Towards a Consumer-Oriented Supply Chain
by
Panagiotis Andrianopoulos
Master of Business Administration, ALBA Graduate Business School, 2014
Diploma of Advanced Engineering Studies, Mechanical Engineering, NTUA, 2011
and
Hector Rafael Perez Wario
Bachelor of Science Civil Engineering and Management, Universidad Panamericana, 2008
Bachelor of Science Civil Engineering and Built Environment, Hogeschool Utrecht, 2008
Submitted to the Engineering Systems Division
ARCHNES
in partial fulfillment of the requirement for the degree of
MASSACHMTTS
OF
Master of Engineering in Logistics
ECHNOLAL
at the
JUL 16 2015
Massachusetts Institute of Technology
LIBRARIES
June 2015
2015 Panagiotis Andrianopoulos and Hector Rafael Perez Wario. All rights reserved.
The authors hereby grant to MIT permissixTo reproduce and to distribute publicly
paper and electrpip copi'ethis document in whole or in part.
Signature redacted
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Towards a Consumer-Oriented Supply Chain
by
Panagiotis Andrianopoulos
and
Hector Rafael Perez Wario
Submitted to the Engineering Systems Division
On May 8, 2015 in Partial Fulfillment of the
Requirements for the Degree of Master of Engineering in Logistics
ABSTRACT
The current consumer products industry is primarily designed as a customer oriented supply
chain; this means that it is designed to fulfill orders from the distribution centers or stores of the
retailers. The question posed by retailers and vendors is how might a consumer (not customer)
oriented supply chain be defined and designed in a way which retailers and vendors could move
towards it in the future? Our methodology consisted of interviews with key stakeholders and
industry experts, a literature research, value stream mapping as well as data analysis of historical
sales and shipments between a retailer and a vendor that sponsored the project. As a result of our
research, we conclude that a consumer oriented supply chain is defined as a supply chain that is
triggered by consumer demand data and it implements strong collaboration between the retailer
and the vendor, in order to achieve a more efficient response to the consumer needs. A series of
interviews with key stakeholders revealed that one of the most important parts of the
collaboration is forecasting. Our data analysis depicts that a single, synchronized forecasting of
the consumer demand would help both parties operate in a more efficient and collaborative way.
As final deliverable we propose a roadmap with short-term and long-term steps necessary to
design a consumer oriented supply chain.
Thesis Supervisor: Dr. Alexis H. Bateman
Title: Research Associate, Center for Transportation & Logistics
2
Acknowledgments
We want to express our gratitude to Alexis Bateman, even during difficult and busy times in her
life as a young mother, she significantly contributed to this project. Also many thanks to
Francisco Jauffred, he helped us to unscramble the challenges of thesis and Chris Caplice for his
patience and guidance during the critical steps of this project. We would also like to thank the
people from the two sponsor companies that involved in making this thesis possible: Andre,
John, Ula, Martin, Sam, Jason and Geri. Special thanks also to our external advisors Larry and
Kai.
Firstly, I want to thank my thesis partner, Hectorinio, for this major journey. I would also like to
thank my father and mentor, Stamatis, for opening my horizons as broadly as to pursue my
dream of studying at MIT. Moreover, I want to thank Anna-Maria for her major daily support
that helped me tackle all difficulties until the next day. I also want to thank the rest of family,
Dimitris, Antonis and Anny, for knowing that they were and would be there for me.
Panagiotis
MIT has been a remarkable experience in my professional development, and this thesis provides
clear evidence of my journey for the last months. I want to thank my thesis mate, Panos, his
dedication and stubbornness were key factors in this thesis. I want to thank my family, specially
my parents. I dedicate this thesis project to the memory of my beloved uncle Architect Esteban
Wario Hernandez, his legacy is a source of continuous inspiration to the community of
Guadalajara.
Hector
3
Table of Contents
1
Introduction ............................................................................................................................
1.1
1.2
1.3
1.4
1.5
2
Literature R eview .................................................................................................................
2.1
2.2
2.3
2.4
2.5
2.6
3
4
M otivation......................................................................................................................................
Sponsor com panies........................................................................................................................
Problem definition.........................................................................................................................
Purpose and claim .........................................................................................................................
Report Structure .........................................................................................................................
Introduction.................................................................................................................................
Background..................................................................................................................................
Autom atic Replenishm ent System s .......................................................................................
Collaborative Planning ...............................................................................................................
O rder M anagem ent.....................................................................................................................
Consum er Interaction.................................................................................................................
7
7
8
8
9
10
11
11
11
15
16
17
18
M ethodology..........................................................................................................................
20
3.1
Data Collection ............................................................................................................................
3.1.1
Quantitative Data....................................................................................................................
3.1.2 Qualitative Data......................................................................................................................
3.2
Data Analysis ...............................................................................................................................
3.2.1
Quantitative Data Analysis.................................................................................................
3.2.2 Qualitative Data Analysis...................................................................................................
21
22
24
25
25
27
R esults, A nalysis, and D iscussion.....................................................................................
33
4.1
AS-IS Process...............................................................................................................................
4.1.1
M ap Construction Process...................................................................................................
4.1.2 AS-IS Value Stream M ap (VM I Process)..............................................................................36
4.1.3 A S-IS Value Stream M ap (Non-VM I Process) .................................................................
4.2
Connecting the Dots ....................................................................................................................
4.2.1
People .....................................................................................................................................
4.2.2 Processes ................................................................................................................................
4.2.3 Technology .............................................................................................................................
4.3
Forecast Analysis.........................................................................................................................
4.3.1
Analysis of SKU ...................................................................................................................
4.3.2 Analysis of SKU2...................................................................................................................
4.3.3 Analysis of SKU3...................................................................................................................
4.3.4 Analysis of SKU4...................................................................................................................
4.4
TO-BE Scenarios.........................................................................................................................
4.4.1
Hybrid Forecast Replenishm ent Process ............................................................................
4.4.2 Retailer-Level Forecast Replenishm ent Process .................................................................
4.4.3 Vendor-Level Forecast Replenishm ent Process.................................................................
4.5
Roadmap towards a Consumer-Oriented Supply Chain ...................................................
4.5.1
Short-term Steps .....................................................................................................................
4.5.2 Long-term Steps .....................................................................................................................
33
34
40
43
43
45
49
54
54
56
58
59
61
61
64
66
68
70
71
5
C onclusion .............................................................................................................................
73
6
R eferences .............................................................................................................................
75
4
Table of Figures
Figure 1.1 Retailers Lose in 59% of Out of Stock Incidences and Vendors in 48% (GMA/FMI)............ 8
Figure 2.1 High Level Communication Map.........................................................................................
14
Figure 2.2 Integrating concepts to build a Consumer-Oriented System ................................................
19
F igure 3.1 Sp iral technique........................................................................................................................
21
Figure 4.1 Value Stream Map Canvas Description................................................................................
35
Figure 4.2 Value Stream Map Symbols Explanation..............................................................................
36
Figure 4.3 AS-IS Value Stream Map (VMI Process) .............................................................................
37
Figure 4.4 AS-IS Value Stream Map (Non-VMI Process) ....................................................................
42
Figure 4.5 Integration of People, Process and Technology (Ireland & Crum, 2005)............................. 43
Figure 4.6 Revenues Reinforcing Loops from Replenishment Collaboration (System Dynamics Model) 48
Figure 4.7 JDA Flowcasting (JDA Software Group, 2014)...................................................................
52
Figure 4.8 Oracle Collaborative Planning (Oracle Corporation, 2014)................................................
53
Figure 4.9 SK U I Forecast Com parison ..................................................................................................
55
Figure 4.10 SK U 2 Forecast Com parison ...............................................................................................
57
Figure 4.11 SK U 3 Forecast Com parison ...............................................................................................
58
Figure 4.12 SK U 4 Forecast Com parison ...............................................................................................
60
Figure 4.13 TO-BE Value Stream Map (Hybrid Forecast Replenishment Process - Flexible Store
D ep loym ent Sy stem ) .................................................................................................................................
63
Figure 4.14 TO-BE Value Stream Map (Hybrid Forecast Replenishment Process - Non-Flexible Store
D eploym ent Sy stem ) .................................................................................................................................
64
Figure 4.15 TO-BE Value Stream Map (Retailer-Level Forecast Replenishment Process).................. 66
Figure 4.16 TO-BE Value Stream Map (Vendor-Level Forecast Replenishment Process).................... 67
Figure 4.17 The Roadmap towards a Consumer Oriented Supply Chain ..............................................
69
List of Tables
Table
Table
Table
Table
Table
Table
4.1
4.2
4.3
4.4
4.5
4.6
Top 20 supply chain management software suppliers (Gartner, 2013)...................................
Information Received for Data Analysis ...............................................................................
SK U I forecast m etrics ...............................................................................................................
SK U 2 Forecast m etrics ..............................................................................................................
SK U 3 Forecast m etrics ..............................................................................................................
SK U 4 forecast m etrics...............................................................................................................
5
51
54
56
57
59
60
List of Acronyms
ARS
B2B
CAO
CPFR
CPG
CRP
CV
DC
DTS
ECR
EDI
EOQ
ERP
IT
MAPE
PO
POS
QOH
RMSE
SCP
SKU
TMS
UPC
VICS
VMI
WMS
Automatic Replenishment Systems
Business-to-Business
Consumer Packaged Goods
Collaborative Planning Forecasting and Replenishment
Consumer Packaged Goods
Continuous Replenishment program
Coefficient of Variation
Distribution Center
Direct-To-Store
Efficient Consumer Response
Electronic Data Interchange
Economic Order Quantity
Enterprise Resource Planning
Information Technologies
Mean Absolute Percentage Error
Purchase Order
Point of Sale
Quantity of Hand
Root Mean Square Error
Supply Chain Planning
Stock Keeping Unit
Transportation Management System
Universal Product Code
Voluntary Interindustry Commerce Standards
Vendor Management Inventory
Warehouse Management System
6
1
Introduction
1.1
Motivation
Is a consumer oriented supply chain the future of the supply chain industry? Supply chain
managers of different companies around the world raise this question increasingly in discussions.
A consumer oriented supply chain is one that utilizes the actual demand of the final consumer to
generate feedback to the different stakeholders involved in a particular supply chain. A consumer
oriented supply chain opposes the traditional customer oriented supply chain that the majority of
companies implement. In a customer oriented supply chain, each stakeholder involved receives
feedback only from the next stakeholder in the chain, i.e. the manufacturer of raw materials from
the manufacturer of the finished goods, the manufacturer of the finished goods from the retailer
and so on. This practice is very popular among companies because it is easier and faster to
implement. However, in the
2 1st
century the evolution of the supply chain technology and the
ubiquitous presence of the Internet create new opportunities in the supply chain industry. These
facts create a new territory for collaboration among the different stakeholders of a supply chain
and make the vision of consumer oriented supply chains realistic.
