Kesheng Wang
Jan Ola Strandhagen
Dawei Tu
Editors
Proceedings of
IWAMA 2013
The Third International Workshop of
Advanced Manufacturing and Automation
27 November 2013, Trondheim, Norway
Preface
IWAMA – International Workshop of Advanced Manufacturing and Automation – began in
2010 as a joint seminar between SFI Norman (SINTEF and NTNU) and Shanghai Key Laboratory
of Manufacturing Automation and Robotics (Shanghai University). In 2013 IWAMA expanded to
include academic and industrial experts in the fields of advanced manufacturing and automation
worldwide when the University of Manchester and Tongji University joined as co-organizers. As
IWAMA becomes an annual event, we expect more universities and industry sponsors will join the
international workshop as co-organizers.
Manufacturing and automation have assumed paramount importance and are vital factors for
the economy of a nation and the quality of life. The field of manufacturing and automation is
advancing at a rapid pace and new technologies are also emerging in the field. The challenges
faced by today’s engineers are forcing them to keep on top of the emerging trends through
continuous research and development.
IWAMA aims at providing a common platform for academics, researchers, practicing
professionals and experts from industries to interact, discuss trends and advances, and share ideas
and perspectives in the areas of manufacturing and automation.
IWAMA 2013 takes place in Trondheim, Norway, 27 November 2013, organized by
Norwegian University of Science and Technology, SFI Norman/SINTEF, Shanghai University,
University of Manchester and Tongji University. The program is designed to improve
manufacturing and automation technologies for the next generation through discussion of the most
recent advances and future perspectives, and to engage the worldwide community in a collective
effort to solve problems in manufacturing and automation. Participants from different countries,
such as Norway, China, UK, Germany, Italy and India attend the workshop.
Manufacturing research includes a focus on the transformation of present factories, towards
reusable, flexible, modular, intelligent, digital, virtual, affordable, easy-to-adapt, easy-to-operate,
easy-to-maintain and highly reliable “smart factories”. Therefore, IWAMA 2013 has mainly
covered 3 sessions in manufacturing engineering:
1.
2.
3.
Intelligent Manufacturing Technology
Intelligent Logistics and Supply Chains
Intelligent Diagnosis and Prognosis of Wind Turbines
The proceedings of IWAMA2013 provide up-to-date, comprehensive and worldwide state-ofthe-art knowledge of the manufacturing and automation science and engineering. All papers
submitted to the workshop have been subjected to strict peer-review by at least 2 expert referees.
Finally, 28 papers have been selected to be included in the proceedings after a revision process.
We hope that the proceedings will not only give the readers a broad overview of the latest
advances, and a summary of the event, but also provide researchers with a valuable reference in
this field.
On behalf of the organization committee and the international scientific committee of IWAMA
2013, I would like to take this opportunity to express my appreciation for all the kind support,
from the contributors of high-quality keynotes and papers, and all the participants. My thanks are
extended to all the workshop organizers and paper reviewers, NTNU and SFI Norman for the
financial support, and co-sponsors for their generous contribution. Thanks are also given to Quan
Yu, Yi Wang, Cecilia Haskins, Tonje Berg Hamnes, Line Holien, Jorurnn Auth, and Lilan Liu for
I
their hard editorial work of the proceedings and arrangement of the workshop. Thank also goes to
Lasse Postmyr from Tapir Academic Press for producing the proceedings.
Kesheng Wang, Ph.D. Professor of NTNU
Chairman of IWAMA 2013
Trondheim, Norway, 25th October, 2013
II
© IWAMA 2013 & Akademika Publishing, 2013
Copyright is held by the respective authors.
ISBN 978-82-321-0377-5
ISSN 1892-8110
This publication may not be reproduced, stored in a
retrieval system or transmitted in any form or by any
means; electronic, electrostatic, magnetic tape, mechanical,
photo-copying, recording or otherwise, without permission.
Layout: The authors
Cover layout: Akademika Publishing
Printed and binded by: AiT Oslo AS
We only use environmentally certified printing houses.
