development of dynamic simulation modelfor inventory...

DEVELOPMENT OF DYNAMIC SIMULATION MODELFOR INVENTORY

LEVEL OPTIMIZATION THROUGH SUPPLY CHAIN

MARYAM MOFARRAHI

A project report submitted in partial fulfilment of the

requirements for the award of the degree of

Master of Engineering (Industrial Engineering)

Faculty of Mechanical Engineering

Universiti Teknologi Malaysia

JUNE 2013

iii

Thanks God for giving me the strength and health in doing this research and I would

like to dedicate this dissertation to my beloved parents who taught me how to be

strong and ambitious. It is also dedicated to my best friends who their positive

energies and supports always lead me in the best way.

iv

ACKNOWLEDGEMENTS

I would like to express my deep appreciation to my supervisor, Dr. Syed

Ahmad Helmi bin Syed Hassan for his valuable guidance and comments during this

study.

Warm thanks to the manager and personnel of Electronic Company for their

support and providing data that are used in model analysis.

Last but not least, thanks to all my friends, and to all people who have

involved directly and indirectly in making my research project become flourishing

and successful.

v

ABSTRACT

System dynamic is an approach to understanding the behavior of complex

systems over time. Current research on system dynamics modeling in supply chain

management focuses on inventory decision and policy development, time

compression, demand amplification, supply chain design and integration, and

international supply chain management. Less attention has been devoted on the

inventory level improvement with fuzzy demand controller in dynamic simulation

model. This study is aimed to consider customer demand fluctuation to improve the

finished goods inventory level of Electronic Company by using dynamic simulation

model. Dynamic and changes in demand and corresponding excess during time

through the product life is considered as serious problems in supply chain. Data

collection and analysis have been considered to find the current problems of the

company. Dynamic model has been constructed by ITHINK software to represent the

inventory level of company. The stock and flow diagrams are become visible to

represent the structure of a system with more detailed information. Three different

variables have been applied in Fuzzy controller to give the better level of inventory

by MATLAB SIMULINK. Generated results have been compared by current

company inventory level in ITHINK software and the best alternative was selected to

suggest the company management.

vi

ABSTRAK

Sistem Dinamik merupakan pendekatan untuk memahami perlakuan bagi

sistem yang kompleks dalam satu jangka masa. Kajian-kajian semasa bagi sistem

dinamik dalam pengurusan rantaian bekalan adalah menjurus kepada membuat

keputusan dalam inventori dan pembangunan polisi, pemampatan masa,

pengembangan permintaan, rekabentuk dan integrasi rantaian pembekal, dan

pengantarabangsaan pengurusan rantaian pembekal. Penumpuan kajian kepada

peningkatan tahap inventori melalui kawalan permintaan “fuzzy” dalam simulasi

model dinamik adalah sangat terhad. Kajian ini bertujuan untuk meningkatkan

prestasi penentuan tahap invetori barangan siap bagi sebuah syarikat elektronik

dengan pertimbangan permintaan pelanggannya yang tidak menentu, menggunakan

simulasi model dinamik. Dinamika serta perubahan dalam permintaan dan lebihan

semasa dalam jangkahayat produk merupakan satu masalah yang serius dalam

rantaian bekalan. Dapatan serta analisis data berkaitan dengan rantaian bekalan

telah dilakukan untuk menentukan masalah semasa yang dihadapi oleh syarikat ini.

Model dinamik telah dibina menggunakan perisian iThink bagi menwakili tahap

inventori syarikat. Rajah “stock” dan “flow” mewakili sturktur sistem yang dibina

digambarkan dengan jelas dengan perincian informasi yang lebih ketara. Tiga

pemboleh ubah yang berbeza telah digunakan dalan kawalan “fuzzy” untuk

menghasilkan penetapan tahap inventori yang lebih baik, dengan bantuan perisian

MATLAB SIMULINK. Dengan mengunakan perisian iThink, keputusan yang

terjana ini dibandingkan dengan tahap inventori semasa syarikat. Alternatif yang

terbaik telah dipilih dan dicadangkan kepada pihak pengurusan syarikat.