Supply chain collaboration is of paramount importance for an efficient supply chain. A
successful supply chain brings the product to the final consumer in the right place, at the right
time and in the most cost efficient way. Out of stock is the indication of a non-consumer oriented
supply chain and penalizes significantly both the retailers and the vendors as, according to the
Voluntary Interindustry Commerce Standards (VICS) Association (2004), the study of Grocery
Manufacturers of America/Food Marketing Institute depict (Figure 1.1). More specifically, the
study reveals that in 48% of out of stock incidences, the vendors lose, while the retailers are
penalized even more, i.e. in 59% of the incidences.
7
Who loses when an item is out-of-stock?
DO Not Purchase
Item
1"%Dif"ft
loses
Substiue
-
Manufacturer
Bra"d
(48%)
Deiay Purchase
17%
loses
(59%)
-
Rtailer* RetailerSubsititute
2D%
Buhy ItemT at
Another Store
32%
Source: GMA/FMI Retail Out-o-Stocks Study
Figure 1.1 Retailers Lose in 59% of Out of Stock Incidences and Vendors in 48% (GMA/FMI)
1.2
Sponsor companies
This research takes place by the sponsorship of two companies. Company A, from now
on mentioned as "the retailer", is a multinational retail company that owns and operates
supermarkets in three different continents. Company B, from now on mentioned as "the vendor",
is a multinational consumer packaged goods (CPG) company with operations around the globe.
The companies have already established good business relations and implemented a vendor
managed inventory (VMI) system for the majority (80%) of the products that the vendor sells to
the retailer.
1.3
Problem definition
Both the retailer and the vendor realize the penalties of out of stock and lost sales. In their
traditional customer oriented supply chains, the retailer and the vendor work as single units and
invest money and time into technologies and processes that allow them to predict better and
8
respond more efficiently to the demand of their customers. In other words, the distribution
centers (DCs) of the vendor struggle to respond to the demand of the retailers' DCs, while the
retailers' DCs struggle to respond to the demand of the retailers' stores. Ultimately, the
traditional supply chain does not take into direct consideration the actual demand of the final
consumer, i.e. the point of sale (POS) data.
A number of studies indicate that the use of POS data in the demand planning process of
retailers and vendors create more accurate and predictive forecasts. This was confirmed through
interviews with Larry Lapide, research affiliate at MIT, and Kai Trepte, senior software engineer
at Harvard, who have expertise in this area. However, there is no broad research as far as the
interaction and the collaboration between the retailers and the vendors is concerned. Most of the
studies only consider improvements that a more responsive planning process has for a retailer or
a vendor. Yet, the major problem that we identified is the lack of meaningful communication
between retailers and vendors, as far as the replenishment process is concerned. This fact creates
a significant obstacle to consumer oriented supply chains.
1.4
Purpose and claim
Our research interest focuses on how the retailer and the vendor can collaborate to build a
more effective replenishment system with an ultimate goal to create a consumer oriented supply
chain. The use of POS data to create forecasts is only a step towards that goal. To take advantage
of these - more accurate and responsive - POS forecasts, the retailer and the vendor should
establish new business rules and initiate new forms of collaboration in terms of people, processes
and technology. Eksoz, Mansouri, & Bourlakis (2014) mention that according to Helms et al.
(2000), "collaborative forecasting is the practice in which the knowledge and information that
exists internally and externally is brought together into a single, more accurate forecast that has
9
the support of the entire supply chain". This single, synchronized forecast can be the base to
build a more responsive and efficient replenishment policy.
This project is particularly interesting because it involves both a retailer and a vendor;
rarely did a similar research incorporate both the inputs of a retailer and a vendor at the same
time. Our methodology consists of interviews with key stakeholders and industry experts,
literature research, value stream mapping as well as data analysis of historical sales and
shipments between the retailer and the vendor that sponsored the project. The results of our
research include the existing process of collaboration between the two companies, depicted in
value stream maps, as well as three different scenarios for future collaboration. The three
alternative scenarios indicate who will create the forecast and at what level. The scenarios are
evaluated based on data collection and forecast accuracy comparison. Based on the scenarios a
high level roadmap with long-term and short-term steps is suggested so as to move towards a
consumer-oriented supply chain.
1.5
Report Structure
In the sections that follow we firstly present our literature review based on which we
identified the environment of our research and narrowed our scope (section 2). In section 3 we
present the methodology we used in order to conduct our research and perform our analysis. Our
analysis and results are included in section 4, while section 5 and 6 presents our conclusions and
references respectively.
10
2
Literature Review
2.1
Introduction
In the past decade, inventory optimization, strategic replenishment and order
management have experienced tremendous advancement. For instance, traditional supply chains,
often full of uncertainties and ambiguities, have evolved into collaborative environments,
resulting in economic and social benefits. The supply chain between a retailer and a vendor
presents a wide range of opportunities due to its complex nature. In particular, this type of supply
chain is deeply influenced and indirectly managed by the consumer.
2.2
Background
One of the first companies that understood this advantage was P&G (Clark and
McKenny, 1995). The Continuous Replenishment program (CRP) changed that entire value of
the chain, driving orders based on distribution centers withdrawals and sales data. P&G
implemented the approach with several retailers, resulting in dramatic results in both lowering
inventories and increasing in-stock at retail. This program provided the foundation for Efficient
Consumer Response (ECR) and Collaborative Planning Forecasting and Replenishment (CPFR)
programs that are still used today (Taylor, 2004). However we can predict that the benefits
harvested from such innovations will come to an end (Bohlen, Beal and Rogers, 1957), therefore
the challenge will be to reengineer the process of replenishment itself at its core and reorganize it
as one common procedure between the retailer and the vendor.
The industry has faced challenges before, the major market developments that make retail
challenging started in the 1990s and still are prevalent today, namely high cost pressure, shorter
innovation cycles, increasing consumer expectations and globalization (Baumgarten & Wolff,
11
1993). Currently, the market expansion has also brought saturation, especially for the Consumer
Packaged Goods (CPG) market.
Given the research question, how can we move towards a consumer-oriented supply
chain in the CPG market? And what benefits and new challenges will bring to the vendor-retailer
relation? This research examines these concerns by analyzing a major CPG vendor and a food
retailer. The results of the study will provide a framework for companies in the CPG market
aiming to implement this novel approach as a business strategy. The research questions focus on
the steps that CPG companies should follow to prepare for change and future industry trends
concerning retail replenishment.
Currently there are a few relevant academic sources examining the benefits and
challenges of a consumer-oriented supply chain. Some studies have described, evaluated and
applied consumer-oriented technologies to different industries and with different purposes.
Therefore, our literature review is concentrated on collaborative innovations across four different
perspectives that together define a consumer-oriented supply chain. The four areas are automatic
replenishment systems, collaborative planning, order management, and consumer interaction. It
is important to note that these areas focus on minimizing stock outs, the most frequently
mentioned cause of frustration for dissatisfied customers in retail (Sterns, Unger L. S., & et al.,
1981) and reducing the bullwhip effect.
A consumer-oriented supply chain has the motivation to constrain and ultimately reduce
the problem of the bullwhip effect. The meaning of the bullwhip effect is well described by
Fransoo & Wouters (2000), Lee et al. (1997) and Posey & Bari (2009) as the phenomenon in
which the quantity of orders increases as one moves up the supply chain due to the additional
safety stock at each stage. Therefore, the quantity of orders of upstream firms is much higher
12
than consumer demand. Simultaneously, the quantity of consumer demand is only known by the
retailer (Hohmann & Zelewski, 2011). Lee et al. (1997) state that there are five fundamental
causes of bullwhip: demand signaling processing, non-zero lead times, price variations, rationing
and gaming, and order batching (Disney & Towill, 2003). Based on preliminary feedback from
the sponsor companies, we want to add one more that is crucial. That is the lack of real
communication among the different parts of the supply chain. For example, in the worst case, the
supplier is not aware that a stock keeping unit (SKU) has been eliminated from a category in the
retailer side, until the distributor returns the last big shipment as obsolescent (Holmstrom,
Framling, Kaipia, & Saranen, 2002). This lack of communication prevents the supply chain from
being responsive to changes.
Figure 2.1 depicts the high level processes/communications that take place in the product
flow from the vendor manufacturer plants towards the end of the chain or once the consumer
takes possession of the product(s). This figure illustrates the complexity involved in the
"obvious" process of replenishment / fulfillment, several stakeholders and systems continually
monitor the state of the system, triggering the switches to move product downstream, while the
information flows upstream, represented with the red arrows or communication flows.
13
ORDER OVERVIEW
Stakeholders
Infrastructure
Systems
- p
E=*
Communication
Product flow
Figure 2.1 High Level Communication Map
The need for better communication has been in the center of many past efforts between
retailers and vendors to create a more efficient supply chain. The most common is that of Vendor
Managed Inventory (VMI). VMI involves the vendor making the replenishment decision for
products supplied to the retailer based on specific inventory and supply chain policies embedded
in the contract between the retailer and the vendor. Practically, the vendor will receive logistics
information from the retailer (inventory position, service level etc.), create the purchase order
and ship the products to the retailer. The VMI process faces a number of challenges in order to
be successful. According to Angulo, Nachtmann, & Waller (2004) different incentives and
performance measures exist between the vendor and the customer, confidentiality and trust
issues arise, technology investments and expenses appear, inventory ownership issues should be
resolved, and antitrust regulations apply. Of course, when supply chain members overcome these
14
issues and jointly define the information to be shared in establishing a VMI partnership, the
&
vendor is faced with the challenge of using that information effectively (Angulo, Nachtmann,
Waller, 2004). In the same paper the authors conclude that retailers should not let information
inaccuracy slow them down in their VMI implementation (Angulo, Nachtmann, & Waller,
2004). Current trends show the validity of this statement, especially in today's business world
where information gathering, even at the Point of Sale (POS) level, is relatively easy with
software and hardware collecting and sharing tools. However, it is difficult to agree with the
second conclusion of Angulo, Nachtmann, & Waller (2004) that retailers should carefully audit
the replenishment processes of VMI suppliers to ensure they will be using shared information in
a timely manner, in today's advance technological environments, new software development
reduces the need of burdensome auditing.
2.3
Automatic Replenishment Systems
The Continuous Replenishment program was the foundation of the Automatic
Replenishment Systems (ARS); this technology is one of the most widely applied methods to
increase customer responsiveness. However, automation is not a new concept for manufacturers.
