF based on their family.
Step 4: In each cluster, calculate “Job-Priority-Index” for every job and sort the
same.
Step 5: For each cluster, create a “temporary batch” by picking ‘B’ jobs from the
top. If the number of jobs in any of the temporary batch is not equal to ‘B’, then
check the WIP for whether the jobs of the same family are coming for diffusion
operation in the future. If it is true, then wait for those jobs to form a full batch.
Otherwise, form a partially filled batch. Then modify the starting time of the
temporary batch of that cluster as max (T, longest release time of all jobs in that
temporary batch).
Step 6: Compute and compare the starting time of each temporary batch,
If any temporary batch has a completion time strictly less than the starting time
of all other temporary batches, then select it and go to Step 9
Else go to the next step
Step 7: Compute “Batch-Priority-Index” for each temporary batch formed in Step
5.
Step 8: Choose the temporary batch that has the greatest “Batch-Priority-Index”.
Step 9: If any of the job(s) related RTEs occur, then modify the corresponding jobdata on the work-in-process (WIP), else if any of the resource(s) related RTEs
occur, then modify the DF’s available time as T = T+ delay time due to resource
related RTE and go to Step 2.
Else, go to the next step.
Step 10: Assign the selected batch to the diffusion furnace.
Step 11: Compute TWT, NT, WNT, OTD rate, TEL, TWEL, and Lmax of assigned
batch (es).
Step 12: Remove the assigned jobs from WIP and modify the DF’s available time
“T” as the completion time of the assigned batch in Step 10
Step 13: Repeat Step 1 until all jobs are scheduled.
This study proposed and implemented seven different above GHA in Turbo C.
All these seven different GHA differ only in two steps (Step 4 and Step 7), and the
same is presented in Table 4.
5.2 ATC-GHA for DRTS for NPDF-MER
The details of ATC based GHA for DRTS of NPDF-MER are given below:
Step 1: At the time of deciding a batch (batch selection) for scheduling a DF, (a) if
any of the job(s) related RTEs occur, then modify the corresponding job-data on
260
M. Vimala Rani and M. Mathirajan
Table 4 Modified steps in different ATC-GHA
Proposed ATC-GHA
ATC-GHA1
ATC-GHA2
ATC-GHA3
ATC-GHA4
ATC-GHA5
ATC-GHA6
ATC-GHA7
Step 6: Calculate
Job-Priority-Index based on
EDD
ATC rule in Balasubramanian
et al. (2004)
ATC rule in Mönch et al.
(2006)
ATC rule in Li et al. (2010)
ATC rule in Li et al. (2010)
ATC rule in Vimala Rani and
Mathirajan (2016a)
ATC rule in Vimala Rani and
Mathirajan (2016a)
Step 9:Calculate
Batch-Priority-Index based on
Batch ATC (BATC) rule in
Mehta and Uzsoy (1998)
BATC rule in
Balasubramanian et al. (2004)
BATC rule in Mönch et al.
(2006)
BATC rule in Li et al. (2010)
BATC rule in Mönch et al.
(2006)
BATC rule in Mönch et al.
(2006)
BATC rule in Vimala Rani and
Mathirajan (2016a)
the work-in-process (WIP), and (b) if any of the resource(s) related RTEs occur
then modify the corresponding DF’s (say, DFi) available time as Ti = Ti + delay
time due to resource related RTE.
Step 2: At the time of deciding a batch for scheduling a DF, capture all the
characteristics of the jobs, which are waiting for diffusion operation and the
available time for each of the diffusion furnaces (say ti , ti > 0, where i = 1 to
M and M indicates the number of diffusion furnace).
Step 3: Select the diffusion furnace, which is available earlier.
(i) If a tie occurs in terms of DF available time, then select the DF, which has the
maximum capacity.
(ii) If a tie occurs in terms of DF available time and maximum capacity, then
select the DF, which has machine eligibility restriction.
(iii) If a tie occurs in terms of DF available time, maximum capacity and machine
eligibility restriction, then select the DF arbitrarily.
Step 4: Store the selected DF available time and its capacity to ‘B’ & ‘T’
respectively.
Step 5: Cluster the jobs waiting in front of the selected DF based on their family.
Step 6: For each of the qualified cluster (that is, families, which are possible to
process in a selected diffusion furnace), calculate “Job-Priority-Index” for every
job and sort the same.