Akademika Publishing
Oslo/Trondheim
Norway
www.akademikaforlag.no
Publishing Editor: Lasse Postmyr (lasse.postmyr@akademika.no)
Organized and Sponsored by
NTNU (Norwegian University of Science and Technology);
SHU (Shanghai University);
SINTEF (The Foundation for Scientific and Industrial Research)
SFI Norman (Center for Research-based Innovation – Norwegian Manufacture Future)
UM (The University of Manchester)
TU (Tongji University)
Honorary Chairs:
Prof. Minglun Fang
General Chairs:
Prof. Kesheng Wang, Prof. Jan Ola Strandhagen and Prof. Dawei Tu
Local Organizing Committee:
Prof. Jan Ola Strandhagen, Prof. Kesheng Wang, Prof. Heidi Dreyer, Lars Tore Gellein, Quan Yu,
Tonje Berg Hamnes, Associate Prof. Cecilia Haskins, Jorunn Auth
International Program Committee:
Prof. Jan Ola Strandhagen, Norway
Prof. Kesheng Wang, Norway
Associate Prof. Per Schjølberg, Norway
Prof. Heidi Dreyer, Norway
Associate Prof. Erlend Alfnes, Norway
Lars Tore Gellein, Norway
Geir Vevle, Norway
Associate Prof. Hirpa L. Gelgele, Norway
Associate Prof. Wei Deng, Norway
Lecturer Yi Wang, UK
Prof. Wladimir Bodrow, Germany
Prof. Janis Grundspenkis, Latvia
Prof. Hans Henrik Hvolby, Denmark
Prof. Dawei Tu, China
Prof. Minglun Fang, China
Prof. Tao Yu, China
Prof. Binheng Lu, China
Prof. Ming Chen, China
Prof. Yun Chen, China
Prof. Xinguo Ming, China
Prof. Yingjie Yu, China
Prof. Yayu Huang, China
Secretariat
Tonje Berg Hamnes (Norway) and Prof. Lilan Liu (China)
III
About Editors
Prof. Kesheng Wang holds a Ph.D. in production engineering from the Norwegian
University of Science and Technology (NTNU), Norway. In 1993 he was appointed
Professor at the Department of Production and Quality Engineering, NTNU. He is a
director of the Knowledge Discovery Laboratory (KDL) at IPK, NTNU at present. He is
also an active researcher and serves as a technical adviser in SINTEF. He was elected
member of the Norwegian Academy of Technological Sciences in 2006. He has published
17 books, 10 book chapters and over 214 technical peer-reviewed papers in international
journals and conferences. Prof. Wang’s current areas of interest are intelligent
manufacturing systems, applied computational intelligence, data mining and knowledge
discovery, swarm intelligence, condition-based maintenance and structured light systems
for 3D measurements and RFID.
Prof. Jan Ola Strandhagen is a research director of the research center SFI NORMAN at
SINTEF. He is also Professor at Department of Production and Quality Engineering, the
Norwegian University of Science and Technology (NTNU). He holds a PhD in
Production Engineering from NTNU (1994). His research has focused on production
management and control, logistics, manufacturing economics and strategies. He has
managed and performed R&D projects in close collaboration with a wide variety of
Norwegian companies and participated as researcher and project manager in several
European projects.
Prof. Dawei Tu received his bachelor’s degree, master’s degree and PhD from Zhejiang
University in 1987, 1989 and 1993 respectively. Since 1993 he joined Shanghai
University, and became a professor in 2000. Now he is the Executive Dean of the School
of Mechatronics Engineering and Automation, Shanghai University. His research
interests include optoelectronic detecting, precision mechanics and instrumentation, 3D
vision and vision-based robot servo-control.