Kodak and Epicor have used ARS which eventually turned into lean manufacturing back in
1980's. Inventory Replenishment has been a core functional requirement in software systems
since 1970 (Broekmeulen & Donselaar, 2005). However, the concepts of automating the
replenishment and essentially pulling the product through production without human intervention
are revolutionary. As a result, improvements in conventional manufacturing systems, and their
respective replenishment, have been the focus of many academic studies. These sources have
tried to identify the optimum condition using different methodologies, from the Economic Order
of Quantity (EOQ) to simulation models (Silver, Pyke, & Peterson, 1998). However recent
15
studies have coined the Automatic Store Replenishment (ARS) term, an evolution of automation
in automobile manufacturing and information integration (Norman Gotz 1999). ARS promises
to decrease the number of stock outs while simultaneously reducing store-handling costs. A
recent study in ARS analyzes the impacts of the stores and introduces a descriptive model that
provides valuable understanding of the differences between manual and automatic systems
(Corsten & Angerer, 2007). However this study does not identify the effect on consumer
perception and value regarding responsiveness and resilience.
Responsiveness is the best way to deliver product directly to the store. An MIT thesis
published in 2014 posed the potential of switching the delivery method from a 100% Distribution
Center (DC) method to 100% Direct-To-Store (DTS) method (Panditrao & Adiraju, 2014). This
study implies an increase in consumer orientation and level of service improvement, although it
is focused almost exclusively in the transportation benefits and the reductions of safety stock. In
section 4, we discuss the relevance of transportation in the implementation of collaboration
practices; in fact the transportation network flexibility can define the ideal processes and
strategy. Transportation is a trigger that needs to be considered as part, not purpose, of the future
consumer oriented supply chain.
2.4
Collaborative Planning
CPFR is by far the most advanced technique that integrates visibility of data through the
entire supply chain. Visibility enhances transparency in supply chain and reduces the common
bullwhip effect. The CPFR model is not new and although it can provide many benefits, there
have been many failed implementations (BUydk6zkan & Vardaloglu, 2012). For the purpose of
our study, this perspective gives us an understanding of the past, current and future
communication flows and also the barriers generated by any flaw in the system. In the
16
competitive environment of CPG there is a lot of pressure to meet high customer service levels
while minimizing the costs. Effective demand planning and collaboration facilitates this balance
(Moon, Mentzer, & Thomas Jr., 2000). The value of information among players in a supply
chain is something that has not been fully studied in terms of consumer value. Electronic Point of
Sales (POS) systems are very valuable technological advancements; these systems capture and
transmit data to suppliers using Electronic Data Interchange (EDI). CPFR could combine POS
and EDI with the advantage of the internet; trading partners use centralized servers to view and
update shared plans and forecasts (Taylor, 2004). Accurate demand forecasts are critical to
maintain consumer service levels.
The use of CPFR and POS data is clearly exemplified in a study for a 10 ready-to-eat
cereal stock-keeping units (SKUs) from 18 regional U.S. grocery distribution centers, this
research investigated collaborative forecasting issues and presented a value proposition for the
use of shared information (Williams & Waller, 2011). The research demonstrates empirically
that the use of shared POS data through the entire supply chain could further improve
performance through improved consumer service and reductions of excess inventory. The issue
with the empirical analysis is that it is subjective with certain assumptions, and some initial
variables are randomly assigned, in addition it doesn't quantify the real consumer improved
value.
2.5
Order Management
The initial focus of the thesis was on the "orderless supply chain". This concept is so
novel that only a small amount of papers have mentioned the concept and with limited
applications. After extensive research we found that orderless can be interpreted as a
simplification of internal processes with the aim to reduce costs, improve service and
17
consequently increase sales (Angerer & Corsten, 2004). Two authors, Cannella and Ciancimino
discussed that the impact of supply chain collaboration is greater than an order simplification or
orderless. Order smoothing mitigates the bullwhip effect, but it may have a negative effect on
customer service, thus damaging the consumer oriented approach (Cannella & Ciancimino,
2010). This study reshaped our research efforts and proved that the initial approach of an
orderless oriented supply chain was not aligned with the consumer focus.
2.6
Consumer Interaction
In the last part of our literature review, we examined the processes that suppliers and
retailers implement to attract and affect consumer perception to increase demand. A promotion is
the main technique to interact and affect consumer behavior. The study by Rajagopal on
promotions and simulation buying in retail stores, builds arguments around attractiveness of
promotions using POS data, and the effectiveness of consumer service as a tool for gaining a
competitive advantage in the retail business environment (Rajagopal, 2008). There is limited
literature that combines the complexities of promotions, the value of information sharing,
visibility and collaboration with responsiveness of consumer behavior. This study provided
insights about collecting POS data and implementing promotions; the main limitation is the
geographical dissimilarity because the study is based on the Mexican CPG market.
Our research found that there is a limited amount of academic studies that
comprehensively address strategies and tools to move towards a consumer-oriented supply chain
in the CPG industry. In fact, the consumer-oriented term is not extensively used as the other four
terms that we explored in our literature research which are Consumer Interaction, Order
Management, Collaborative Planning and Automatic Replenishment. We concluded that if we
combine these four terms, we end up with the theoretical scheme of a consumer-oriented supply
18
chain. As depicted in Figure 2.2, the combination of the four main topics of this thesis form the
foundations of the future Consumer-Oriented System, in the following section we identify a
methodology to integrate these perspectives into an analytical perspective.
CONSUMER-ORIENTED SYSTEM
Figure 2.2 Integrating concepts to build a Consumer-Oriented System
19
3 Methodology
The initial approach is to understand and analyze a single part of the existing supply
chain system. The intention is to clearly define and identify the potential opportunities for
improvement, and then expand the findings and potential solutions to the rest of the scope. This
approach allows us to visualize the relations among different stakeholders and the information
flow more clearly and concisely. Some researchers define this methodology as the spiral
technique (Boehm, 2000). The main reason to use this approach is essentially because of the
ambiguity and wide extension of the problem, since it includes a great proportion of the supply
chain between the supplier and the vendor.
The initial approach in the spiral method was to select one category of products, one
relevant category for both Vendor and Retailer. The findings, explained in the Data Collection
part, showed that laundry category is relevant in terms of volume and revenue, both sponsor
companies agreed with this, and our conclusions with this category can then be adopted into the
rest of the product categories and applied on a broader scale. The spiral technique, described in
Figure 3.1, is a comprehensive approach that allowed us to learn with every step we took in our
methods. Furthermore, the knowledge gained reinforces and reshapes past and future procedures
in our methodology, this means that with every insight, discovery or mistake the mental model
evolves and our perceptions change to new conditions, this eventually creates a clear and concise
picture of the challenge, as depicted on chapter 4 in Figure 4.3 and Figure 4.4. From the data
collection, to the future value stream maps, spiral method has been a proven method of constant
success in complex problems. Apart from the spiral method, our methodology consists of two
parts: Data Collection and Data Analysis.
20
Figure 3.1 Spiral technique
3.1
Data Collection
Quantitative and qualitative data was collected from internal and external sources. The
Vendor and Retailer, sponsors of this thesis, use different tools to measure and store data.
Moreover, even within companies, sometimes divisions employ different software and
techniques. In section 4.1 there is a relevant representation of the different systems, used by
Vendor and Retailer. One of the initial fears was the challenge of assessing data from two
different parties. However thanks to the development and integration of Information
Technologies, the exchange and analysis of data can be a simple process, in chapter 4 we
21
discussed the integrations of technological platforms that companies can leverage to further
increase their collaboration.
3.1.1
Quantitative Data
Quantitative data were necessary to support the forecast analysis. In this analysis
three types of quantitative data were collected and used: forecasting, replenishment and
point of sales (POS) data. To simplify the collection and analysis process, the data came
from one category of products. A challenge was to identify a representative category, one
that could potentially extrapolate the insights and conclusions to the entire portfolio of
products.
Forecasting Data
The vendor and the retailer use different forecasting techniques and systems.
Forecast methodologies can vary significantly from one platform to another. It is difficult
to assess the effectiveness of their techniques, and it is not part of the scope of this thesis.
We assumed that technological platforms share capabilities and functions, and we can
rely on this data. Section 4.3 presents the results of this analysis and it is possible to
visually compare forecasts from the vendor and the retailer. However these comparisons
do not represent a feasible analysis of the effectiveness of the different techniques, but
help us understand the importance of the information used to draw those forecasts.
The collected forecast data exposed two main differences in the forecast strategies
of the sponsor companies. The vendor reports the forecast on a weekly base for a
determined horizon; every week the vendor updates the forecast for the subsequent weeks
according to their perception of demand and the information sharing with the retailer. On
the other hand, the retailer generates reports every working day, Monday to Friday. These
22
reports have a 26 week forecast projection of demand including the current week's
demand. The forecast projections are made per Universal Product Code (UPC), i.e. single
product.
Replenishment Data
Replenishment or order fulfillment is the number of items that moved from a
distribution center to another a store. The replenishment data indicates the final decision
of the number of items to ship based on the forecasts created. Replenishment data show
the degree of collaboration, if any, and the responsiveness of the fulfillment system. The
vendor and the retailer provided replenishment from the vendor's plant or distribution
center to the retailer's DC, and from the retailer's DC to the retailer's stores respectively.
The replenishment data is reported on a daily basis including the following information:
UPC, Item Description (Primary), Date, Quantity Sold (in the case of the vendor to the
retailer), Items Shipped (in the case of the retailer to the stores).
POS data
The most critical input was the Point of Sales data. This information serves as a
comparison tool to measure the performance of the separate forecasts and also gives
insights about the replenishment policies and the general behavior of the order and
fulfilling management. POS data represents reality, i.e. the consumer preferences. The
access to this information is limited and becomes large as we move upstream in the
supply chain. Fortunately the retailer stores the POS data using a third party service, this
makes it accessible anytime, anywhere. The third party service brings a web-based
platform that generates reports based on the criteria established by the user - a vast
number of attributes can be selected. For our analysis we exported weekly data with the
23
following attributes: UPC, Item Description, Date range, Total Sales Amount and Total
Sales Volume Units. The object of the quantitative study is to focus on the forecast and
replenishment, sales volume is critical to determine the performance of these two
processes, the other attributes were used for the matching tasks and selection of SKUs.
3.1.2
Qualitative Data
To better understand the existing process of replenishment between the retailer
and the vendor, a number of interviews were held with key stakeholders. In addition we
visited the premises of the retailer (store, DC, Headquarters) in October 2014 with the
intention of obtaining a better sense of the operations and the different parts involved in
the retailer's supply chain. Our interview schedule included:
" Vendor, VMI and non-VMI replenishment
" Vendor, promotions and playbooks
" Retailer, category management and replenishment
" Retailer third party IT, Data capture and extraction
" Weekly VMI calls, Retailer and Vendor, VMI coordination and feedback
" Bi-weekly joint calls, Retailer and Vendor, project status updates
Additionally, we conducted interviews with Larry Lapide, research affiliate at
MIT, mentor and opinion leader in the field of supply chain replenishment and Kai
Trepte, senior software engineer at Harvard, experienced professional in the field of
supply chain collaboration.
24
3.2
Data Analysis
In this section we expound the strategy for data collection and quantitative analysis. The
intention of this analysis is to retrieve insights of the replenishment process and the overall
performance of its participants.