Step 7: For each of the qualified clusters, create a “temporary batch” by picking ‘B’
jobs from the top. If the number of jobs in any of the temporary batch in any of
the qualified clusters is not equal to ‘B’ then check the WIP for whether the jobs
of the same family are coming for diffusion operation in the future. If it is true,
then wait for those jobs to form a full batch. Otherwise, form a partially filled
batch. Now, modify the starting time of the corresponding temporary batch as
max (T, longest release time of all jobs in that temporary batch).
Step 8: Compute and compare the starting time of each temporary batch,
Prescriptive Analytics for Dynamic Real Time Scheduling of Diffusion Furnaces
261
If any temporary batch has a completion time strictly less than the starting time
of all other temporary batches, then select it and go to Step 11
Else go to the next step
Step 9: Compute Batch-Priority-Index for each temporary batch formed in Step 7.
Step 10: Choose the temporary batch that has the greatest “Batch-Priority-Index”.
Step 11: If any of the job(s) related RTEs occur, then modify the corresponding
job-data on the work-in-process (WIP), and (b) if any of the resource(s) related
RTEs occur, then modify the corresponding DF’s (say, DFi) available time as
Ti = Ti + delay time due to resource related RTE and go to Step 2.
Else, go to the next step.
Step 12: Assign the selected batch to the selected diffusion furnace.
Step 13: Compute TT, TWT, NT, WNT, OTD rate, TEL, TWEL, and Lmax of
assigned batch(es)
Step 14: Remove the assigned jobs from WIP and modify the selected DF’s
available time “Ti ” as the completion time of the allocated batch in Step 12.
Step 15: Repeat Step 1 until all jobs are scheduled.
This study proposed seven different ATC-GHA for DRTS-NPDF-MER by
varying Step 6 and Step 9, as per Table 4 and implement all these seven different
ATC-GHA in Turbo C.
6 Performance Evaluation of ATC-GHA for DRTS
of Diffusion Furnaces
The performance of the seven different ATC-GHA for DRTS-SDF as well as
DRTS-NPDF-MER are evaluated w.r.t. optimal solution on small scale data and
estimated optimal solution (EOS) on large scale data. The EOS is computed based
on Weibull distribution (Rardin and Uzsoy 2001). The details of the experimental
design, performance measures, empirical and statistical analyses are presented in
this section.
Experimental Design The experimental design proposed in Vimala Rani and
Mathirajan (2020a) is extended to represent the new research problem defined
on scheduling SDF and NPDF-MER, with four additional factors: number of
diffusion furnaces (M), capacity of diffusion furnace (B), available time of diffusion
furnace (AT), and machine eligibility restriction (Mf ) to address non-identical
parallel diffusion furnace with machine eligibility restrictions. Since this study
compares the heuristics solution w.r.t optimal solution on small-scale data, the
factor: number of jobs (N) has two more additional values such as 5 and 10. Table
5 shows the various factors and their values, which are used to generate 450 test
data.
Performance Measures for Empirical Analysis Average relative percentage deviation (ARPD) and Integrated Rank (IRANK) are used here. For each proposed
262
M. Vimala Rani and M. Mathirajan
Table 5 An experimental design for DRTS of diffusion furnaces
Parameters
Number of diffusion furnaces (M)
Capacity of diffusion furnace (B)
Number of levels
1
1
Available time of diffusion
furnace (AT)
Number of jobs (N)
Release time of jobs (RTj )
Due-date of jobs (DDj )
Family (f)
Family processing time (PTf ) and
the probability of job being in the
family ‘f’
1
Machine eligibility restriction
(Mf )
Weight (Wj ) [low, high]
No. of problem configurations
No. of instances per configuration
Total problem instances
5
3
3
1
1
1
Level wise values
4
[6, 6, 9, 12] for DF1 to DF4
respectively
[2, 5, 7, 8] for DF1 to DF4
respectively
5, 10, 25,50,100
[1,8], [1,16], [1,24]
[1,40], [1,60], [1,80]
[1,5]
[2,4,10,16,20] with a probability
of
[0.1, 0.3, 0.4, 0.1, 0.1]
respectively
f3 can be processed in only DF2
1
[1,10]
5*3*3*1*1*1 = 45
10
450
different ATC-GHA, this study computes deviation as per the Eq. (43), for each
problem instances. To know the extent of this deviation w.r.t. benchmark solution
(BSi ), this study divides the deviation of each different ATC-GHA for each problem
instance (Dij ) by corresponding benchmark solution (BSi ) and converts it into
percentage as per equation in (44).