IV
Table of Contents
Preface ............................................................................................................................... I
Keynotes
Knowledge Dynamic in Software Applications for Process Controlling
Wladimir Bodrow......................................................................................................................... 1
Toward Intelligent Manufacturing: a Label Characters Marking and Recognition
Method for Steel Products with Machine Vision
Qijie Zhao, Dawei Tu and Peng Cao ............................................................................................ 9
The Development of Intelligent and Integrated RFID (II-RFID) System for
Manufacturing Industries
Kesheng Wang ........................................................................................................................... 25
Responsible and Dynamic Logistics
Jan Ola Strandhagen ................................................................................................................... 49
Session IMT: Intelligent Manufacturing Technology
Development and Deployment of ACT System - RFID Solutions
Jan Erik Evanger and Fredrik W. Goborg .................................................................................. 53
Assessment of Geometric Tolerance Information as a Carrier of Design Intent to
Manufacturing and Inspection
Hirpa G. Lemu ........................................................................................................................... 59
Phase to Depth Conversion Algorithm by Using Epipolar Restraint in Objectadaptive Fringe Projection Technique
Junzheng Peng, Yingjie Yu, Wenjing Zhou and Mingyi Chen .................................................. 71
Computer Vision Supported by 3D Geometric Modeling
Sven Fjeldaas ............................................................................................................................. 81
Studies on the Intelligent Manufacturing Technologies of the Fan Blade
Wenhua Zhu, Baorui Li, Bin Bai, Hu Han and Minglun Fang ................................................... 93
Virtual Simulation of Production Line for Ergonomics Evaluation
Ming Chen and Jinfei Liu......................................................................................................... 113
A Point Cloud Registration Strategy Combining Particle Swarm Optimisation
and Iterative Closest Point Method
Quan Yu and Kesheng Wang ................................................................................................... 123
Graph Representation of N-Dimensional Space
Tomasz Kosicki ........................................................................................................................ 135
Study of the Role of Virtual Engineering Technologies in Design
Hirpa G. Lemu ......................................................................................................................... 147
V
Session ILSC: Intelligent Logistics and Supply Chains
Lean Thinking in Product Development: Looking into Front-load Effects Using a
System Dynamics Approach
Alemu Moges Belay and Torgeir Welo.................................................................................... 157
Ontology Based Interoperability Solutions within Textile Supply Chain: Review
and Analysis
Yi Wang, Rishad Rayyaan and Richard Kennon ..................................................................... 171
A Framework for Real Time Production Planning and Control
Emrah Arica and Daryl J. Powell ............................................................................................. 185
Sources of Complexities in New Product and Process Development Projects
Giedre Pigagaite, Pedro P. Silva, Bassam A. Hussein ............................................................. 197
Investigating the Fit of Planning Environments and Planning Methods, the Case
of an Automotive Part Manufacturer
Philipp Spenhoff, Marco Semini, Erlend Alfnes and Jan Ola Strandhagen ............................. 211
Investigating the Applicability of Lean Principles in a Small, One-of-a-kind
Production Company
Layli Dahir and Daryl Powell .................................................................................................. 221
Introducing Portfolio Maps as Basis for Standardization and Knowledge-based
Development
Sören Ulonska and Torgeir Welo ............................................................................................. 235
Incentive Regulation of Banks on Third Party Logistics Enterprises in Principalagent-based Inventory Financing
Xuehua Sun and Xuejian Chu .................................................................................................. 249
Automation in the ETO Production Situation: The Case of a Norwegian Supplier
of Ship Equipment
Børge Sjøbakk, Maria Kollberg Thomassen and Erlend Alfnes .............................................. 259
Competing in the Shell Eco-marathon. Challenges and Lessons Learned
Bassam A. Hussein and Fariborz Ali Haidrloo ........................................................................ 271
Tackling the Storage Problem through Genetic Algorithms
Lapo Chirici and Kesheng Wang ............................................................................................. 281
