NEURAL NETWORKS

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NEURAL NETWORKS

By

Steven Susanto 1-1208-078

A Thesis submitted In Partial Fulfillment of the Requirements for BACHELOR OF SCIENCE

DEPARTMENT OF INDUSTRIAL ENGINEERING

FACULTY OF ENGINEERING

SWISS GERMAN UNIVERSITY EduTown BSDCity

Tangerang 15339 INDONESIA Telp. +62 21 3045 0045

Fax. +62 21 3045 0001 E-mail: [email protected]

www.sgu.ac.id

2012

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STATEMENT BY THE AUTHOR

I hereby declare that this submission is my own work and to the best of my knowledge, contains no material previously published or written by another person, nor material which to a substantial extent has been accepted for the award of any other degree or diploma at any educational institution, except where due acknowledgement is made in the thesis.

________________________________________ __________________

Steven Susanto Date

Approved by:

________________________________________ __________________

Dr. Ir. Adhi S. Soembagijo, MSME Date

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Dr. Ir. Prianggada I. Tanaya, MME Date

________________________________________ __________________

Chairman of the Examination Steering Committee Date

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ABSTRACT

FORMULATING STANDARD PRODUCT LEAD TIME AT A TEXTILE FACTORY USING ARTIFICIAL NEURAL NETWORKS

By

Steven Susanto

SWISS GERMAN UNIVERISTY Bumi Serpong Damai

Dr. Ir. Adhi S. Soembagijo, MSME, Thesis Advisor Dr. Ir. Prianggada I. Tanaya, MME, Thesis Co-Advisor

Product lead time (PLT) is difficult to be estimated in the textile industry due to problems, such as incomplete data, large product variation, and non-linearity in the time-affecting factors. This thesis proposed a methodology to formulate product lead time of textile fabric production at a textile factory using artificial neural networks.

Analysis of the order fulfillment process flow of the textile company was conducted to identify the individual sequential processes that constitute product lead time. Feed forward multilayer perceptron (MLP) neural networks are developed to estimate the lead time of critical PLT processes with incomplete data and various non-linear time- affecting factors. The networks are trained in a supervised manner using back propagation algorithm. The finalized neural network models are able to estimate the lead time for each process with a good degree of accuracy and can be used as a decision making tool for quoting product lead time to customer.

Keywords: Product Lead Time, Textile Production, Artificial Neural Networks,

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DEDICATION

I dedicate this thesis to my parents, my lecturers, and all my dear friends who have supported me during the making of this thesis. Without their support and constant encouragement, the completion of this thesis would not be possible.

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ACKNOWLEDGMENTS

The author wishes to take this opportunity to express his sincere gratitude to a number of people for their support and encouragement throughout the development of this thesis.

First and foremost, the author wishes to express his deepest gratitude to Dr. Ir. Adhi S. Soembagijo, MSME, and Dr. Ir. Prianggada I. Tanaya, MME, for their constant support and invaluable guidance throughout the entire progress of this thesis. Without their patience and positive encouragement, the completion of this thesis would not be possible.

A special thank goes to the director of PT Sandatex Wenpindo, Mrs Aida, and the rest of the staff at the company for their valuable assistance and guidance. Their support contributed a great deal towards the successful completion of this thesis.

The author would also like to thank all his dearest friends and the lecturers at Swiss German University, whose company and moral support helped encouraged him to overcome the various challenges faced throughout the process of completing this thesis.

Last, but certainly not the least, the author wishes to thank his parents and the rest of his family for their constant and loving support throughout this entire experience.

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TABLE OF CONTENTS

STATEMENT BY THE AUTHOR……… 2

ABSTRACT……… 3

DEDICATION……… 4

ACKNOWLEDGEMENT……….. 5

LIST OF TABLES……….. 10

LIST OF FIGURES………... . 11

CHAPTER 1 – INTRODUCTION……… . 15

1.1 Background………. 15

1.2 Thesis Purpose………. 16

1.3 Research Question………... 16

1.4 Thesis Scope……… 16

1.5 Thesis Limitation………. 17

1.6 Significance of Study……….. 18

1.7 Thesis Organization………. 18

CHAPTER 2 – LITERATURE REVIEW……….. 20

2.1 Introduction………. 20

2.2 Product Lead Time……….. 20

2.3 Conventional Standard Time Formulation Methods……….. 22

2.4 Regression Analysis……… 25

2.5 Artificial Neural Networks………. 27

2.5.1 Constructing Neural Networks………33

2.5.2 Application of Neural Networks in the Textile Industry… 36 2.6 Concluding Remarks………37

CHAPTER 3 – METHODOLOGY……… 39

3.2 Analyze Process Flow of the company to fulfill customer order…… 40

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3.2.2 Phase 2: Production planning………. 44

3.2.3 Phase 3: Textile fabric production……….. 45

3.2.4 Phase 4: Delivery of order to customer………...46

3.2.5 Product Lead Time Formulation………. 47

3.3 Select processes to be modeled using Neural Networks………. 48

3.4 Overview of Artificial Neural Networks……… 51

3.4.1 Neural Network Paradigm……….. 52

3.4.2 Supervised Learning with back propagation……….. 54

3.5 Neural Network Simulation Tool……… 56

3.5.1 Overview of Scilab………. 56

3.5.2 Scilab ANN Toolbox……….. 56

3.6 Determine Paradigm of Neural Networks………... 57

3.6.1 Model 1: Standard Color Matching Time………...… 58

3.6.2 Model 2: Standard Knitting Time……….. 59

3.6.3 Model 3: Standard Dyeing Time……… 60

3.7 Prepare Input Data Set……… 61

3.7.1 Standard Time Data Collection……….. 62

3.7.2 Normalize input data set………. 62

3.8 Train and Test the Neural Networks………63

3.8.1 Flowchart of training program……… 64

3.8.2 Determine the optimum size of train data……….. 65

3.8.3 Determine the optimum number of nodes in the hidden layer……… 66

3.8.4 Determine the optimum number of hidden layer………… 66

3.8.5 Determine the optimum learning rate………. 66

3.8.6 Determine the optimum momentum factor……… 67

3.9 Evaluate Neural Network Performance……….. 67

3.10 Concluding Remarks……….. 68

CHAPTER 4 – RESULT AND DISCUSSION……….. 69

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Time………. 73

4.3.2 Determine the optimum number of nodes in the hidden layer……….76

4.3.6 Finalize the network……… 83

4.4 Training and test result for Model 2: Standard Knitting Time……… 85

4.4.2 Determine the optimum number of nodes in the hidden layer……… 88

4.4.6 Finalize the network……… 97

4.5 Training and test result for Model 3: Standard Dyeing Time………. 98

4.5.2 Determine the optimum number of nodes in the hidden layer………... 102

4.5.3 Determine the optimum number of hidden layer……….. 105

4.5.4 Determine the optimum learning rate……… 106

4.5.5 Determine the optimum momentum factor……….. 109

4.5.6 Finalize the network……….. 110

4.6 General evaluation and comparison of finalized network…………. 112

4.7 Evaluation of Network Performance………..113

4.8 Concluding Remarks……….. 116

CHAPTER 5 – CONCLUSION AND RECOMMENDATION……….. 118

5.1 Conclusion………. 118

5.2 Recommendation for future work………. 119

GLOSSARY……….. 121

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APPENDIX A Result of discussion and interview with representative

from PT Sandatex Wenpindo………. 125

APPENDIX B Input data sets collected from the company………... 127

APPENDIX C Scilab training and test program coding………. 143

CURRICULUM VITAE ……….. 147