Dij = F S ij − BS i
(43)
RP D ij = Dij /BS i ∗ 100
(44)
Where
i: Problem instances and i ∈ [1,450]
j: ATC-GHA and j ∈ [1,7]
FSij : Feasible solution obtained from ‘jth ’ ATC-GHA for ‘ith ’ problem instance
BSi : Benchmark solution for ‘ith ’ problem instance
Dij : Deviation of ‘jth ’ ATC-GHA for ‘ith ’ problem instance
RPDij : Relative percentage deviation of ‘jth ’ ATC-GHA for ‘ith ’ problem instance
Prescriptive Analytics for Dynamic Real Time Scheduling of Diffusion Furnaces
263
Then, for each different ATC-GHA, the score on ARPD for each problem
configuration as well as for 450 problem instances are computed as per Eq. (45)
ARP D j =
N
i=1
RP D ij /N
(45)
Where
ARPDj : Average relative percentage deviation of ‘jth ’ ATC-GHA
N = 10 when ARPD is computed considering problem configuration wise
N = 450 when ARPD is computed considering entire problem instances
Further, for each of the different ATC-GHA, this study computes IRANK, which
is proposed by Mathirajan et al. (2004), for each of the objectives considered in this
study as per the Eq. (46).
IRANKj =
Maxrank
r=1
Maxrank
{N (r, j ) ∗ r} /
N (r, j )
r=1
(46)
Where
r: Rank and r ∈ [1, 7]
N (r, j): Number of times the ‘jth ’ ATC-GHA in rank ‘r’
Maxrank: Maximum Rank possible (Maxrank = 7 = Number of proposed ATCGHA).
IRANKj : Integrated rank of ‘jth ’ ATC-GHA
Performance Measures for Statistical Analysis For performance evaluation using
statistical analyses, first we compute descriptive statistics: mean, median, standard
deviation, and 95% confidence interval. As normality test is failed over the obtained
RPD results, Kruskal-Wallis non-parametric test are conducted to compare the
proposed different ATC-GHA (Beldar and Costa 2018).
6.1 Empirical Analyses on the Performance of ATC-GHA for
DRTS of Diffusion Furnaces
Empirical analyses of seven different ATC-GHA for DRTS of (i) SDF, and (ii)
NPDF-MER w.r.t. the objectives: TT, TWT, NT, WNT, OTD rate, TEL, TWEL,
and Lmax are discussed here.
Performance Analyses of ATC-GHA for DRTS-SDF: The first ninety problem
instances (small-scale data) are solved by both mathematical model as well as seven
different ATC-GHA for DRTS-SDF in order to obtain the optimal solution and
feasible solution, respectively, for the objective: TT, TWT, NT, WNT, OTD rate,
264
M. Vimala Rani and M. Mathirajan
Fig. 1 Performance of seven different ATC-GHA for DRTS-SDF with respect to optimal: TT,
TWT, NT, WNT, OTD rate, TEL, TWEL, and Lmax: over 90 problem instances
TEL, TWEL, and Lmax. Using the solutions obtained on small scale data, this study
computes RPD for each objective as per the Eq. (44) by considering the optimal
solution as a benchmark solution. Then, for each of the scheduling objectives and
each of the different ATC-GHA, this study calculates the average value of ninety
small-scale problems’ RPD values. ARPD score of seven different ATC-GHA for
each of the objectives are presented in Fig. 1. From this figure, it is observed based
on solving small-scale problems that, by and large, ATC-GHA5, and ATC-GHA6
are performing better for the majority of the objectives considered here.
The reason for the outperforming ATC-GHA may be due to the following:
(a) The ATC rule (given by Li et al. (2010)) considered in ATC-GHA5 helps to
compute exact slack time. This helps to avoid getting high priority for low
Prescriptive Analytics for Dynamic Real Time Scheduling of Diffusion Furnaces
265
priority jobs. In addition, the ATC rule (given by Vimala Rani and Mathirajan
(2016a)) considered in ATC-GHA6 ensures that if the slack is negative or the
job is tardy, the ATC rule reduces not only to WSPT rule but also to weighted
shortest tardy.