3.2.1
Quantitative Data Analysis
The retailer and the vendor follow strict and complex tools and procedures for the
forecast and replenishment processes. However, the complexity of the tools doesn't
necessary mean better accuracy. In chapter 4, we will discuss technology and the trends
that point towards integration and simplification of platforms. In addition to new
technologies, a quantitative analysis of existing forecast and replenishment conditions
strengthens our main deliverable, the roadmap, and the final conclusions.
Segmentation Strategy
With thousands of different products, the segmentation and selection of SKUs for
analysis becomes a challenge. Selective Inventory Control or ABC analysis is a
segmentation strategy that divides SKUs into three different categories according to its
volume and monetary value. ABC analysis is similar to the Pareto principle which states
that, for many events, roughly 80% of the effects come from 20% of the causes (Lysons
& Farrington, 2006). In this case the class A items, which require more managerial
attention, represent 20% of the SKUs and bring 80% of the effects. Class B items or the
middle share represent 30% of SKUs with 15% of the value. Finally, class C items are
nearly 50% of the SKUs with 5% or less of effects. To extrapolate effectively the results,
the data was extracted for three class A and one class B products, chosen from the
category laundry.
25
Time Horizon Selection
Establishing the time horizon is another critical component of the analysis
because not all the products have the same life cycle. We received partial data from 2013,
full data from 2014 and the beginnings of 2015. During 2013 and 2014 several SKUs
where either introduced to the market or discontinued. Additionally, like in any other
large database, there are discrepancies and errors, creating additional boundaries for the
horizon selection. Some of the initial selected SKUs were not robust enough in terms of
data continuity to establish a solid horizon, therefore the horizon analysis was another
factor considered in segmentation strategy. After manual verification, the horizon for our
study is the 26-week period from December 29, 2013 until June 28, 2014.
Comparison
Visual comparison is a standard methodology to compare complex phenomena,
such as the forecast and replenishment of two collaborative partner companies. For
visual comparison we are using standard two-plane graphs that provide a clear and
concis
C0umparisOn 01 the friecast,
replenihmenIt anud POS data.
Metrics
For data comparison, the use of statistic metrics has proven a consistent and
reliable universal method. The Mean Absolute Percentage Error (MAPE) and the Root
Mean Square Error (RMSE) are widely used and recognized in the industry. MAPE
measures the accuracy of the forecast compared to the POS. The closer a MAPE value is
to zero the more accurate the forecast technique. RMSE measures the difference between
the predicted and the real values; RMSE is the standard deviation of the sample. The
formulas for these metrics are:
26
Future Value Stream Mapping & Roadmap
The final objective is to design a future value stream map, which will incorporate
the potential improvements mainly in the information flow part. A main part of the future
improvement could be the utilization of point of sale (POS) data, which is created in the
store cashier scanners, to trigger replenishment. The future value stream map will intend
to eliminate any unnecessary existing flows of information as well as replace existing
flows with updated processes that will help the companies experience efficiencies and
responsiveness in their supply chain. In contrast to the existing value stream map, the
future value stream map is built in general terms, this map includes both the changes in
the physical, as well as the information flow, a high degree of detail in the future value
stream map is not considered as part of this study.
After identifying the potential future state, the final part of our methodology
presents the modifications and changes that need to take place to reach that future state.
These recommendations could be in terms of processes, technology and people, depicted
in chapter 4 as Roadmap.
32
n
n
MAPE =
n t=1
RMSE=
At
- (At - F,)2
n t=1
where At is the POS data, Ft the forecast and n the number of values.
In addition to the error metrics, we employed the coefficient of variation (CV).
This is a standardized measure of dispersion, defined as the ratio between the standard
deviation and the mean of a sample. In our analysis, the coefficient of variation
determines the volatility of the demand or POS data. It is commonly represented as a
percentage or decimals, the closer the value to zero the less dispersion and variability.
The formula for the CV is:
-
CV =
where a is the standard deviation, and t is the average or arithmetic mean.
3.2.2
Qualitative Data Analysis
The objective of this thesis is to depict a future consumer oriented supply chain
that can in part be achieved by increasing collaboration. However, assessing collaboration
is difficult, especially if we only try to understand only with numbers. A qualitative
analysis provides the insights and understanding that is difficult to evaluate with
quantitative methods. The core components of the qualitative analysis are:
" Existing Value Stream Mapping
"
System Dynamics Representation
*
Future Value Stream Mapping & Roadmap
27
The Existing Value Stream Mapping is a tool that illustrates the existing process
of replenishment between the retailer and the vendor. This process helps visualize, in a
meaningful way, the current flows of product and information (Martin & Osterling,
2013). The second tool, the System Dynamics representation, is a systems thinking
methodology that enables us, through a causal loop diagram, to depict a holistic view of
the information and product flow, and feedback loops created in the system (Sterman,
2000). This approach increases our understanding of the current state and what
improvements in terms of processes, technology, and human resources could take place.
The third part of the qualitative analysis is the Future Value Stream Mapping. In this
section a high level redevelopment of the Existing Value Stream Map depicts the
proposed model of replenishment between the retailer and the vendor. This will help both
companies understand how their future collaboration could work. In this part we are
going also to present the short-term and long-term steps that the companies should follow
in a high level in order to shift from the existing to the future state.
Existing Value Stream Mapping
The use of a value stream map is the most suitable approach to represent the
current process of replenishment as well as the future state to incorporate potential
improvements. According to Rother & Shook (2003), a value stream consists of the total
number of value added and non-value added activities that take place in order for a
product to get through the flows of design and production from raw materials into its final
consumable form. According to Tabanli & Ertay (2013), Tapping D, Luyster T, Shuker T
(2002) consider value stream mapping as a tool that companies utilize in order to create a
map of material (physical flow) and information (information flow) of a product or a
28
process. For this thesis a value stream map is a tool that allow us to depict the current
process of replenishment between the retailer and the vendor in a clear and structured
way. This current picture would be the starting point in order to build the future map. The
process of improvement would be both in terms of identifying the waste of non-value
added and not necessary activities (muda, i.e. waste), as well as adding new beneficial
elements/ communications. Depicting the current and future state map allows us to realize
the necessary steps to reach the desired future situation; in other words will help us
identify the roadmap of how to move from the current to the future state.
The current state map of the information flow would utilize the gathered
information and data from the interviews we carried out with different stakeholder of the
retailer and the vendor in order to depict in a concise and meaningful way the steps that
allow the products to flow from the plants of the vendor to the stores of the retailer. The
current state map would include the physical flow of goods as well as the information
flow between the different stakeholders. We created two different versions of the current
flow map, aligning to the two different processes that the retailer follows. The difference
is based on the replenishment practices that the retailer exercises with the supplier. For
the majority of the products of the particular supplier, the retailer implements a vendor
managed inventory (VMI) process. This means that the purchase orders (PO) are
generated by the vendor, not the retailer. The vendor is the one responsible for receiving
all the required information, performing the right analysis and fulfilling the replenishment
of the retailer's distribution centers (DCs) meeting the contracted service level and
inventory level. On the other hand, the existing value stream map of the more traditional
replenishment practice is created. This traditional approach is implemented on the rest of
29
the products of the supplier. In this approach the retailer is responsible for gathering all
the necessary data, performing inventory analysis and creating the required PO to send to
the vendor in order to help the retailer meet its demand efficiently. Consequently, we
create two existing value stream map and one depicting the VMI process and one
depicting the traditional (non-VMI) process.
The next step was to identify the desired future of the replenishment process
between the retailer and the vendor. System thinking is a method that can enhance our
perception of the situation.
System Dynamics Representation
This project addresses the interaction of different systems such as replenishment
processes and policies, forecasting models, communication and interaction between the
retailer and the vendor, and the information flow created by complex integrated and
interdependent systems. Systems thinking is an approach to visualize problems
holistically. Systems thinking has its roots in General Systems Theory, a work developed
by the biologist Ludwig von Bertalanffy in the 1940's, continued by Ross Ashby in the
1950's and extensively developed by Jay Forrester at MIT (Forrester, 2013). Systems
thinking is a mental strategy to build models with nonlinear interactions to understand the
components of the system in the context of relationships with other components or other
systems.
System dynamics combines the inevitable change with the notion of time in one
single approach, and how the state of our system creates behavior (Sterman, 2000). In this
thesis, demand is an uncontrollable force, affected by several constraints. Demand
evolves with time and it can be considered a dynamic element. In the law of economics,
30
the supply, price and other controllable factor can affect its behavior and increase its
predictability.
Every system is affected by a rate of change, which in the system dynamics world
is called flow. Some components of the systems can be measured at any time, and this is
called a stock. The main differences between these two crucial components of models is
that one directly affects the other. The principles of system dynamics can help us to
understand the different feedback loops of complex issues. These models are mainly used
for simulation analysis and eventually to create or revise policies. This methodology is
used to depict the interaction of stakeholders and visually help us identify causal loops
that are creating certain behavior. A causal loop is a map that shows the causal links
among variables with arrows from a cause to an effect. Causal loop uncovers hidden
feedbacks that seem not so obvious to our non-system thinking methodologies.
One of the main purposes of this thesis is to create a blueprint or roadmap that
depicts the interaction of different stakeholders and to identify room for improvement.
System dynamics can provide that level of insight and can be easily understood by nonacademics. There are a minimum number of research studies that employ system
dynamics approach in addressing the problem. On the contrary, system dynamics is more
a complementary method that creates awareness of the magnitude of a problem and
potentially depicts solutions. System Dynamics is a quantitative method, although our
intention is not to simulate the model but understand holistically the forecast and
replenishment processes.
31
4
Results, Analysis, and Discussion
In this section we present the results of our project as well as the analysis and discussion
on them. Firstly, we present and elaborate on the existing ("AS-IS Process") value stream map,
i.e. the map that describes how the retailer and the vendor collaborate. Then we present the
discussion ("Connecting the Dots"), i.e. the results of our literature research that led us to
propose the alternative scenarios towards a consumer oriented supply chain. After this we
present a descriptive analysis of the forecast comparison ("Forecast Analysis") followed by the
discussions on the future value stream maps ("TO-BE Scenarios"). Ultimately, all the previous
analysis and synthesis helped us create a path towards a consumer oriented supply chain
("Roadmap Towards a Consumer-Oriented Supply Chain").
4.1
AS-IS Process
A Value Stream Map is a powerful tool to show the condition of business operations. In
the context of a consumer oriented supply chain, the value stream map allows us to understand
the existing process of replenishment between the retailer and the vendor. This can be done by
depicting the existing flow of information and product between the different stakeholders that
engage in the replenishment process from both companies. The existing process map, or AS-IS
map, shows the crucial interaction among people, process and technologies and helps to identify
misconnections, flaws and delays. It also gives visual insights and ideas of possible corrections
and improvements to the system. Additionally, the map can function as a strategic management
tool, an agreement document not only between the retailer and the vendor, but also among
different members of the same organization.