vii

TABLE OF CONTENTS

CHAPTER TITLE PAGE

DECLARATION ii

DEDICATION iii

ACKNOWLEDGEMENTS iv

ABSTRACT v

ABSTRAK vi

TABLE OF CONTENTS vii

LIST OF TABLES xi

LIST OF FIGURE xii

1 INTROUDUCTION 1

1.1 Introduction 1

1.2 Background of Study 2

1.3 Statement of Problem 3

1.4 Objective of the Study

1.5 Scope of Study

4

5

1.6 Significance of the Study 6

1.7 Conclusion 6

2 LITERATURE REVIEW 8

2.1 Introduction 8

2.2 Conceptual of Supply Chain Management

2.2.1 Supply Chain

2.2.1.1 Decision Levels in Supply Chain

8

9

10

viii

Management

2.2.1.2 Production-distribution problem in

supply chain

2.2.2 Systems, Model and Simulation

2.2.2.1 Time Handling in Dynamic

Simulation Models

2.2.2.2 Difference Equations in Discrete

Time Models

2.2.2.3 Differential Equations in Continuous

Time Models

2.2.2.4 Computer Simulation Models Time

Advance Methods

2.2.2.5 Executable Timelines

2.2.2.6 Deterministic and Stochastic

Simulation

2.2.3 System Dynamics

2.2.3.1 System Dynamics Modeling Tools

2.2.3.2 System Dynamics Software Tools

2.2.4 Supply Chain Modeling

2.3 Conclusion

13

15

17

18

19

20

20

21

22

23

25

26

32

3 RESEARCH METHODOLOGY 33

3.1 Introduction 33

3.2 Operational Framework 33

3.2.1 Phase I

3.2.2 Phase II

3.2.3 Phase III

3.2.3.1 Fuzzy Controller

3.2.3.2 MATLAB SIMULINK

3.3 Conclusion

35

36

40

40

41

47

4 DATA ANALYSIS 46

4.1 Introduction 46

ix

4.2 Case Study

4.2.1 Organization Chart

4.3 Dynamic Simulation Model of Company through

Supply Chain

4.3.1 CLD Diagram

4.3.2 Stock and Flow Diagram

4.4 Design and Implementation of Fuzzy Controller

4.4.1 Membership Function of Input and Output

4.4.1.1 Input I

4.4.1.2 Input II

4.4.1.3 Input III

4.4.1.4 Membership Function of Output

4.4.2 Creating Rule Base

4.4.3 Implementation of Fuzzy Controller in

MATLAB

4.5 SIMULINK Inventory Systems in MATLAB

4.5.4 SIMULINK Case Model

4.5.4.1 Scenario 1

4.5.4.2 Scenario 2

4.5.4.3 Scenario 3

4.6 Verification

4.7 Validation

4.7 Conclusion

47

47

48

48

50

54

55

55

56

56

57

58

60

61

61

62

65

68

70

72

74

5

RESULTS, DISCUSSION, CONCLUSIONSAND

RECOMMENDATIONS

5.1 Introduction

5.2 Result

5.2.1 MATLAB SIMULINK Results

5.2.1.1 Scenario 1

5.2.1.2 Scenario 2

5.2.1.3 Scenario 3

5.2.2 Dynamic Model in Supply Chain

75

75

76

76

76

77

78

78

x

6

5.2.2.1 Scenario 1

5.2.2.2 Comparison before and after

Improvement for Scenario1

5.2.3.1 Scenario 2


Improvement for Scenario 2

5.2.3.3 Scenario 3


Improvement for Scenario 3

5.3 Conclusion

5.4 Future Research

5.5 Discussion

REFERENCE

79

80

82

83

85

86

88

90

91

93-95

xi

LIST OF TABLES

TABLE NO. TITLE PAGE

2.1 Summary of the techniques and tools applied for

modeling the supply chain

29

3.1 Summary of the objectives, activities and output of each

phase

44

‎4.1

4.2

Weekly Demands of each Lamp Type, First Scenario

Total Weekly Demands First Scenario

63

64

4.3 SIMULINK’s‎Inputs 65

‎4.4 Weekly Demands of each Lamp Type, Second Scenario 65

4.5

4.6

4.7

4.8

4.9

4.10

5.1

5.2

5.3

Total Weekly Demands for all lamps, Second Scenario

SIMULINK’s‎Inputs

Weekly Demands for Each Lamp Types, Third Scenario

Total Demands for all lamps, Third Scenario

SIMULINK’S‎Inputs‎3

Current Finished Goods Inventory, Company Result

Total Weekly Customer Demand for Scenario 1

Total Weekly Customer Demand, Scenario 2

Total Weekly Customer Demand, Scenario 3

66

67

68

69

70

73

79

83

86

xii

LIST OF FIGURES

FIGURE NO. TITLE PAGE

2.1 Supply Chain Decision Level 10

2.2 Forward and Backward Flow in Supply Chain 13

‎2.3 Sample Causal loop Diagram (CLD) 23

3.1 Research Operational Framework 33

‎3.2 Literature review parts 34

3.3 Dynamic Modeling of supply chain in details 35