(b) The BATC index rule (given by Mönch et al. (2006)) considered in ATC-GHA5
and ATC-GHA6 ensures that unavailable jobs are assigned low priority.
Furthermore, this study evaluates seven different ATC-GHA w.r.t. estimated optimal solution (EOS) as (i) the mathematical models are extremely time-consuming,
which is not suitable for large-scale data, and (ii) the findings observed based on
small-scale data may not be generalized to say that this is true for large-scale data
also. Accordingly, EOS is computed as per Rardin and Uzsoy (2001) for every 450
large scale data, and for each of the objective functions.
By considering these estimated optimal solutions as a benchmark solution,
RPD is computed as per the Eq. (44) for each problem instance and for each
objective. Then, for every 45 problem configurations, ARPD is calculated using the
corresponding RPD score obtained over ten problem instances per configuration,
as per Eq. (45) for the objectives: TT, TWT, NT, WNT, OTD rate, TEL, TWEL &
Lmax. Due to the brevity of the study, these results are not presented. However, the
result obtained for the objective TT is given in Table 6, as an example. However,
the average of the RPD values over entire problem instances is computed as per
Eq. (45) by considering ‘N’ as 450 for each objective and is presented in Fig. 2. It
is observed from Table 6 and Fig. 2 that, for large-scale data, by and large, ATCGHA5 and ATC-GHA6 are outperforming ATC-GHA for most of the scheduling
objectives. It endorses the observation obtained from small-scale data.
This study triangulates the findings observed from the ARPD analysis by using
another performance measure, IRANK, over 450 problem instances. Accordingly,
a [450×7] ranking matrix, indicating a rank for each of the seven different ATCGHA, is constructed for every objective considered here. Using the ranking matrix
[450×7], a frequency ranking matrix [7×7] indicating how many times each of
the proposed ATC-GHA performed to have a particular rank is computed for
every objective. A sample frequency ranking matrix for the scheduling objective
(minimizing TT) is given in Table 7. Using the computed frequency ranking matrix
[7×7] of each of the scheduling objectives, the IRANK of seven different ATCGHA is computed using the Eq. (46).
The computed IRANK of seven different ATC-GHA w.r.t. to every objective is
presented in Table 7. This Table indicates that ATC-GHA5 and ATC-GHA6 are
outperforming ATC-GHA for most of the objectives. This is clearly endorsing the
observation obtained from the analysis of ARPD score wr.t. large-scale data.
Performance Analyses of ATC-GHA for DRTS-NPDF-MER Due to computational
intractability in scheduling DRTS-NPDF-MER for real-life sized problems and the
highly questionable nature of generalizability of better performing variants observed
based on small-scale problem, the performance analyses are carried out only
on large-scale data considering both performance measures: ARPD and IRANK.
Similar to single diffusion furnace, for every objective, problem instance wise EOS
Problem configuration
J1.A1,D1
J1.A1,D2
J1,A1,D3
J1,A2,D1
J1,A2,D2
J1,A2,D3
J1,A3,D1
J1,A3,D2
J1,A3,D3
J2.A1,D1
J2.A1,D2
J2,A1,D3
J2,A2,D1
J2,A2,D2
J2,A2,D3
J2,A3,D1
J2,A3,D2
J2,A3,D3
J3,A1,D1
J3,A1,D2
J3,A1,D3
J3,A2,D1
J3,A2,D2
S.No
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