Session IDPWT: Intelligent Diagnosis and Prognosis of Wind Turbines
(WINDSENSE Project)
Evaluation of Thermal Imaging System and Thermal Radiation Detector for
Real-time Condition Monitoring of High Power Frequency Converters
Anders Eriksen, Dominik Osinski and Dag Roar Hjelme ........................................................ 295
SCADA Data Interpretation for Condition-based Monitoring of Wind Turbines
Kesheng Wang, Vishal S. Sharma and Zhenyou Zhang........................................................... 307
VI
Wind Turbine Fault Detection Based on SCADA Data Analysis Using ANN
Zhenyou Zhang and Kesheng Wang ........................................................................................ 323
Models for Lifetime Estimation - An Overview with Focus on Applications to
Wind Turbines
Thomas M. Welte and Kesheng Wang ..................................................................................... 337
VII
List of Authors
Osinski, D. 295
Peng, J. 71
Pigagaite, D. 197
Powell, D. J. 185, 221
Rayyaan, R. 171
Semini, M. 211
Sharma, V. S. 307
Silva, P. P. 197
Sjøbakk, B. 259
Spenhoff, P. 211
Strandhagen, J. O. 49, 211
Sun, X. 249
Thomassen, M. K. 259
Tu, D. 9
Ulonska, S. 235
Wang, K. 25, 123, 281, 307, 323, 337
Wang, Y. 171
Welo, T. 157, 235
Welte, T. M. 337
Yu, Q. 123
Yu, Y. 71
Zhang, Z. 307, 323
Zhao, Q. 9
Zhou, W. 71
Zhu, W. 93
Alfnes, E. 211, 259
Arica, E. 49, 185
Bai, B. 93
Belay, A. M. 157
Bodrow, W. 1
Cao, P. 9
Chen, M. 113
Chen, M. 71
Chirici, L. 281
Chu, X. 249
Dahir, L. 221
Eriksen, A. 295
Evanger, J. E. 53
Fang, M. 93
Fjeldaas, S. 81
Goborg, F. W. 53
Haidrloo, F. A. 271
Han, H. 93
Hjelme, D. R. 295
Hussein, B. A. 197, 271
Kennon, R. 171
Kosicki, T. 135
Lemu, H. G. 59, 147
Li, B 93
Liu, J. 113
VIII
International Workshop of Advanced Manufacturing and Automation 2013 (IWAMA 2013)
A Framework for Real Time Production Planning and Control
Emrah Arica and Daryl J. Powell
Department of Production and Quality Engineering, Norwegian University of Science
and Technology, Trondheim, Norway
Abstract
This paper aims to develop a conceptual framework for real-time production planning and
control. Firstly, applied contemporary information and communication technologies for
production planning and control have been discussed in the paper. Enterprise Resource
Planning (ERP) systems that integrate the value chain in an enterprise, Manufacturing
Execution Systems (MES) that manage and control the production on shopfloor, and
Advanced Planning and Scheduling (APS) systems that develop solutions for complex
planning problems are the planning and control systems that have been analysed. Radio
Frequency Identification (RFID) has been emphasized as being the most advanced and
promising real-time data capture technology. Having analysed the features and
shortcomings of the systems per se, and advantages that may come out by integrating
them efficiently, a framework is proposed.
Keywords
ERP, MES, APS, RFID, Production Planning and Control
1
Introduction
The future demand driven factory will be based on intelligent and automated planning
and control systems, employing closely integrated real-time information systems
throughout the entire supply chain. This will enable the manufacture of customised
products within highly responsive, reconfigurable, and time efficient environments, as
well as effective and robust planning for standardised products. However, we foresee a
number of obstacles which need to be overcome in order to achieve a demand driven
value chain.
The purpose of this paper is to evaluate current enterprise systems and technologies
and analyse their limitations in order to present a conceptual framework for real-time
production planning and control within the visual enterprise. This paper considers
production planning and control (PPC) systems at the individual plant level and reflects
on integration within the supply chain through the application of ICTs. This enables more
effective and real-time production planning and control process.
We investigate the level of automation within enterprise resource planning (ERP)
systems and discuss the impact of human interaction with ERP systems, as the quality
and accuracy of data input is considered a weakness with ERP. The concept of advanced
planning and scheduling (APS) systems is explored. Interaction of ERP systems and MES
and its shortcomings are discussed. By considering RFID, the paper also explores the
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International Workshop of Advanced Manufacturing and Automation 2013 (IWAMA 2013)
utilisation of real-time data within the supply chain, the production planning function, and
the shopfloor. On the basis of the functions, shortcomings and abilities of the systems a
framework that integrates ICTs for real time production planning and control is
suggested.
The paper is structured as follows: we first address the ICT systems which can be
utilized for automated production planning and control. Next we consider real-time
information systems, introducing and discussing applications of RFID. Then we discuss
and present our conceptual framework for real-time production planning and control,
utilizing and integrating enterprise systems with real-time information systems. We
finally draw conclusions to this research.