‎3.4 SIMULINK Library Browsers 42

‎3.5

3.6

3.7

3.8

Constant Block

Arranging Constant Values

Fuzzy Logic Controllers Block

Display Block

43

43

44

44

4.1

4.2

4.3

‎4.4

4.5

4.6

4.7

4.8

4.9

4.10

Company Organization Chart

Casual loop diagram of the supply chain

Stock and Flow Diagram of the Supply Chain

Membership Function of Variation of Customer

Demand

Membership Function of Average of Customer Demand

Membership Function of Customer Demand

Membership Function of Output

SIMMULINK Model

Weekly Demands of each Lamp Type, First Scenario

Total Weekly Demands First Scenario

47

48

50

55

56

57

57

62

63

64

xiii

4.11

4.12

4.13

4.14

4.15

4.16

4.17

5.1

5.2

5.3

5.4

5.5

5.6

5.7

5.8

5.9

5.10

5.11

5.12

Weekly Demands of each Lamp Type, Second Scenario

Total Weekly Demands for all lamps, Second Scenario

Weekly Demands for Each Lamp Types, Third Scenario

Total Demands for all lamps, Third Scenario

Finished Goods Inventory

Current Finished Goods Inventory, iThink Result

Current Finished Goods Inventory, Company Result

SIMULINK Model, Scenario1

SIMULINK Model, Scenario 2

SIMULINK Model, Scenario 3

Finished Goods Inventory, before Improvement,

Scenario1

Finished Goods Inventory, after Improvement,

Scenario1

Comparison before and after optimization for second

scenario, Scenario 1


Scenario2

Finished Goods Inventory, after Improvement,

Scenario2


scenario, Scenario 2


Scenario 3

Finished goods inventory, after improvement

forscenario3


scenario for scenario 3

66

67

69

70

71

72

73

77

78

78

81

81

82

84

84

86

87

87

88

CHAPTER 1

INTRODUCTION

1.1 Introduction

In recent years, supply chain has received increasing attention from both

researchers and practitioners. A supply chain is a network of suppliers,

manufacturing sites, distribution centres, retailers through which raw materials are

acquired, transformed, and delivered to customers. Supply chain management is to

respond to the rapid change and severe competition environment in the marketplaces,

enhance the efficiency of enterprises, reduce the cost of inventories and improve

customers’ satisfaction, which exerts great influence on business. The supply chain

starts with raw-material supplier and ends at the consumers. The coordination of

cross-enterprise is an important and difficult task in supply chain management. One

of the critical issues in supply chain management is how to characterize such a

complex dynamic behaviour in supply chain network and assure product availability

throughout the chain. It makes dynamics modelling an ideal tool to model supply

chain networks.Dynamic modelling is a computer-aided approach for analysing and

solving complex problems with a focus on policy analysis and design which has its

origins in control engineering and management; the approach uses a perspective

based on information feedback and delays to understand the dynamic behaviour of

complex physical, biological, and social systems.

2

This chapter presents an introduction of this research work reported in this

study. It covers many important aspects. First, it describes the background of the

problem, followed by the problem statements and objectives of the research. Next, it

describes the scope of the research work, and finally it explains the significance of

the research.

1.2 Background of the Study

Before investigating the problems in modelling of supply chain, it is

important to have an overview of the general related aspects. This section begins

with the overview of supply chain. Next, system modelling is described.

Furthermore, it continues through defining supply chain modelling. Finally, it would

be focused on using dynamic modelling in supply chain.