ARPD score of seven different ATC-GHA in comparison with EOS
ATC-GHA1
ATC-GHA2
ATC-GHA3
ATC-GHA4
274.05
32.39
18.05
−0.74
861.21
103.36
126.04
127.91
1248.84
5.80
84.06
84.06
275.68
228.64
228.64
228.64
149.78
0.00
0.00
44.00
46.44
22.59
−3.41
71.92
765.59
76.29
42.73
60.98
47.90
0.45
−6.22
0.45
−13.33
0.00
0.00
0.00
105.90
7.65
4.77
4.77
261.05
44.00
11.12
8.12
1693.88
−2.47
−1.62
−0.76
31.12
13.48
0.23
8.20
972.17
873.77
−5.74
859.24
404.08
0.92
−1.67
48.71
82.26
5.48
−0.39
1.37
64.84
53.17
3.42
39.54
416.12
118.91
375.32
142.30
102.05
55.73
44.09
58.18
294.71
46.08
19.23
54.48
142.79
29.44
38.85
55.72
56.80
10.11
4.31
7.47
110.47
49.76
22.76
39.79
Table 6 Performance of seven different ATC-GHA for DRTS-SDF w.r.t. EOS
ATC-GHA5
−0.25
126.04
84.06
228.64
0.00
−3.41
42.73
−6.22
0.00
4.77
11.12
−1.62
0.23
−5.74
−1.67
−0.39
3.42
375.32
44.41
18.32
38.85
4.67
22.76
ATC-GHA6
−0.25
126.04
84.06
228.64
0.00
−3.41
42.73
−6.22
0.00
4.77
11.12
−1.62
0.23
−5.74
−1.67
−0.39
3.42
375.32
44.41
20.59
40.50
4.67
22.76
(continued)
ATC-GHA7
−0.25
−2.62
104.06
426.86
906.67
10.86
502.26
35.16
0.00
33.36
8.00
57.57
11.80
866.85
41.57
3.96
50.75
142.30
61.37
73.82
58.73
17.92
42.07
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
J3,A2,D3
J3,A3,D1
J3,A3,D2
J3,A3,D3
J4.A1,D1
J4.A1,D2
J4,A1,D3
J4,A2,D1
J4,A2,D2
J4,A2,D3
J4,A3,D1
J4,A3,D2
J4,A3,D3
J5,A1,D1
J5,A1,D2
J5,A1,D3
J5,A2,D1
J5,A2,D2
J5,A2,D3
J5,A3,D1
J5,A3,D2
J5,A3,D3
Table 6 (continued)
351.55
99.88
169.85
172.80
59.78
50.37
85.67
43.18
36.14
58.09
38.08
55.54
94.98
8.88
23.71
24.09
37.56
35.70
23.96
38.40
35.95
46.70
141.99
30.27
78.57
51.21
24.94
18.29
39.23
27.19
8.22
34.34
20.26
40.92
23.03
7.94
25.64
23.07
23.61
27.30
24.24
22.13
16.40
34.61
79.43
31.57
65.15
33.41
31.10
33.48
59.25
25.02
5.40
30.63
13.51
32.75
17.71
−3.27
36.26
13.33
32.34
24.87
18.22
16.40
11.60
10.51
144.26
21.68
76.41
63.67
29.32
32.61
46.41
27.86
7.74
30.89
23.77
38.17
16.78
4.09
40.59
16.32
38.72
27.36
17.62
19.31
18.21
34.19
77.62
31.57
67.30
33.41
32.15
30.61
58.34
27.42
9.00
35.04
12.43
32.90
18.01
−2.61
32.25
19.80
33.20
29.07
18.94
17.31
16.73
14.42
77.94
32.08
67.54
33.41
32.17
31.40
61.46
27.35
8.46
37.17
12.79
33.59
22.71
−2.91
29.74
28.19
26.31
29.92
23.36
20.79
20.91
15.95
71.82
31.87
67.19
60.41
47.45
35.17
72.62
40.22
13.46
46.05
21.44
34.55
48.84
9.12
45.23
28.03
41.10
40.76
31.69
17.18
26.71
21.05
268
M. Vimala Rani and M. Mathirajan
Fig. 2 Performance of seven different ATC-GHA for DRTS-SDF w.r.t. estimated optimal: TT,
TWT, NT, WNT, OTD rate, TEL, TWEL, and Lmax: over 450 problem instances
is computed using seven objective values, and then using these EOS as benchmark
solution, RPD is computed as per Eq. (44). Then, objective-wise, the score on ARPD
for (i) 10 problem instances over each of the problem configuration, and (ii) 450
problem instances are computed for seven different ATC-GHA for DRTS-NPDFMER. To keep this chapter brief, the results developed from the solution obtained,
similar to Table 6 and Fig. 2, are not presented here.
Similar to single diffusion furnace, IRANK performance score, while scheduling
DRTS-NPDF-MER over 450 problem instances, was computed to triangulate the
analysis with ARPD score. To keep this chapter brief, the computed IRANK of
seven different ATG-GHA for DRTS-NPDF-MER is not presented here.