2
Contemporary Planning and Control Systems
With the advances of communication and integration standards, data processing
capabilities, network computing and internet technologies, the computerization pace in a
manufacturing enterprise is accelerating so that the enterprise can provide better customer
service level with higher productivity, higher quality, and lower inventory costs. As such,
many manufacturers are implementing advanced information and communication
technologies (ICTs) to support manufacturing operations. This section reviews the state
of the art in terms of applied production planning and control systems.
2.1 Enterprise Resource Planning Systems
The majority of the manufacturing firms use Enterprise Resource Planning (ERP) systems
to develop medium-term and short-term production plans and schedules [Stevenson et al,
2005]. ERP is a standardized software package designed to integrate the internal value
chain of an enterprise [Møller, 2005]. According to Nah [2002], the American Production
and Inventory Control Society (APICS) define ERP as “a method for the effective
planning and control of all resources required to take, make, ship and account for
customer orders in a manufacturing, distribution or service company”.
However, as ERP systems remain centred around old material requirements planning
(MRP) logic, in many cases, capacity constraints remain to be ignored and lead times are
still assumed to be fixed. This creates many problems for the shopfloor [Chen & Ji,
2007]. With a typical MRP-based planning system, there is no guarantee that a feasible
production schedule exists for the generated production plan. This results in constant
adjustments to the plan, and drives planners to lengthen lead times and increase work-inprocess (WIP) inventories.
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Fig. 1 ERP systems for Manufacturing Planning and Control [Jacobs et al, 2011]
Much like ERP systems and the hierarchy of production planning and control, the
planning and scheduling of a supply chain also occurs in various phases: a first phase
involving the medium-term planning process (using aggregated data), and a subsequent
phase performing a more detailed, short-term schedule [Kreipl & Pinedo, 2004].
2.2 Advanced Planning Systems
Although ERP systems provide an information backbone that is needed for sound
planning procedures, there is still a lack of intelligent planning and decision support
functions. This has led to the development of so-called advanced planning systems. APS
emerged to supplement the ERP systems and eliminate some of the major MRP
assumptions such as fixed lead times [Steger-Jensen et al, 2011].
Kjellsdotter and Jonsson [2008] suggest that APS is supposed to help companies deal
with more complex planning situations. Kletti [2007] refers to ERP-based planning as
rough planning, and suggests that the use of APS allows for so-called detailed planning.
Whereas ERP systems are typically transaction-orientated (i.e. sales invoices, shipment
notices etc) and have limited decision support features, an APS system leverages the data
within the ERP system to provide decision support for both production planning and
demand planning [Kilpatrick, 1999].
By considering the most common standard APS modules in Figure 2, it is apparent that
APS is perhaps more applicable to planning and scheduling the entire supply chain or
network, rather than purely within a single production plant. (It is clear in the figure that
production planning and production scheduling comprise only one module of the APS
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International Workshop of Advanced Manufacturing and Automation 2013 (IWAMA 2013)
system, which makes ERP a central part of the APS). As such, Jonsson and Kjellsdotter
[2007] reflect that APS systems are potential support tools for managing complex supply
chain planning problems and as a means for supply chain integration. On the other hand,
in the context of the individual plant, Fleischmann and Meyr [2003] suggest that the real
advance with APS is the implementation of advanced planning concepts in standard
software, which they suggest is a great progress in comparison to traditional ERP
systems.
Fig. 2 Typical APS Planning Structure [Stadtler & Kilger, 2008]
When combined with ERP systems APS allows for explicit, capacity constrained
production planning and control. It considers not just shopfloor capacity constraints, but
also inventory and bill of material constraints, inventory stocking and replenishment
levels, and order generation policies [Turbide, 1998]. However, Jonsson and Mattsson
[2008] states that although APS methods offer concurrent priority and capacity planning,
the use of APS is still low in industry. Comprehensive study of Ivert [2012] about the use
of APS systems in different planning and control tasks also supports this view.