33
4.1.1
Map Construction Process
In order to make our value stream map more structured and understandable, we
first designed the value stream map canvas (Figure 4.1). The canvas has a meaningful
horizontal and vertical area separation:
" The horizontal lines create three distinct areas:
-
The upper area is that of the physical (product) flow from plants to end
consumer (black color)
*
-
The middle area is that of the software and computer systems (blue color)
-
The last area is that of human interaction (red color)
The vertical lines (swim lanes) create four distinct areas:
-
The left swim lane represents the plants and distribution centers (DCs) of the
vendor
-
The next swim lane to the right represents the DCs of the retailer
-
The next to the right the retail stores
-
The last one the final consumer
Ultimately, by combining the horizontal and vertical lines, the canvas is separated
into meaningful areas that intuitively help understand the "position" of the area in the
supply chain (vendor, retailer, consumer) and the content of the area (product interaction,
software interaction or human interaction).
34
Value StrE am Map Canvas
Product Interaction
W)
C
E
E
E
E
IA
0
0
A1
C)
C
0
U
Human~~ Intrcto
Figure 4.1 Value Stream Map Canvas Description
The symbols that we used to create the value stream map are depicted in Figure
4.2. We utilized boxes to represent key stakeholders and important elements in the
physical and information flow. The colors of the boxes represent the content areas that
they belong (as described above). We utilized arrows to depict the different flows. The
black arrows represent the physical (product) flow, while the blue arrows represent the
information flow.
35
Map Symbols
Retailer or Vendor Facility
Software or camputof
ystem
Human Interaction
a
-
Physical (Product) Flow
Information Flow
Figure 4.2 Value Stream Map Symbols Explanation
4.1.2
AS-IS Value Stream Map (VMI Process)
Following the spiral technique as described in the methodology section (Section
0) we initiated the construction of the AS-IS value stream map following the
replenishment process for the laundry category. The replenishment of the laundry
category is fulfilled utilizing the Vendor Managed Inventory (VMI) method. Through
interviews with the stakeholders, we understood that for all VMI product categories the
process of replenishment is the same. The process is depicted in the following value
stream map Figure 4.3.
36
VMI Replenishment Process
Retailer
Distribution
Vendor
Distribution
Vendor
Center
Center
M AL
VMI Po
*
store pull-s - - - - - - - - - - - - - - - - - - - - - - - - -
-
---
i ED[ 856
R M PO #2
EA
4M.P
Cons ume
Pqan Stores
store pulls
dail
E RP #1Tool
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in
ut
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CP (Turn &I
Promno)
Daily
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(1week)
E
nToo!
Prm
~-management, pricing,-
info
VMI Analyst
4weeks)
merchandising dpt
F
merchandising
agsnr
ong-term
(2e forecast VIV
Feedbck
0-
Planogram
#1b
Forest
AnlystFA edo
red flags
Anyst
(FA)
---
-
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#2
-
P
0
An0s
exceptions (3w)
Store Analyst
0
(3A)
ssociate
Cashier
The replenishment process initiates from the moment a consumer buys a vendor's
product in a retailer's store. The transaction is registered by a cash register POS system,
modem systems allow the consumer to automatically checkout and pay for products,
removing the need of an associate; nevertheless cashiers supervise operations and
reliability of the system. At the retail level, a Computer Assisted Ordering (CAO) system
is the mainstream technology for replenishment of the stores. CAO is composed of two
separate systems, one at the store level and another one at the DC level. The one at the
store feeds the DC system with critical inputs that eventually result in a suggested
replenishment order.
At the store side, Tool #la retrieves POS data from registers every 30 minutes,
Tool #Ia provides information for stock, quantity on hand (QOH) and sales. At the DC
side ERP #1 gets daily information from Tool #1 a. ERP #1 also needs established
planograms from category mangers, sales history, price, promotions and delivery
schedules to generate a weekly demand forecast. With the forecast and inventory
position, it is possible to determine if replenishment is necessary and with this an order.
CAO generates this order and provides access and visibility to the store to confirm the
validity of the information and make any changes. If no changes occur the order is
transmitted to the DC for picking of the products.
At this point it is important to make a product distinction. A product can be
characterized as turn (already established product with no current promotion),
promotional or new launch. The replenishment process is different among these product
categories as far as forecasting is concerned. Tool #1 c is the forecasting software that the
retailer utilizes. The main purpose of this software is to create forecasts (for the turn
38
products) and help the forecast analysts of the retailer to forecast (for promotional
products, new launches and other exceptions). The main input to the software is the store
pulls, i.e. the actual deliveries of products from the retailer's DC to the retailer's stores.
Another input to Tool #1 c is the promotional information (this information is
communicated by another software called Tool #Ib). The category management, pricing
and merchandising departments of the retailer create the promotional information at the
first place. Tool #1 c uses the historical demand of the retailer's DCs (orders of the
retailer's DCs to the vendor's DCs) and the promotional information to create a threeweek forecast per product (Stock Keeping Unit - SKU).
Because the retailer may sell 12,000 SKUs in its stores, the forecasts that Tool
#1 c creates cannot be fully reviewed by the forecast analysts of the retailer. The software
creates red flags if it realizes significant changes in the forecast of a SKU. The forecast
analysts review these red flags as well as the SKUs that are on promotion (around 200
SKUs per week) that period of time and decide for the final forecast (forecast of
exceptions). The inventory analysts receive the forecast for exceptions as well as a
different set of red flags from Tool #1 c. The inventory analysts will not create a purchase
order (PO) in the VMI process. On the contrary, inventory analysts will use the
information they receive (forecasts of exceptions and red flags) to resolve any issues that
may come up in the weekly collaboration meeting between the retailer and the vendor.
The vendor is then responsible to create the PO for the retailer under the VMI
system. The vendor utilizes software similar to Tool #Ic that is called Tool #2. Tool #2
receives as an input the daily inventory position of the DCs of the retailer (utilizing the
EDI 852 technology). Tool #2 utilizes this information to create the PO for the turn
39
products that the particular vendor sells to the retailer. For the promotional products and
the new launches, the vendor occupies a determined to the retailer team of VMI analysts.
These VMI analysts receive the promotional information from the retailer and create the
PO for the promotional products. The total of the PO (both for turn and promotional
products) are sent to the vendor's plants and DCs for fulfillment as well as to the
retailer's Tool #lc (via EDI 855) in order for the retailer to have knowledge and be able
to resolve any issues that may come up in the weekly collaboration meeting.
To sum up, at a high level under VMI replenishment policy, the supply chain of
the retailer and the vendor create three forecasts (plus an operational forecasts for the
vendor's manufacturing planning). The vendor creates the PO and the retailer reviews the
performance of the vendor in weekly meetings.
4.1.3
AS-IS Value Stream Map (Non-VMI Process)
Apart from the VMI replenishment policy, the supply chain of the retailer and the
vendor implements a non-VMI policy (Figure 4.4). The non-VMI policy is a more
traditional approach in which the retailer handles the vendor as a supplier and the vendor
handles the retailer as a customer. The non-VMI process is exactly the same as the VMI
process described in section 4.1.2 with the difference that the POs are created by the
inventory analysts of the retailer, i.e. the vendor receives no more data than the POs to
fulfill. The POs for the turn products would again be generated automatically by Tool #1 c
and the POs for the exceptions (promotions, red flags, new launches) would be created by
the inventory analysts of the retailer. The inventory analysts receive the forecast of
exceptions from the forecast analysts and they are those to finalize the POs according to
their internal knowledge of the inventory position of the retailer as well as different
40
exogenous factors, such us weather conditions, availability of trucks among others. The
inventory analysts are responsible to have the product in the DCs of the retailer on time.
On a high level basis, under non-VMI replenishment policy, the supply chain of the
retailer and the vendor create again three forecasts (plus an operational forecasts for the
vendor's manufacturing planning). The retailer creates the PO and the vendor fulfills the
order.
41
Non-VMI Replenishment Process
Vendor
Retailer
Distribution
Distribution
Center
Center
MAL
cj.~
Tstore
PO
ED[ 856
ERP #2
PO (no exceptio s)
Retailer
Stores
I
I- -
pulls
-
-
-
-
C
Consumer
-
--
-
-
-
-
-
-t
Vendor
Plantl
store pulls
To
1
EiR P it I
17Tl
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Tool #1a
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Flano.ran
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(3 days)
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4.2
Connecting the Dots
In this section we present the discussion, i.e. the results of our literature research that led
us to propose the alternative scenarios towards a consumer oriented supply chain (as presented in
the following section 0). In order to improve the existing replenishment collaboration, the retailer
and the vendor should focus on all three aspects of people, processes and technology. According
to Ireland & Crum (2005), companies should not look at the replenishment process initiative as a
technology project. In other words, people should be directly connected to the initiative. If
people are not engaged, then the equation old processes plus new technology will result in
expensive old processes, i.e. Old Processes + New Technology = Expensive Old Processes
(Ireland & Crum, 2005). People, processes and technology need to be aligned to the common
goal of improving the existing replenishment collaboration (Figure 4.5).
People
)
People
Process
Technology
Figure 4.5 Integration of People, Process and Technology (Ireland & Crum, 2005)
4.2.1
People
People are the most important asset of businesses. According to the interview that
we conducted with Kai Trepte, experienced professional in the field of supply chain
collaboration, in the improvement of the replenishment process, people are the most
crucial element that determine the success or failure of the initiative. The employees of
43
both the retailer and the vendor need to be educated for the change. According to Ireland
& Crum (2005), 80 percent of the education/training effort should focus on the new
business processes while the remaining 20 percent should focus on familiarizing with the
new technology.
Another very important aspect of people, apart from training, that affect the
success of a collaborative replenishment initiative is the need for executive-level
sponsorship. This is an absolute requirement for both the retailer and the vendor. C-level
executives of both organizations should be regularly briefed on progress and potential
hurdles. Lack of engagement by top management could turn out to be a critical failure
point for the initiative (VanDeursen & Mello, 2014). The executives should sponsor the
needed changes in culture, organization, incentives/rewards and technology investments
(Ireland & Crum, 2005).
Trust is another crucial aspect in terms of people. The issue of trust rises not only
between the retailer and the vendor but also internally in each company. Between the
rctailcr and the vendu
Rthe iI Si iII LUstiing the partner to Jo what 1hey promise to o.
Internally the different teams may not trust other teams on how they are going to use the
partner's data. However, data sharing is mandatory in order to achieve a collaborative
replenishment. The reality is that a risk exists both in sharing data and not sharing data
(Ireland & Crum, 2005). Sam Walton, founder of Wal-Mart, said "communicate
everything you possibly can to your partners. The more they know, the more they will
understand. The more they understand, the more they will care. Once they care, there is
no stopping them" (Ireland & Crum, 2005).