ATC-GHA1
ATC-GHA2
ATC-GHA3
ATC-GHA4
ATC-GHA5
ATC-GHA6
ATC-GHA7
Proposed ATC-GHAs
Number of times the proposed ATC-GHAs for
DRTS-SDF yielded relatively best TT with the
rank position of
1
2
3
4
5
6
7
77
79
80
63
64
56
31
153
102
65
51
33
30
16
203
92
68
47
22
14
4
135
115
81
42
35
25
17
174
110
64
51
34
15
2
178
98
58
49
42
16
9
145
87
72
41
42
32
31
The ranking position, based on IRANK (over 450 problem instances),
of seven different ATC-GHA for DRTS-SDF, the scheduling objective
TT
TWT
NT
WNT
OTD rate
TEL
WTEL
Lmax
7
7
7
7
7
6
7
3
4
3
3
3
3
4
3
4
1
4
4
4
4
1
5
1
5
5
5
5
5
5
4
4
2
1
2
2
2
2
1
2
3
2
1
1
1
3
2
5
6
6
6
6
6
7
6
6
Table 7 Performance of seven different ATC-GHA for DRTS-SDF, over 450 instances, w.r.t. IRANK
270
M. Vimala Rani and M. Mathirajan
However, from the analyses of the ARPD scores with respect to configurationwise (10 problem instances per configuration) and irrespective of the configuration
(i.e. overall 450 instances), it is observed that by and large, ATC-GHA5 and ATCGHA6 are continuously performing better for the majority of the objectives in the
large-scale data. This observation was further endorsed by analyzing the IRANK
score. The better performing ATC-GHA, observed based on the empirical analyses
for both cases of DRTS-SDF and DRTS-NPDF-MER, are further confirmed by
statistical analysis, and the same is discussed in the next section.
6.2 Statistical Analyses on the Performance of ATC-GHA for
DRTS of Diffusion Furnaces
In addition to empirical analyses presented here, this study conducts statistical analysis using SPSS software. Accordingly, this study computes descriptive statistics:
mean, median, standard deviation, and 95% confidence interval, for seven different
ATC-GHA for DRTS of SDF w.r.t. every objective considered here, using 450
objective values obtained corresponding to 450 problem instances and is given in
Table 8. It is observed from Table 8 that ATC-GHA5 is continuously outperforming
for all the objectives. Moreover, ATC-GHA6 is also performing better for most of
the objectives.
Further, for each objective, this study desires to check whether the distribution of
RPD across seven different ATC-GHA for DRTS-SDF is the same or not. For that,
this study conducts Kruskal-Wallis non-parametric test, and the results are presented
in Table 9a, b. From Table 9a, it is observed that a statistically significant difference
is there in the distribution of RPD values across seven different ATC-GHA, as the PValues is 0.000, which is less than 0.05. In addition, Table 9b clearly says that ATCGHA5 and ATC-GHA6 are relatively outperforming heuristic algorithms among
seven different ATC-GHA for DRTS-SDF to optimize various objectives considered
here.
Similar to statistical analyses carried out on seven different ATC-GHA for DRTS
of SDF were performed on seven different ATC-GHA for DRTS-NPDF-MER.
Due to the brevity of the report, the statistical results obtained are not presented
here. However, the inference obtained from the statistical results endorses the same
inferences obtained with respect to seven different ATC-GHA for DRTS-SDF to
optimize various objectives. From both the analyses, this study detected that by
and large ATC-GHA5, and ATC-GHA6 are continuously performing better for both
DRTS-SDF and DRTS-NPDF-MER to optimize most of the objectives considered
in this study. This could be due to the job-priority-index and batch-priority-index
computed by these ATC-GHA that could yield an exact value of the slack. And this
helps to avoid getting high priority for low priority jobs or unavailable jobs. Further,
for the tardy jobs, these rules give a high priority to the job, which has the largest
weighted tardy.