2.3 Manufacturing Execution Systems
Manufacturing Execution Systems (MES) have been evolved to aid production execution,
monitoring, and control activities on the shop floor, closing the drawbacks of the ERP
systems in real time information exploitation from the shop floor. An MES controls the
operations that enable the realization of the plans from the ERP system. MES systems
have initially been implemented in process industries (e.g. pharmaceutical, food, and
semiconductors) for traceability purposes imposed by authorities, currently they are in
use in most manufacturing industries [De Ugarte et al, 2009].
The Manufacturing Execution Systems Association [MESA, 1997] defines the
principle functionalities of MES, shown in the figure below. Additionally, depending on
the process type and shop floor requirements some MES vendors may offer extended
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functionalities such as engineering change management, rework management, outsourced
manufacturing.
Fig. 3 MES functionalities [De Ugarte et al, 2009]
2.4 Key Limitations with Contemporary Planning and Control Systems
Though the enterprise systems described in this report can be classified as automated
planning and control systems to a certain degree, they still require a large amount of
human interaction in order to work properly. For example, a production planning and
control system cannot function effectively when the enterprise system is driven by
inaccurate, untimely and uncontrolled data. Poor input data will lead to excessive replanning and rescheduling, which is both costly and disruptive. Xu et al. [2002] supports
this view by suggesting that when organizations implement an ERP system, it is
imperative that data quality issues are a high priority. Such a simple, yet vital task as data
entry into an enterprise system becomes the point of failure, resulting in synchronization
issues, loss of data and other potential risks.
ERP systems are mainly intra-firm focused and provide seamless integration of
processes delivering improved workflow, standardized business processes, improved
accounting and provide up-to-date operational data [Mabert et al, 2003]. Though this
sounds good in theory, in practice, it can be very different. For example, standardized
business processes maybe challenged across functional boundaries, and the up-to-date
operational data may, in fact, not be so up-to-date after all. Introduce several
organizational boundaries when attempting to push the enterprise system out across the
supply chain, and suddenly these problems increase exponentially.
Jonsson and Mattsson [2006] discuss the results of a longitudinal study into the use of
material planning methods in manufacturing companies, which they conducted between
1993 and 2005. One conclusion they made was that a typical way of determining
parameters used in ERP systems (for example order quantities and safety stocks) is by
general judgment and experience. Thus, rather than using the ERP system to calculate and
propose intelligent and efficient order quantities and stock levels, the planner typically
overwrites this function with his or her best guess.
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There is still lack of information availability in shop floor. For example, an ERP
system alone lacks the functionality to offer detailed visibility into the manufacturing
work flow and information on individual critical resources, such as machine and tooling
status, WIP levels and the location of products in the production processes in order to
support intelligent, dynamic decision making as unexpected events occur.
The major contributions of the MES in the shop floor control context are: (i) sensing
the unexpected events during production execution, (ii) elementary data storage, (iii)
restitution of the data, and (iv) relevant data selection [De Ugarte et al, 2009]. Yet, the
contribution of the other information systems and decision support tools is also important
to complete the process and respond the changes effectively. In this context, MES may
especially lack decision making capabilities to evaluate the unanticipated situation and
suggest corrective actions, which for example can be supported by APS if the data timely
and accurately provided.
Accurate lead times and safety stocks are two of the most critical parameters for
achieving high planning and control performance [Jonsson & Mattsson, 2008]. By using
real-time information technologies integrated within an ERP-MES-APS infrastructure,
the accuracy of such parameters will be increased substantially. Furthermore more precise
and accurate plans can be developed.
With the recent development of ISA95 standard, the enterprise-control systems
integration has been made easier to achieve. The standard defines a general model for
manufacturing operations and planning levels that can be mapped into systems such as
ERP, MES, APS, and Product Data Management (PDM) [Campos & Miguez, 2011].
Hence, it facilities the real time data access across manufacturing operations. More
specifically it defines;
• the information exchange between business functions and manufacturing operation
functions,
• the interfaces and data flows between associated control systems (e.g. ERP and MES)
• the interface contents between the systems.
3
Real time Data Capture
Traditionally, data collection has been a very manual process, at best using simple
technologies in the form of barcode readers to track inventory movement and shopfloor
activities. There is now a general belief within industry that capturing and sharing realtime demand information is the key to improve supply chain performance [Cachon &
Fisher, 2000].