44
4.2.2
Processes
Processes are extremely important in the replenishment collaboration. According
to Larry Lapide, opinion leader in the field of supply chain collaboration, well-defined
and established processes are mandatory in order to determine the collaboration between
the retailers and the vendors. In fact, processes become even more important taking into
consideration that the benefits from collaboration can be effectively realized if at least 50
to 60 percent of the retailers and vendors are collaborative partners (Ireland & Crum,
2005). Without specific processes this critical mass of retailers and vendors would be
unable to collaborate effectively.
The collaboration between retailers and vendors is difficult because their interests
are not completely aligned. Vendors are most interested in getting the retailer to commit
to binding plans earlier (to cope with long lead-times) and getting access to better
demand data to be able to forecast more accurately, while the retailers typically need less
accurate forecasts (due to short lead-times) and their main problem is managing store
level demand, rather than aggregate demand, adequately (Smaros, 2007).
The majority of the discussions we conducted with supply chain professionals and
the research we performed for replenishment collaboration included the topic of
Collaborative Planning, Forecasting and Replenishment (CPFR). The CPFR was created
by industry people in order to address and frame the collaboration between retailers and
vendors towards more efficient supply chains. According to VanDeursen & Mello
(2014), CPFR is a supply chain strategy in which two or more companies share
information in order to monitor, control, and facilitate the overall performance of a
supply chain by achieving a smooth flow of product between firms.
45
Coordination of forecasts and promotional events is one key to preventing
surprises on the supply side that lead to misunderstandings concerning actual customer
demand, and that cause wild fluctuations in replenishment plans at the supplier, i.e. the
bullwhip effect (VanDeursen & Mello, 2014). The bullwhip effect in supply chains is the
increased order variability as demand moves upstream, away from the final customer (Ali
and Boylan, 2010). Under CPFR, the retailer and the vendor agree to execute against a
joint sales forecast and a joint order forecast. A joint sales forecast was discussed
frequently during conversations with the sponsor companies. They refer to it as "one
truth". This means, in other words, that the two companies should have a common picture
of how much product will be sold and what promotional activities will occur. According
to VanDeursen & Mello (2014), the joint sales forecast is considered to be a commitment
on the part of both firms regarding supply and demand actions. The joint forecast should
be used to drive production scheduling, distribution planning, and store-activity planning.
Exception based interventions into the joint forecast can take place from both companies
following
promotional activities, or
uneuxpCctU
actuai UIdmandU. I he
two parties would
manage these exceptions in collaboration meetings, as described in section 0. New
products introduction would also require the collaboration from both parties in order to
come up with the joint sales forecast.
In our respective interview, Larry Lapide also mentioned the important work of
the Voluntary Interindustry Commerce Standards Association (VICS) that created the
CPFR process. The CPFR process could be the philosophy of collaboration for the two
sponsor companies. According to VICS, the main body of CPFR consists of nine steps
(Ireland & Crum, 2005):
46
1. Front-End Arrangement, i.e. a document determining who does what, when and
how.
2. Joint Business Plan, i.e. a flowchart of process roles and rules to manage the
collaboration process from forecasting to replenishment (exchange of information
about strategies, promotions, events among others).
3. Demand Forecast Creation, i.e. a consensus, joint, synchronized forecast of
demand that is agreed on by both the retailer and the vendor (the forecast could be
in a store level, distribution center level or corporate level). Sharing of the
forecast utilizing the technology that is agreed to do so.
4. Identify Item-Level Exceptions to the Demand Forecast, i.e. generation of
exceptions by the technology tool and solution by both the retailer and the vendor.
5. Collaborate and Resolve Demand Forecast Exception Items, i.e. adjust the
specific item forecasts following the processes defined in step 1.
6. Replenishment Order Forecast Creation, i.e. using the demand forecast and
inventory strategies create and share the replenishment forecast.
7. Identify Exceptions to the Order Replenishment Forecast, i.e. develop a list of
item level exceptions to review collaboratively between the retailer and the
vendor. Exceptions stem from supply and capacity constraints.
8. Collaborate and Resolve Exceptions to the Order Replenishment Forecast, i.e.
adjust the specific item replenishment forecasts following the processes defined in
step 1.
9. Create the Replenishment Order, i.e. create and communicate replenishment
orders.
47
Our research informs this 9 steps process by suggesting that the sponsor
companies could implement partially the nine-step model. Additionally, it is important
that the retailer and the vendor agree on which items or product categories are more
meaningful for them to implement CPFR.
Another process that helped us identify the importance of collaborative
replenishment was the system dynamics methodology. The high-level system dynamics
model that we built is depicted in Figure 4.6. The model consists of two reinforcing loops
that depict the strong positive correlation between order effectiveness (stemming from
replenishment collaboration) and revenues increase for the sponsor companies. More
analytically, as the order effectiveness increases, the replenishment responsiveness
increases and therefore the service level increases and the revenues increase. As revenues
increase the software investment that lead to demand data accuracy increase and this
leads to increased order effectiveness. This is the "information accuracy" loop.
Simultaneously, as the order effectiveness increase, the inventory costs decrease and the
revenues increase. These are the main elements of the "inventory benefits" loop.
Order
effectiveness
Demanddata
accurac
+
Replenishment
responsiveness
(RD
CR..
Inventory
benefits
Software
+
investment
+
[nventory
costs
(R)
Information
accuracy
Service
level
Revenues
Figure 4.6 Revenues Reinforcing Loops from Replenishment Collaboration (System Dynamics
Model)
48
4.2.3
Technology
Communication and interaction, information flow and decision making, vital
activities in supply chain collaborative efforts, have been progressively improved thanks
to the incorporation of novel technologies, derived of the universal computer adoption
and virtual networks in almost every modem corporation. Technologies are bridges that
span complex circumstances, connecting people to processes. The ubiquity of the novel
information technologies have allowed corporations to lessen some of the burdensome
analytical tasks, particularly those related to ordering and replenishment. Although in
their continuous effort to integrate state of the art technologies, corporations are facing
new challenges of the era of interconnectivity, almost every major retailer captures and
stores data at some point, most frequently at the point of sales (POS). Today companies
look for ways and strategies to effectively take advantage of their existing information.
Some existing technological systems lack the ability to aggregate and manage POS data
properly in one single engine; in addition technology systems are not able to translate
complex analytics into executable decision, human control is still necessary to discern red
flags in the system. Promotions and product introductions are clear examples of how
technology depends on human intuition.
For the past several decades, the industry has strived to integrate technology in its
supply chain. The most known achievement is the Collaborative, Planning, Forecasting
and Replenishment system. A technological process, developed more than 20 years ago,
CPFR has proven that technology can link consumer demand with replenishment needs.
Large-scale retailers applied some of these principles and, as expected, the results of this
innovation were different. CPFR was beyond the implementation of rules and
49
technologies; it requires full commitment and human touch. As mentioned by
VanDeursen & Mello (2014), partnerships using CPFR used a common standard
(extensible markup language, or xml), however actual execution was based on preestablished agreements.
In the era of information technologies, new companies have emerged offering a
vast range of enterprise software products and solutions. Traditional companies such as
Oracle Corporation, SAP AG, IBM, BMC Software, IFS AB, QAD Inc, Microsoft, CA
technologies and many more have improved reliability and constant improvement in their
products. Most of them have contributed to the development of solid and robust software
infrastructure, particularly to the Enterprise Resource Planning (ERP) technologies. ERP
is the most accepted and widely used platform; it integrates the entire supply chain
processes from financial to manufacturing applications. ERP facilitated the development
and integration of new specific tools or suites. New companies have exceled positioning
their suites in the market, we can identify companies such as NetSuite, Infor, JDA and
Google, but there are many more offering yearly new interesting products. These
applications tackle specific problems such as forecasting, Business-to-Business (B2B)
communication, replenishment process, data storage, advance planning and scheduling,
transportation, and collaboration, among others. Table 4.1 displays the top supply chain
companies in 2013 and their different offerings, Supply Chain Planning (SCP),
Warehouse Management System (WMS), Manufacturing Execution System (MES) and
Transportation Management System (TMS). In the research of supply chain collaboration
and consumer oriented supply chain, we solely focus on the forecasting and collaboration
applications, although when redesigning the supply chain towards a consumer oriented
50
perspective, it is also important to consider other potential applications. This depends
entirely on the current installed software infrastructure and its integration within the
different aspects of the organization, such as marketing or finance.
Top 20 supply chain management software suppliers
I
SAP
S1721 bilIoin
sap.com
2
Oracle
$1.453 billon
ciace.com
3
JDA
$426
jdaoM
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4
Manhattan Associates
$160 milifn
manh.com
x
x
$131L2 md~ilon
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miltion
ibm-com
x
111 million
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Softwort
5 Epor
6 IBM
$112
on
Infor, Gblol
8 RedPrairie
7
9 D"Carw
$106
SyuMIW Group
Kewill Systems
10 Urit4
10
iciior
2
million
x
x
redprairie.com
$96 million
$62 millon
descoi.mcom
562 milgn
wrnt4,co/.w.pysalem
$54
gtnexUs.com
x
US2 mIlion
ibaqw
x
13 Quwntiq
$52 milon
quintiq.com
x
16 Logility
x
x
x
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A
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S51
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$50
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logilitycom
$48
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totvwcom
K
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S47 million
iisworld com/en
x
x
19 inapur Genursoft
$46 millon
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en.inupurcom
x
kinaxis-com
9
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Table 4.1 Top 20 supply chain management software suppliers (Gartner, 2013)
Recently, we identified three companies that have stood out with their technology
advancements in collaboration and integrated forecasting. Oracle, SAP and JDA are
pioneers and leaders in the Supply Chain software systems. Their suites JDA@
Flowcasting TM, SAP
Supply Chain solutions and Oracle® Collaborative Planning
represent an outstanding advancement towards a consumer oriented supply chain.
JDA@ flowcasting (Figure 4.7) provides one single truth, one single forecast.
Flowcasting enables cooperation and joint planning between vendor and retailer using
sell-through forecast, promotions and supply chain planning parameters. It also enables
cooperation beyond forecasting, such as promotions, safety stocks, display and
presentation stocks, lead times, direct vs. indirect shipments, delivery calendars, lot sizes,
51
etc. JDA flowcasting is deployed via JDA Cloud Services to deliver rapid, sustained
return on investment and enhanced organizational agility. According to JDA the main
value of the flowcasting is the flexibility and proactive management approach, since
flowcasting uses effectively daily POS data the forecast accuracy should be improved.
A QJgAjig Company Forecasting Today
CEINS
44
44
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Figure 4.7 JDA Flowcasting (JDA Software Group, 2014)
SAP has increased the portfolio of services in supply chain collaboration. New
solutions such as Demand Network increases visibility of real time demand to users and
52
suppliers, the Response Network solution improves replenishment efficiency by 50% and
up to 20% of inventory reduction, SAP Responsive Network suite allows users to connect
directly with partners and plan the push-and-pull-based product replenishment.