TT
ATC-GHA1
ATC-GHA2
ATC-GHA3
ATC-GHA4
ATC-GHA5
ATC-GHA6
ATC-GHA7
ATC-GHA1
ATC-GHA2
ATC-GHA3
ATC-GHA4
ATC-GHA5
ATC-GHA6
ATC-GHA7
ATC-GHA1
ATC-GHA2
ATC-GHA3
ATC-GHA4
ATC-GHA5
ATC-GHA6
ATC-GHA7
NT
TWT
Scheduling Objective
Proposed ATC-GHA
Descriptive Statistics
Mean
Median
1392.99
407.00
1248.85
248.50
1209.36
239.00
1263.90
268.00
1225.41
239.00
1240.50
239.00
1322.84
278.50
7617.35
2169.00
5245.97
1235.50
5314.14
1169.00
5422.12
1393.50
5195.25
1137.00
5253.20
1137.00
5462.17
1405.00
30.61
17.50
25.98
13.00
25.99
13.00
26.42
13.00
25.80
13.00
25.46
13.00
26.46
13.00
Table 8 Descriptive statistics of the proposed seven different ATC-GHA for DRTS-SDF
Standard Deviation
2005.15
1879.97
1840.64
1919.33
1862.93
1876.87
2002.29
11048.96
7769.11
7996.89
8125.54
7834.27
7889.45
8115.24
32.44
29.17
29.30
29.51
28.97
28.58
29.42
(continued)
95% Confidence Interval
(1207.73,1578.25)
(1075.15,1422.54)
(1039.29,1379.42)
(1086.57,1441.24)
(1053.28,1397.53)
(1067.09,1413.91)
(1137.84,1507.84)
(6596.58638.2)
(4528.16,5963.79)
(4575.28,6053)
(4671.37,6172.87)
(4471.41,5919.09)
(4524.26,5982.13)
(4712.38,6211.97)
(27.61,33.61)
(23.28,28.67)
(23.28,28.69)
(23.69,29.14)
(23.12,28.48)
(22.82,28.1)
(23.75,29.18)
WNT
ATC-GHA1
ATC-GHA2
ATC-GHA3
ATC-GHA4
ATC-GHA5
ATC-GHA6
ATC-GHA7
ATC-GHA1
ATC-GHA2
ATC-GHA3
ATC-GHA4
ATC-GHA5
ATC-GHA6
ATC-GHA7
ATC-GHA1
ATC-GHA2
ATC-GHA3
ATC-GHA4
ATC-GHA5
ATC-GHA6
ATC-GHA7
TEL
OTD rate
Scheduling Objective
Proposed ATC-GHA
Table 8 (continued)
Descriptive Statistics
Mean
Median
168.45
97.00
137.35
68.00
138.69
70.00
140.84
70.00
137.12
70.00
135.48
70.00
141.47
74.50
0.34
0.26
0.46
0.40
0.47
0.40
0.46
0.40
0.47
0.40
0.47
0.40
0.45
0.40
1470.75
460.00
1399.99
452.00
1370.57
428.00
1413.53
440.00
1384.05
429.00
1402.19
431.50
1472.40
438.00
Standard Deviation
178.90
154.93
156.85
157.83
154.15
152.96
157.89
0.27
0.26
0.26
0.26
0.26
0.25
0.26
2010.13
1908.26
1869.04
1939.35
1889.40
1909.40
2027.01
(continued)
95% Confidence Interval
(151.92,184.98)
(123.03,151.66)
(124.2153.18)
(126.26,155.42)
(122.88,151.37)
(121.34,149.61)
(126.88,156.06)
(0.32,0.37)
(0.44,0.49)
(0.44,0.49)
(0.43,0.48)
(0.45,0.49)
(0.45,0.5)
(0.42,0.47)
(1285.03,1656.48)
(1223.68,1576.3)
(1197.88,1543.25)
(1234.35,1592.72)
(1209.48,1558.62)
(1225.78,1578.61)
(1285.12,1659.69)
ATC-GHA1
ATC-GHA2
ATC-GHA3
ATC-GHA4
ATC-GHA5
ATC-GHA6
ATC-GHA7
ATC-GHA1
ATC-GHA2
ATC-GHA3
ATC-GHA4
ATC-GHA5
ATC-GHA6
ATC-GHA7
Table 8 (continued)
Lmax
TWEL
8049.60
6119.09
6245.64
6283.41
6108.99
6181.25
6317.45
70.20
70.53
68.33
70.32
69.12
70.52
73.04
2613.00
2274.50
2338.50
2386.00
2270.50
2270.50
2319.50
55.00
50.00
49.00
50.00
49.00
49.00
55.00
11078.13
7951.30
8194.66
8260.75
8017.37
8090.68
8277.90
56.54
62.41
61.50
62.20
62.19
63.19
63.59
(7026.06,9073.15)
(5384.44,6853.74)
(5488.51,7002.78)
(5520.17,7046.65)
(5368.24,6849.74)
(5433.72,6928.77)
(5552.62,7082.27)
(64.97,75.42)
(64.76,76.29)
(62.64,74.01)
(64.58,76.07)
(63.37,74.86)
(64.69,76.36)
(67.16,78.91)
ATC-GHA1
ATC-GHA2
ATC-GHA3
ATC-GHA4
ATC-GHA5
ATC-GHA6
ATC-GHA7
Total
450
450
450
450
450
450
450
3150
N
0
0
0
6
OTD Rate
243.242
0
6
TEL
70.504
(b): Mean Rank for the seven different ATC-GHA w.r.t the Objective
TT
TWT
NT
WNT
OTD Rate
2060.41
2204.68
2032.64
2042.05
2165.02
1590.35
1511.50
1534.63
1525.30
1512.24
1350.76
1409.86
1451.84
1455.17
1432.73