A supply chain consists of all stages including the manufacturer and

suppliers, transporters, warehouses, retailers, and customers to satisfy a customer

demand. Modelling refers to the process of generating a model of some real world

entity, processor system. Traditionally, the formal modelling of systems to predict

their behaviour has been done via a mathematical model which attempts to find

analytical solutions enabling the prediction from a set of parameters and initial

conditions. For many systems, however, simple closed form analytic solutions are

not possible. This is the point at which computer simulation models come into play.

The modelling and analysis of supply chain has been an active area of research for

many years. Supply chains are multifaceted structures focusing on the integration of

all the factors involved in the overall process of production and distribution of end

products to the customers. Growing interest in supply chain systems has highlighted

the need to adopt appropriate approaches that can ensure the efficient management of

their complexity, enormity and broadness of scope(Shee, Tang et al. 2000). The

purpose of supply chain management is to optimize the performance of supply

3

chains, therefore development of modelling frameworks can be utilized to analyse

and comprehend the dynamic behaviour of supply chains.

Industrial dynamics, described as the application of feedback concepts to

social systems, is evolving toward a theory of structure in systems as well as being

an approach to corporate policy design. Dynamic modelling is appropriate for supply

chain modelling because supply chains are dynamic. It means that they display

distinctive behaviour that is known to vary through time. They are interactive

because system components interact with one another, and their interaction produces

the distinctive behaviour of the system. In this Study, finished goods target level in

inventory is investigated because, inventory is an important factor which should be

considered as a main aspect that influence on delivery lead time(Özbayrak,

Papadopoulou et al. 2007).

1.3 Statement of the Problem

Strategic supply chain management deals with a wide range of issues and

includes several types of decision-making problems that affect the long-term

development and operations of a firm, namely the determination of number,

throughputs of machines and production capacity, warehouses capacity and flow of

material through the logistics network, inventory management policies, distribution

strategies, supply chain integration and customer delivery lead time. The

methodological approach which is dynamic modelling could be used for capturing

most of the named supply chain management problems. Supply chains are dynamic

because they show different behaviour during the time. So, it makes dynamics

modelling as a proper approach to model supply chain networks.

4

Demand fluctuations leads to have high inventory level in warehouse which

causes high cost. However, less attention has been devoted on the inventory level

optimization with Fuzzy demand controller in dynamic simulation model. A

comprehensive dynamic model with Fuzzy demand is applied to fulfill the mentioned

gap.This research develops a dynamic model of an electronic company through

Supply Chainto optimize level of finished goods inventory through the developed

system modelling. Inventory is an important factor which should be considered as a

main aspect that influence on delivery lead time. The research questions of this

project are as follow:

1. How does a dynamic model of supply chain can be represented for an

electronic company?

2. How to optimize inventory level of finished goods through the dynamic

model of the system?

1.4 Objectives of the Study

Based on the problem statements mentioned above, this research

encompasses a set of objectives that is associated with the milestones of the research

process. The research objectives are mentioned below:

1. To develop a dynamic model of an electronic company through Supply Chain

that illustrates system behavior of finished goods inventory related to demand

2. To optimize the inventory level of finished goods inventory through the

developed system modeling with the assisting of present’s optimization

software.

5

1.5 Scope of the Study

Scope of the study includes method that addresses dynamic modelling of

supply chain for an electronic company that is located in Johor Baharu, and its

production is lamp. In order to develop a dynamic model through the supply chain

system, IThink software is used to construct the dynamic model. Also, MATLAB

SIMULINK software through Fuzzy Controller for optimization is applied. Supplier

order sizes, order threshold, raw material on order, number of lines, number of

production, actual customer order are performance parameters are collected from the

electronic company.

The available data from the company is used to optimize inventory level of

finished goods in three scenarios (the first four months, the second four months, and

the third four months).The study is just a proposed model using the available data

from company (not a real implementation). After simulating the model, this research

optimizesthe level of finished goods inventory by using MATLAB SIMULINK

through the Fuzzy controller.

1.6 Significance of the study

Supply chains are dynamic because they display distinctive behaviour that is

known to vary through time. They are interactive because system components

interact with one another, and their interaction produces the distinctive behaviour of

the system. In supply chains, dynamics are produced by different types of interaction,

for instance buyer-supplier interaction, interaction between the user and the provider

of information services (Shee, Tang et al. 2000), etc. They are complex because

many objects interact in the system of interest, and their individual dynamics need

careful consideration and analysis. The operations performed within a supply chain

are a function of a great number of key variables which often seem to have strong

6

interrelationships. The ability of understanding the network as a whole, analysing the

interactions between the various components of the integrated system and eventually

supplying feedback without decomposing it makes dynamics modelling an ideal tool

to model supply chain networks (Özbayrak, Papadopoulou et al. 2007).