Oracle's Collaborative Planning (Figure 4.8) is an internet-based collaboration
solution that rapidly and significantly improves supply chain performance by providing
advance capabilities for collaborative demand, supply, and inventory planning across the
supply chain (Oracle Data Sheet). Oracle uses a holistic, e-business planning process that
reduces the conventional steps and processes to react quickly to supply chain exceptions.
The solution is fully automated and connected to store databases. Trading partners
receive proactive notifications when inventory needs to be replaced, according to preestablished inventory levels and planograms policies. Partners confirm the replenishment
information in the system and by using the E-business suite, the purchase order is
generated automatically, essentially a breakthrough technology with the principles of an
Orderless System.
Figure 4.8 Oracle Collaborative Planning (Oracle Corporation, 2014)
53
4.3
Forecast Analysis
In this section a descriptive analysis of the forecast comparison is presented. As described
in the methodology section, we carried out an analysis of four different SKUs in the laundry
category. The laundry category is under a VMI process - Figure 4.3 depicts the replenishment
map of these items. We pulled data for the first 26 weeks of 2014 and built the respective graphs
utilizing the information described in Table 4.2:
Information
Vendor replenishment
Description
This is the number of items shipped from the Vendor DCs or
manufacturing plants to the Retailer DCs
Forecast from the
As described in the AS-IS section, currently the retailer generates
Retailer at the DC level
forecasts at two levels of the Supply Chain, one at the Store level and
the other one at the DC level. Since we analyze VMI products, the
forecast from the retailer is actually a supplementary and control tool
for managing the vendor
Forecast from the
The retailer has the installed capability to develop a forecast to
Retailer Store level
replenish the stores
Vendor forecast
The Vendor generates weekly forecast
Point of Sales data
This is the comparison point of the replenishment and forecast
performance
Table 4.2 Information Received for Data Analysis
4.3.1
Analysis of SKU1
The first SKU presented is a class B item. These items are the middle share
products -usually 30% of the products fall into this category - and they account for 15%
54
of the Vendor's revenue. Our initial interest in this product stems from the fact that in
week 6 the product was re-introduced to the market. As we can see in Figure 4.9, in 2014
this product had a lifecycle of around 17 weeks, from week 6 until week 23, since after
that the product was discontinued from production. SKU 1 demand is relatively constant
and after week 6 the average is 704 items per week with a Coefficient of Variation (C.V.)
of 0.23.
Figure 4.9 SKUl Forecast Comparison
We can see that Retailer's forecast at the DC level was not interrupted
during the first weeks of 2014, even if the product was not on shelf. We can assume that
the retailer forecast at the DC level takes into consideration historical DC demand. On the
contrary, the retailer forecast at the store level follows closely the consumer demand, and
it seems it relies more on new data. Something similar happens to the vendor forecast; it
is more responsive and stays close to the POS data. Table 4.3 displays different metrics
55
to assess the performance of the vendor and the retailer. It seems that the forecast
accuracy (MAPE, RSME) of the vendor's forecast is better for SKUl.
Vendor replenishment displays an interesting behavior. At week 7 the vendor
ships a considerable amount of product and builds inventory close to the consumer. In the
subsequent weeks the replenishment constantly follows demand. At week 23 there is no
more replenishment but demand continues until inventory depletion.
Average
Std deviation
C.V.
Retailer DC
m
Retailer Store
Vendor
Retailer DC
m
Retailer Store
Vendor
704
159
0.23
0.65
0.33
0.17
651
234
154
Table 4.3 SKUl forecast metrics
4.3.2
Analysis of SKU2
This class A product displays high volatility and complexity in the forecast and
replenishment process. A product with these characteristics requires a high level of
cooperation. The analysis of SKU2 reveals the communication and information sharing
patterns, explained in the AS-IS map, in real action. SKU2 has an average demand of
2,271 items per week with a standard deviation of 1,727 items; the coefficient of
variation is 0.76. SKU2 is subjected to seasonality, possibly promotions that increase
demand, while competitors' strategies and other events decrease it. In Figure 4.10 we can
see this volatility that reaches to a maximum level of nearly 8000 units in week 22, or
more than 3 standard deviations above the mean.
56
Figure 4.10 SKU2 Forecast Comparison
We notice that the replenishment is conservative most of the time, on week 13
and week 22 the vendor builds inventory near the customer to fulfill what it looks like
promotional weeks. As we can expect, the forecast accuracy decreases with this high
level of volatility. Table 4.4 displays different performance metrics, the most accurate
forecast comes from the vendor with a MAPE of 0.53.
SKU2
Average
Std deviation
C.V.
Retailer DC
Retailer Store
Vendor
Retailer DC
Retailer Store
Vendor
2,271
1,727
0.76
0.64
0.74
0.53
1,643
2,3731,774
Table 4.4 SKU2 Forecast metrics
57
SKU2 reveals the necessity for communication between parties. Week 5 is a clear
example of this need as the vendor forecasted around 1000 items while the store
forecasted nearly 8 times more and the DC 5 times more. For week 5 the vendor shipped
more product following the trends of the retailer. Weeks 7, 11, 13 and 22 display the
same behavior.
4.3.3
Analysis of SKU3
SKU3 is considered a class A item and has a low coefficient of variation of 0.4.
SKU3 has also seasonality at different time periods and of different magnitude compared
to SKU2. In Figure 4.11 we can see that the level of response from the different
stakeholders involved is similar, responsive to the demand, except for week 19 when the
Retailer DC forecast expected an extraordinary level of sales. Based on the replenishment
and store forecast we can assume that this value is an outlier. Without this outlier the
MAPE of the DC forecast gets 0.49, a value close to the other forecasts (Table 4.5).
Figure 4.11 SKU3 Forecast Comparison
58
Similar to the previous SKUs, the vendor forecast seems to understand and catch
more effectively the demand; still the replenishment is not purely following the insights
of their forecasts. Clearly the vendor replenished following the retailer's store and DC
forecasts recommendations. Weeks 4, 9 and 14 and 20 display this effect, when the
vendor ships more product than the forecast insights.
Average
Std deviation
C.V.
Retailer DC
cc
2,316
930
0.40
0.67
Retailer Store
Vendor
Retailer DC
0.47
0.42
2,647
Retailer Store
Vendor
1,682
1,190
Table 4.5 SKU3 Forecast metrics
4.3.4
Analysis of SKU4
The final chosen item combines constant demand with high turnovers. SKU4 is
by far the bestselling item of our analysis. With an average demand of 4,338 items per
week and a standard deviation of 1,359, this item seems ideal to finalize the comparison
analysis. For this product we notice an increase in forecast performance, as Figure 4.12
depicts. The three different forecasts behave similarly, although the retailer's forecast at
the DC level is above the demand during most of the extraordinary weeks.
The main difference compared to the previous items is that the retailer's forecast
at the store level has a MAPE of 0.2, the lowest error recorded for all four different
59
SKUs, although the Vendor is not far with a MAPE of 0.22. Table 4.6 summarizes the
different performance metrics.
Figure 4.12 SKU4 Forecast Comparison
Average
4,JSs
Std deviation
1,359
C.V.
0.31
Retailer DC
0.37
Retailer Store
0.20
Vendor
Retailer DC
0.22
4,239
Retailer Store
1,478
Vendor
1,461
Table 4.6 SKU4 forecast metrics
The analysis of these 4 different SKUs reveals an interesting arrangement in the forecast
and replenishment processes of the sponsor companies:
.
The forecast from the vendor is relatively constant
60
"
The only abrupt changes occur during seasonal periods and possibly promotions
" The retailer is generally more responsive in its store-level and DC-level forecasts
" The forecast at the DC-level is generally more conservative and predicts high volumes
" The forecast at the store-level is more aligned with the demand
" During extraordinary events (e.g. promotions, new launches) it seems that the vendor
takes more into consideration the input from the retailer than from its own forecasts.
4.4
TO-BE Scenarios
The AS-IS value stream maps helped us understand the needs of the retailer and the
vendor as well as facilitate our interviews with opinion leaders in the field of planning,
forecasting and replenishment. The value stream maps helped us also manage and focus our
literature research in the field. Additionally, the data analysis revealed a number of potential
future explorations for the companies. By utilizing all these means, we identified opportunities to
improve the existing replenishment process and accuracy. Ultimately, three potential solutions
can be implemented to effectively leverage the use of POS data, reduce the bullwhip effect,
improve replenishment and fulfill aggressive service levels. The three scenarios focus on the
reduction of processes, a better utilization of people and an implementation of technologies. The
main differentiation factor of the alternative TO-BE scenarios is the forecasting process. The
scenarios include: Hybrid Forecast Replenishment Process, Retailer-Level Forecast
Replenishment Process and Vendor-Level Forecast Replenishment Process.
4.4.1
Hybrid Forecast Replenishment Process
In the AS-IS value stream maps (of the VMI process) we notice that the retailer
and the vendor create three different forecasts in order to replenish (store-level, retailer
DC-level, vendor DC-level). The Hybrid Forecast Replenishment Process consists of
61
only two separate forecasts, one at the retailer side and one at the vendor side. As a
concept, the retailer forecast team would collaborate with the vendor forecast team in
weekly meetings in order to reach consensus of the following week forecast and proceed
to the purchase order (PO) creation.
The Hybrid Forecast Replenishment Process consists of two alternatives based on
the flexibility of the retailer's deployment system, i.e. the flexibility of the operations of
the retailer to replenishment its stores. This flexibility is defined in terms of the need for
fixed delivery schedules, assigned trucks or drivers per route among others. A nonflexible deployment system needs store-level forecasting. This stems from the need to
know well in advance the delivery schedules and load per store. A flexible deployment
system requires only a retailer DC-level forecasting, since the operations of store
replenishment could manage the uncertainty of one-day-in-advance delivery plan
creation.
The TO-BE value stream map of the Hybrid Forecast Replenishment Process
(Flexible Store Deployment System) is depicted at Figure 4.13. The main difference with
the AS-IS process is that no forecast is created anymore at the store level. Therefore the
Store Analyst box in the TO-BE map is crossed out. The flexible store deployment
system allows the retailer to replenish its stores following standard replenishment
policies. The Inventory Analyst team would now be responsible to operate these store
replenishment policies. As a result, the Store Analyst department would be merged with
the Inventory Analyst department.
62
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Store Analysts would create a weekly store-level forecast. However, in order to replenish
the DCs of the retailer, no extra forecast will be created. The store-level forecasts would
be aggregated to form the weekly forecast for each DC (according to the DC that each
store belongs). In order to succeed that, the Forecast Analyst department would be
merged with the Store Analyst department.