Real-time information capture can be described as the capability of obtaining the
required information at the required time from intended objects, devices or people by data
capture technologies [Heinrich, 2005]. This enables to monitor the tracked entities and
have rapid and efficient responsiveness to changes in their conditions.
Real-time data acquisition technologies are the enablers to having the availability of
real-time information. Radio Frequency Identification Device (RFID) is currently the
most advanced technology in real-time data capture and has been gaining popularity
increasingly in the last decade in any place requiring identification of products [Xiao et
al, 2007]. It enables to track items of interest and obtain necessary information such as
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identity, location, movement and states of them, and to manage the supply chain of goods
more effectively, accurately and automatically.
RFID offers some significant advantages against other data capture technologies
[Heinrich, 2005; Xiao et al, 2007]: 1) No line of sight is required: as such they are
suitable to communicate from a long distance 2) RFID tags can be positioned on common
materials easily and without confronting with any incompatibility 3) Automatically
captured highly accurate data 4) Information is captured in seconds 5) RFID tags contain
more information.
3.1 Applications on the Supply Chain and Shopfloor
Application and potential benefits of Auto-ID technologies within the supply chain
scenario has been widely investigated, reported and recognized by both research and
business environment [Lee et al, 2009; Veronneau & Roy, 2009]. Considering supply
chain performance measures at the production level, such as range of product and
services, capacity utilization and effectiveness of the scheduling techniques [Gunasekaran
et al, 2004], it is clear that minute-by-minute control on the manufacturing system will
significantly contribute to improvement of these measures, and as a result to the
performance objectives of supply chain management. Slack et al. [2007] states that
capacity utilization affects the speed of response to customer demand through its impact
on flexibility, lead-time and deliverability.
However there is a substantial need for research within the manufacturing applications
[Huang et al, 2009]. Manufacturing environment includes many operational issues such
as production and inventory management and maintenance as well as embodies plenty of
objects including machines, tools, raw materials, WIP, finished products, reports,
production plans and schedules, drawings, instructions etc. But most of the modern
shopfloors have a bottleneck of capturing and collecting real-time information from the
field as well as to exploit this information in their manufacturing planning and control
systems. RFID technology is a promising and emerging technology to deal with this
challenge and can contribute to improve shopfloor productivity and quality [Huang et al,
2008]. RFID tags will be significantly beneficial and provide the factories with minuteby-minute coordination of raw materials, work-in-progress (WIP) and finished goods as
they serve as means of effective data collector and processor. When a fault appears in the
process, reflecting on the problem will be rapidly performed and decisions will be taken
on time without losing the precious time in production.
Tremendous pressure of current competitive market conditions resulting in heading
towards to demand driven manufacturing approach, RFID adaptation has also a lot to
promise to manufacturers to overcome customization challenges within the
manufacturing process. In this competitive environment, companies must shorten product
life cycles, reduce time-to-market, increase product variety and satisfy demand while
maintaining the quality standards and reducing investment costs [Meyer et al, 2009].
Brusey and McFarlane [2009] express that application of sophisticated RFID technology
has potential to solve the tracking problem of customized products in even late assembly
process of the customization competence. As each product ordered by a specific customer
has different specifications, such a system that integrates with the manufacturing control
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system and obtains very accurate information about the location, state and identity of
each product is critically important to avoid highly possible problems and delays.
4
Implications and Discussions for Real-time PPC
The required system is supposed to sense any event occurring on the shopfloor and
collect real-time data, analyze, and reflect upon it automatically within the planning
system. In addition to these capabilities the system should synchronize the objectives of
tactical level planning (ERP-Supply Chain) with the production plans on the shopfloor
(MES). Basically this important issue is not being considered comprehensively; therefore
operational level goals do not conform to those of tactical level. As a consequence,
strategic and tactical goals can’t be accomplished to the desired level as things get much
more complicated down on the shopfloor when the production plans are broken down.
Only focussing on solving the problem at the supply chain level (sales and operations
plan) or on the shopfloor level (production planning, scheduling and execution) is not
sufficient to overcome the challenges that occur due to the uncertain and demand driven
environment. Thus the system should optimize the tactical and operational level plans
simultaneously.