This research developed and simulated a dynamic model of supply chain for

an electronic company to optimize inventory level of finished goods. Therefore, the

result of this research is useful to determine a proper target level of finished goods.

This model is not only used for an electronic company, but also used for other

companies as well.

1.7 Conclusion

In summary, supply chains are dynamic because they show different behavior

during the times. So, it makes dynamics modeling a proper approach to model supply

chain networks. This research simulates a dynamic model of supply chain for an

electronic company to optimize the inventory level of finished goods. Inventory is an

important factor which should be considered as a main aspect that influence on cost

and time.

93

REFERENCES

Alvarez, A., D. Solis, et al. (2006). System dynamics simulation of the expansion of

the Panama Canal. Simulation Conference, 2006. WSC 06. Proceedings of

the Winter, IEEE.

Ashayeri, J., R. Keij, et al. (1998). "Global business process re-engineering: a system

dynamics-based approach." International Journal of Operations & Production

Management18(9/10): 817-831.

Beamon, B. M. (1998). "Supply chain design and analysis: Models and methods."

International Journal of Production Economics55(3): 281-294.

Beamon, B. M. and V. C. P. Chen (2001). "Performance analysis of conjoined supply

chains." International journal of production research39(14): 3195-3218.

Bowersox, D. J. and D. J. Closs (1996). Logistical management: the integrated

supply chain process, McGraw-Hill New York.

Byrne, M. and M. Bakir (1999). "Production planning using a hybrid simulation–

analytical approach." International Journal of Production Economics59(1):

305-311.

Cohen, M. A. and H. L. Lee (1988). "Strategic analysis of integrated production-

distribution systems: Models and methods." Operations Research36(2): 216-

228.

Cooper, M. C., D. M. Lambert, et al. (1997). "Supply chain management: more than

a new name for logistics." International Journal of Logistics Management,

The8(1): 1-14.

Drucker, P. F. (1998). "Management’s new paradigms." Forbes Magazine10: 98.

Dudley, R. G. and C. S. Soderquist (1999). A simple example of how system

dynamics modeling can clarify, and improve discussion and modification, of

model structure. Written version of presentation to the 129th Annual Meeting

of the American Fisheries Society, Charlotte, North Carolina.

Evans, G. N., M. M. Naim, et al. (1998). "Application of a simulation methodology

to the redesign of a logistical control system." International Journal of

Production Economics56: 157-168.

Forrester, J. W. (1968). "Industrial dynamics—after the first decade." Management

Science14(7): 398-415.

Ganeshan, R. and T. P. Harrison (1995). "An introduction to supply chain

management." Penn State University, The United States.

Higuchi, T. and M. D. Troutt (2004). "Dynamic simulation of the supply chain for a

short life cycle product—Lessons from the Tamagotchi case." Computers &

Operations Research31(7): 1097-1114.

Jain, S., R. W. Workman, et al. (2001). Supply chain applications II: development of

a high-level supply chain simulation model. Proceedings of the 33nd

conference on Winter simulation, IEEE Computer Society.

Johnson, G. and L. Malucci (1999). Shift to supply chain reflects more strategic

approach. APICSÐThe Performance Advantage, October.

Kim, B. and S. Kim (2001). "Extended model for a hybrid production planning

approach." International Journal of Production Economics73(2): 165-173.

Kleijnen, J. P. C. (2005). "Supply chain simulation tools and techniques: a survey."

International Journal of Simulation and Process Modelling1(1): 82-89.

94

Lambert, D. M. and M. C. Cooper (2000). "Issues in supply chain management."

Industrial marketing management29(1): 65-83.

Lambert, D. M., M. C. Cooper, et al. (1998). "Supply chain management:

implementation issues and research opportunities." International Journal of

Logistics Management, The9(2): 1-20.

Lee, H. L. and C. Billington (1993). "Material management in decentralized supply

chains." operations research41(5): 835-847.