63
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n
be created downstream, i.e. by the retailer. Preferably, the forecast will be created in the
retailer DC-level, i.e. partially following the Hybrid Forecast Replenishment Process
(Flexible Store Deployment System). The major difference is that in this scenario the
vendor will create no forecast at all. The vendor will receive the weekly forecast from the
retailer. In the weekly consensus meetings, the vendor would give its input on the
forecast and more specifically on the exceptions (promotional products and new
launches), since the vendor knows how its products move in other retailers too. By and
large, the retailer will create the POs with the critical input of the vendor for the
exceptions.
The reason why we selected this scenario is because it demands only a single
forecast leading to a more synchronized supply chain. The retailer could feel more
confident that the vendor will deliver (since the vendor has a real insight in the actual
demand) as well as the vendor could better plan its inventory needs and production plan.
The weekly consensus meetings are the heart of this scenario since they would facilitate
the collaboration between the retailer and the vendor. In order for that collaboration to be
as efficient as possible, a technological environment, common for both companies
(probably a cloud based IT solution), would help both parties address the exceptions and
finalize the forecast that the retailer would have created. The vendor should have access
to the forecast at least one day prior to the consensus meeting. The major challenge is that
the scenario requires a high degree of trust from the vendor side, since the vendor would
not have the consumer demand data to analyze and challenge the base of the retailer's
forecast.
65
Retailer-Level Forecast Replenishment Process
nm
DishionDistriRnte
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-
.- MA-L
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st Frcso
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h
EDI 856
ERP #2
PO (no exceptis)
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i
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Masters Sales
F
(3C days)
Pnetr
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management, pricing,
merchandising dpt
EDI Analystde-A ared cromouV
l
O-BE alue
Figure4.15
kags
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exceptions (3w)epenshen
flags
InVentOry
Analyst (1A)
forecast---- between----- th reale-ndte-edo.Thssige-oecs
threcreatio of-------only -one --Figure 4.15 TO-BE Value Stream Map (Retailer-Level Forecast Replenishment Process)
4.4.3
Vendor-Level Forecast Replenishment Process
The Vendor-Level Forecast Replenishment Process (Figure 4.16) also suggests
the creation of only one forecast between the retailer and the vendor. This single forecast
will be created upstream, i.e. by the vendor. The retailer will create no forecast at all. This
is the reason why in the TO-BE value stream map both the Forecast Analyst and Store
Analyst departmnents are crossed out. The vendor's VMI Analyst team would create the
weekly forecast and send to the retailer. In the weekly consensus meeting the vendor will
receive the input of the retailer's analysts concerning any forecast adaptations that may be
necessary since the retailer is the one that knows the promotional plan of the competitors'
products sold in its stores.
This scenario also incorporates the benefits of a single forecast in the supply chain
and gives as well insightful visibility to the vendor concerning its products throughout the
66
entire supply chain. In reality, this scenario suggests a real VMI process in which the
vendor is responsible for the majority of the decision-making. The scenario does not
require new major technological investments since the retailer and the vendor already
utilize VMI technologies. In this scenario the consensus meeting is again the main key for
success. Therefore, a new technological investment would be meaningful to help
facilitate the discussions there. This technological investment would be a forecast sharing
tool that will enable the retailer to be informed for the forecasts generated by the vendor
at least one day in advance of the consensus meeting. The scenario creates also the
important challenge of trust from the retailer side that the vendor would manage fairly the
retailer's inventory and fulfill the retailer's service levels.
Vendor-Level Forecast Replenishment Process
Rtie
S
Retailer
Distribution
Center
VenorVendor
Distribution
Plnt
Center
-b
E RP #2
EDi3,
EDI 856
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Input0
Feedback
rd flagsAsOat
Inventory Analyst (1A)
Figure 4.16 TO-BE Value Stream Map (Vendor-Level Forecast Replenishment Process)
67
4.5
Roadmap towards a Consumer-Oriented Supply Chain
The ultimate goal of this blueprint is to present to the retailer and the vendor a path
towards a consumer oriented supply chain. As already described, the way we view a consumer
oriented supply chain is the utilization of consumer-demand driven data (POS or store pulls) to
generate a collaboration process between the retailer and the vendor in order to achieve a
responsive and efficient replenishment. The roadmap towards a consumer oriented supply chain
consists of short-term as well as long-term steps and is depicted in Figure 4.17.
68
AS-
Executive-level
engagement
E
~0
Executive-level
engagement
NO
Do not use POS
data to forecast
NO
Establish
Goals & KPIs
Document
& KPIs
Document
collaboration process
Establish Collaboration
(VMI, Technologies etc.)
Study:
Is POS data
forecasting more
lccurate?
YES
Use POS data to
forecast
Study:
Is the store
eployment syste
YES
flexible?
Hybrid Forecast (Flexible Store
Deployment System)
Hybrid Forecast (Non-Flexible
Store Deployment System)
---
------------------------CL
Vendor's
E
Vendor-Level
-------------------------------
Study:
hich forecast i
better per produc
category
Retailer's
NO
Traditional DC Operation
Retailer-Level
Forecast
Forecast
0
POS
/data/
Study:
Could vendor's mixin
enters prepare
level orders?stor e>
YES
Direct Deliveries and/or
Retailer's DC Cross-Dock
Fans
Figure 4.17 The Roadmap towards a Consumer Oriented Supply Chain
69
4.5.1
Short-term Steps
The main concept of the short-term steps is that the retailer and the vendor will
need to establish a clear vision of future collaboration. This vision will affect the
decisions that are about to be made in the short and the long run. Our proposal is that in
the short run the retailer and the vendor should target to implement the Hybrid Forecast
Replenishment Process as described in section 4.4.1. The Hybrid Forecast is the closest to
the existing process of forecasting and replenishment. Organizational and operational
changes would not be that radical for both companies and both will be able to realize the
benefits of the Hybrid Process. The following table describes all the different elements of
the short-term roadmap.
Roadmap Element
As-Is
Establish
Goals &KPIs
Establish Collaboration
(VMl, Technologies etc.)
Study:
Is POS data
forecasting more
accurate
acre?
Description of Element
The improvements will start from the AS-IS state as described
in section 4.1.
The first step is to establish the goals for the improvement
process. Top management from the retailer and the vendor
must engage. KPIs will also be formed to evaluate the
improvements. All decisions will be dumente for future
reference.
The second step is to establish an initial collaboration. VMI is
a very good process that reveals if the retailer and the vendor
can effectively collaborate. The retailer and the vendor should
also examine the compatibility between their IT systems and
identify future technological needs. All findings should be
documented for executive level review and future reference.
The third step is to perform a study in order to identify if the
use of POS data (and/or store pulls) improves the forecast
accuracy. This is because the use of those data increases the
complexity and the effort to forecast and the companies
should be sure that would benefit from it. The proposed study
would compare the forecast accuracy of existing forecasts
with future POS data driven forecasts for a long period of
time.
70
Use POS data to
forecast
Do not use POS
data to forecast
tudy:
Is the store
eployment syste
flexible?
Hybrid Forecast (Flexible Store
Deployment System)
If the study reveals better accuracy, the use of POS data (or
store pulls) would be suggested for both companies.
If the study reveals no better accuracy, the use of DC level
data should continue.
The forth step is to perform a study in order to identify if the
deployment (store replenishment) system is flexible enough so
as not to need store level forecasts to plan operations well in
advance. The desirable situation is for the retailer to have a
flexible deployment system (easiness of truck schedule flips).
If the study reveals flexibility, then the retailer and the vendor
should implement the Hybrid Forecast Replenishment Process
(as described in Figure 4.13).
Hybrid Forecast (Non-Flexible
Store Deployment System)
4.5.2
If the study does not reveal flexibility, then the retailer and the
vendor should implement the Hybrid Forecast Replenishment
Process (as described in Figure 4.14).
Long-term Steps
The Hybrid Process is ideal to progressively move into the future of a consumer
driven replenishment between the retailer and the vendor. However, in that future, only
the retailer or the vendor would be the one completely responsible to generate the forecast
of a product category. The following table describes all the different elements of the longterm roadmap.
Description of Element
Roadmap Element
Study:
hich forecast i
better per produc
category?
The fifth step (first in the long-term steps) is to perform a
study to identify per product category the company that
makes a more accurate forecast in the long run. The
proposed study would compare the forecast accuracy of
previous forecasts of the retailer and the vendor in different
product categories for a long period of time (more than a
year).
71
For the product categories that the retailer had a more
accurate forecast, the two companies should implement the
Retailer-Level Replenishment Process (as described in Figure
4.15).
the product categories that the vendor had a more
accurate forecast, the two companies should implement the
Vendor-Level Replenishment Process (as described in Figure
4.16).
The sixth step (second in the long-term steps) is to perform a
Retailer-Level
Forecast
Vendor-Level
Forecast
1For
Study:
Could vendor's mixin
centers prepare store-
study to identify what requirements the preparation of store
level orders?
there is a significant return of investment (ROI) for the
vendor to offer such a service and if there is real benefit for
the retailer.
If the study is positive, then the vendor could prepare in its
mixing centers shipments ready for direct store deliveries or
ready to be cross docked in the retailer's DCs and then
shipped to stores together with products from other
vendors. This would create a more responsive
replenishment.
If the study is negative, the supply chain between the retailer
Direct Deliveries and/or
Retailer's DC Cross-Dock
Traditional DC Operation
orders in the mixing centers of the vendor creates and if
I
T
and the vendor would continue with its traditional format,
i.e. the vendor would ship full truckloads to the retailer and
the retailer would pick the store orders and distribute
accordingly.
72
5 Conclusion
The question posed in this thesis was: How is a consumer oriented supply chain defined
and in which way the retailer and the vendor could move towards it in the future? As a result of
our research, we conclude that a consumer oriented supply chain consists of two elements. The
first one is that a consumer oriented supply chain is triggered, in most of the cases, by consumer
demand data (POS or store pulls). The second element is that a consumer oriented supply chain
requires a strong collaboration between the retailer and the vendor, so together they can achieve
a more efficient response to the consumer demand.
This collaboration takes place in terms of people, processes and technology. For the
technology aspect there are a lot of software solutions that exist in the market. The biggest
challenge is how people in both companies would establish and follow the right processes by
utilizing this technology. Based on our research, the forecasting process is a significant element
of the desired collaboration. A single, synchronized forecasting of the consumer demand would
help both companies operate in a more synchronized and collaborative way. This forecast would
be the one and only truth that the companies would believe to plan their operations. Therefore,
the ultimate suggestion is that only the retailer or the vendor would create the forecast based on
which the replenishment process would take place in each product category. Based on our
analysis of forecasts and replenishment shipments of the retailer and the vendor, we can
conclude that as forecasts use information closer to the consumer, responsiveness and accuracy
are increased.
In order to move towards a consumer oriented supply chain we propose a roadmap with
short term and long term steps that the companies need to follow. In each major step of the
roadmap, a data-driven study needs to take place in order to justify the respective change. These
73
studies are a reference for future projects and research for the two companies. The most crucial
studies include in what extend the use of POS data improve the accuracy of the forecasts and
which company performs a more accurate forecast per product category.
74
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