The first task in real-time production planning and control is collecting and utilizing
the data on a real-time basis. The data collection from the shopfloor must adopt an
autonomous functionality, which implies the deployment of real-time technologies to the
relevant objects. Integrating the real-time information system, and synchronising it with
the enterprise system, transmitting this information to the business functions and
processing it will provide the organization with a highly adaptive and responsive planning
and control system. It is of high importance to deal effectively with the real-time data in
the organization in order to utilize it in the planning, scheduling and execution system.
Having the ability of real-time control on the shopfloor events is crucial to have the
capability of recognizing deficiencies before losing time and reflecting on it. This
capability will prevent the potential failures and troublesome issues in other parts of the
supply chain. Moreover, such efficient and effective shopfloor control grows in
importance for high responsiveness to changes such as customer requirements since it is
one of the key enablers to have a flexible manufacturing system. RFID system
deployment to the shopfloor objects and connection with the MES through wireless
technologies will provide this ability.
An MES executes the production on the shopfloor. It detects, follow and analyze the
machine run and stop times, cycle time, number of scrap units as well as orders on realtime basis. Furthermore the system can dispatch the jobs to the machines, calculate the
efficiency and actual machine capacities precisely and report it. Sending accurate
information from manufacturing environment to the ERP system efficiently increases the
visibility and control on manufacturing execution which is one of the black boxes for
supply chain control. As the ERP system provides information transparency through the
supply chain, fully integrated ERP-MES system offers high coordination of the
manufacturing processes across the supply chain and increases the responsiveness to
constant changes that occur in both customer and supply side. Having the availability of
accurate data also leads to increase the performance of plans in ERP and MES.
Nevertheless, in order to improve the decision making. process by real-time capability,
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International Workshop of Advanced Manufacturing and Automation 2013 (IWAMA 2013)
there is strong need for interface between the ERP and MES systems that integrates them,
utilize the masses of real-time data, reduces the manual work and automates the data
transfer.
Fig 4: Framework for real time production planning and control
Integrating MES-ERP with the APS system can lead to substantial improvement in
production plans. MES can detect and analyze actual shopfloor constraints (e.g. machine
capacity, efficiency, changeover time etc.) precisely. Having this information available in
the APS system along with supply chain constraints and transaction data coming from the
ERP system can significantly contribute to develop advanced and accurate production
plans by the APS system on real-time basis. Furthermore, the APS system can also
develop a complex bi-objective model for tactical and operational plans and optimize it
taking their constraints and objectives into account simultaneously. This capability also
contributes to integrate the supply chain with the shopfloor. Thus, it is suggested that
MES-ERP-APS integration equipped with real-time information technologies can support
the manufacturers to perform the production planning and control process on an
automated and real-time basis and accurately.
5
Conclusions
Automatic real-time planning and control systems promise substantial advantages to
manufacturing companies, especially those that operate within fluctuating and
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International Workshop of Advanced Manufacturing and Automation 2013 (IWAMA 2013)
competitive market conditions within a demand-driven manufacturing environment, since
flexibility and robustness of the planning and control system are the enablers for a high
level of responsiveness. Integrating the real-time information system, and synchronising
it with the enterprise system, transmitting this information to the business functions and
processing it will provide the organization with a highly adaptive and responsive planning
and control system.
Prior to the implementation of such a revolutionary, demanding, and potentially
expensive system that requires radical re-organization within a company and its supply
chain, as well as dynamic interaction between operational shopfloor level decisions with
enterprise planning and execution systems, the system architecture requirements,
expected benefits, motivations and purposes to adopt such technologies, as well as the
characteristics of the pilot company, should be investigated very carefully. Since the
technologies (APS, MES, RFID, etc.) contain some challenges and shortcomings
themselves, and synchronization with current enterprise systems poses even more
difficulties, it is crucially important to develop solutions before for the seamless
integration of such a real-time planning and control system can be employed. Further
study requires testing this framework on a use case through determination of critical
performance objectives on both tactical and operational level and impact analysis of the
framework on them.
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