Mabert, V. A. and M. Venkataramanan (1998). "Special Research Focus on Supply

Chain Linkages: Challenges for Design and Management in the 21st

Century*." Decision Sciences29(3): 537-552.

Márquez, A. C. (2010). Dynamic Modelling for Supply Chain Management: Dealing

with Front-end, Back-end and Integration Issues, Springer.

Minegishi, S. and D. Thiel (2000). "System dynamics modeling and simulation of a

particular food supply chain." Simulation Practice and Theory8(5): 321-339.

Moyaux, T., B. Chaib-Draa, et al. (2004). Multi-agent simulation of collaborative

strategies in a supply chain. Proceedings of the Third International Joint

Conference on Autonomous Agents and Multiagent Systems-Volume 1, IEEE

Computer Society.

Nagurney, A., K. Ke, et al. (2002). "Dynamics of supply chains: a multilevel

(logistical-informational-financial) network perspective." Environment and

Planning B29(6): 795-818.

Özbayrak, M., T. C. Papadopoulou, et al. (2007). "Systems dynamics modelling of a

manufacturing supply chain system." Simulation Modelling Practice and

Theory15(10): 1338-1355.

Park, B., H. Choi, et al. (2007). "Integration of production and distribution planning

using a genetic algorithm in supply chain management." Analysis and Design

of Intelligent Systems using Soft Computing Techniques: 416-426.

Park, Y. (2005). "An integrated approach for production and distribution planning in

supply chain management." International Journal of Production

Research43(6): 1205-1224.

Paterson, T. S., S. Holtzman, et al. (2000). Method of generating a display for a

dynamic simulation model utilizing node and link representations, Google

Patents.

Persson, F. and J. Olhager (2002). "Performance simulation of supply chain designs."

International Journal of Production Economics77(3): 231-245.

Pidd, M. (1996). Tools for thinking: modelling in management science, Wiley

Chichester.

Safaei, A., S. M. M. Husseini, et al. (2010). "Integrated multi-site production-

distribution planning in supply chain by hybrid modelling." International

Journal of Production Research48(14): 4043-4069.

Sarker, R., S. Kara, et al. (2005). A Multi-agent Simulation Study for Supply Chain

Operation. Computational Intelligence for Modelling, Control and

Automation, 2005 and International Conference on Intelligent Agents, Web

Technologies and Internet Commerce, International Conference on, IEEE.

Shanthikumar, J. and R. Sargent (1983). "A unifying view of hybrid

simulation/analytic models and modeling." Operations Research31(6): 1030-

1052.

Shee, D. Y., T. I. Tang, et al. (2000). "Modeling the supply-demand interaction in

electronic commerce: A bi-level programming approach." Journal of

Electronic Commerce Research1(2): 79-93.

95

Shreckengost, R. C. (1985). "Dynamic Simulation Models: How Valid Are They?"

Self-Report Methods of Estimating Drug Use: Current Challenges to

Validity. National Institute on Drug Abuse Research Monograph57: 63-70.

Swaminathan, J. M., S. F. Smith, et al. (1994). Modeling the dynamics of supply

chains. Proceedings of AAAI-94 SIGMAN Workshop on Intelligent

Manufacturing Systems.

Tarokh, M. and M. Golkar (2006). Supply chain simulation methods. Service

Operations and Logistics, and Informatics, 2006. SOLI'06. IEEE International

Conference on, IEEE.

Thomas, D. J. and P. M. Griffin (1996). "Coordinated supply chain management."

European Journal of Operational Research94(1): 1-15.

Vidal, C. J. and M. Goetschalckx (1997). "Strategic production-distribution models:

A critical review with emphasis on global supply chain models." European

Journal of Operational Research98(1): 1-18.

Wang, J. and Y. F. Shu (2005). "Fuzzy decision modeling for supply chain

management." Fuzzy Sets and systems150(1): 107-127.

Williams, E. J. and A. Gunal (2003). Supply chain simulation software I: supply

chain simulation and analysis with SimFlex™. Proceedings of the 35th

conference on Winter simulation: driving innovation, Winter Simulation

Conference.

Zeigler, B. P., H. Praehofer, et al. (2000). Theory of modeling and simulation:

Integrating discrete event and continuous complex dynamic systems,

Academic Pr.

development of dynamic simulation modelfor inventory...

Documents