1575.73
1567.92
1551.39
1558.45
1531.01
1362.28
1345.14
1423.02
1421.52
1391.54
1392.81
1373.55
1410.03
1419.16
1390.06
1696.16
1615.85
1624.96
1606.86
1605.89
0
0
6
6
6
6
WNT
160.027
Proposed ATC-GHA
KruskalWallis
H
Degrees of
freedom
Asymp.
Sig.
(a): Test Statistics for the Objective
TT
TWT
NT
213.202
293.054
165.270
Table 9 Kruskal-Wallis test results on seven different ATC-GHA for DRTS-SDF
TEL
1822.38
1561.60
1431.27
1562.18
1443.44
1488.08
1719.55
0
6
TWEL
2032.40
1504.32
1476.61
1546.32
1406.56
1451.08
1611.20
TWEL
147.988
0
6
Lmax
1768.72
1608.47
1420.86
1581.76
1432.56
1496.05
1720.09
Lmax
62.566
Prescriptive Analytics for Dynamic Real Time Scheduling of Diffusion Furnaces
275
7 Conclusion
A new research problem on DRTS of DF(s), considering the job and/or resource
related RTE along with different job-arrival time, machine eligibility restriction,
incompatible-job families, and non-agreeable release time & due-date to optimize
one at a time the objectives: TT, TWT, NT, WNT, OTD rate, TEL, TWEL, and Lmax
is considered in this study. (0-1) MILP models for DS-SDF, as well as DS-NPDFMER, considering each of the objectives, are proposed. LINGO Set Code for all
the (0-1) MILP models is also developed. All the proposed models are validated by
solving ninety small-scale data.
Since the mathematical models are not amenable to solve large-scale data, this
study proposed seven different ATC-GHA for (i) DRTS-SDF, and (ii) DRTS-NPDFMER. The performance evaluation of seven different ATC-GHA for (i) DRTS-SDF,
and (ii) DRTS-NPDF-MER is carried out in comparison with (a) the optimal value
for 90 small-scale data, and (b) the estimated optimal value for 450 large scale data,
which are generated based on the experimental design, using ARPD and IRANK
measure.
In addition to the empirical analyses, this study employs statistical analyses:
descriptive statistics and Kruskal Wallis test. From both the analyses, this study
concludes that, by and large, ATC-GHA5 and ATC-GHA6 are outperforming GHA
for DRTS-SDF as well as DRTS-NPDF-MER to optimize the objectives: TT, TWT,
NT, WNT, OTD rate, TEL, TWEL, and Lmax, one at a time. This could be because
the job-priority-index and batch-priority-index computed by these ATC-GHA that
could provide an accurate value of slack. This helps to avoid getting high priority
for low priority jobs or unavailable jobs. Further, for the tardy jobs, these rules give
a high priority to jobs which have large weighted tardy.
The important managerial implication stems from identifying better performing
ATC-GHA that would help the production manager to generate an efficient schedule
even if any unexpected real time events occur. Further, it helps to deliver the
product on or before its due date, which helps to improve the performance of the
semiconductor manufacturing industry.
The bench mark solution procedure: estimated optimal solution procedure has its
own limitations. Moreover, the problem instances used in this study are generated
not collected from SM industry. Considering (a) completion time-based scheduling
objectives, and (b) upstream/downstream operation(s) while doing DRTS of diffusion furnace(s) without the limitation listed above would be the future research
direction in this area.
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