製鞋主要可分為三段製程：裁加、針車、成型，每一段都可視為獨立的生產線，其中針車是很重要的一部分。針車包含的工序複雜，加上機台自動化程度不高，所以它需倚賴大量的人力操作機台。在針車線的生產管理面，常見的議題有：生產數量的設定、加工批量的選擇、人力配置方法…等，然而這些參數設定的好與壞不是那麼的直觀，在不知道哪種設定是最佳條件的情況下，工廠主管往往透過他們過去的經驗來作判斷及抉擇。
本研究致力於建構一具代表性的製鞋業針車線模擬模型，並透過模型來探討不同生產參數對於鞋業績效指標的影響，常見績效指標如：每日產量、每人每小時生產雙數、生產週期時間以及在製品水準。研究結果顯示，不同參數水準的設定對於系統績效指標的確有顯著的影響，而透過實驗設計的手法，本研究也找出了不同生產狀況下對應的最佳的參數組合。

There are three major processes in footwear manufacturing factories: cutting/preparation, stitching and assembling, each process can be considered as a single production line. Among these, stitching is the most critical process because it contains many complex manufacturing processes which are not easy to be automated. A typical stitching process relies on many skilled workers to operator the stitching machines, it is the process that requires the most labors in footwear manufacturing factories. When talking to the management of stitching line, there are some issues regarding to parameters settings such as production target, batch size and human resource arrangement, etc. However, it is not easy to know which parameters setting is the optimal design for the current production line. Therefore, factory managers usually make decisions and set those parameters settings based on their past experiences.
This research plans to develop a representative simulation model for footwear stitching line and justify the effect of the different parameters settings on the Key Performance Indicators (KPIs) such as output per day, Pairs Produced per Hour (PPH), cycle time and Work-In-Process (WIP) level. The simulation results show that different parameters settings do have significant impact on the KPIs, and through the techniques of experimental design, this research also identified the optimal parameters settings for the factory.

Abstract I
摘要 II
致謝 III
Table of Contents IV
List of Tables V
List of Figures VI
Chapter 1 Introduction 1
1.1. Background 1
1.2. Objective 4
1.3. Organization of Thesis 4
Chapter 2 Literature Review 5
2.1. Simulation Modeling 5
2.2. Existing Study in Footwear Industry 7
Chapter 3 Simulation Model 11
3.1. Problem Definition 11
3.2. Assumption 15
3.3. Model Specifications 15
3.4. Data Collection 18
3.5. Verification and Validation 18
Chapter 4 Output Analysis 21
4.1. Experiment Design 21
4.2. Experimental Results and Analysis 23
4.3. Tukey Comparison 34
4.4. Regression Analysis 35
4.5. Optimal Design for Footwear Stitching Line 37
Chapter 5 Conclusion 42
Reference 45

Chang, E. S. (2014). Simulation modeling and analysis of footwear production lines. Master’s thesis, Department of Industrial Engineering and Engineering Management, National Tsing Hua University.
Chen, J. C., Anggono, N., Putra, A. P., Chou, R., & Chang, E. S. (2014). Simulation modeling and analysis of line length and batch size in footwear factory. International Conference on Industrial Engineering and Operations Management, 1173-1180.
Chen, J. C., Lee, S. H., Chen, C. Y., Chen, C. C., & Peng, T. W. (2013). Simulation‐based look‐ahead release planning for color filter fabs. Journal of the Society for Information Display, 21(1), 60-70.
Chen, J. C., Parwananta, H., Thao, T. D. D., Wang, K. J., Anityasari, M., & Chen, C. C. (2012). Branch and bound method for solving line balancing problem in footwear sewing line. Asia Pacific Industrial Engineering & Management Systems Conference, 1061-1069.
Chen, J. C., Putra, A. P., Anggono,N., Chen, J., & Su, Y. S. (2014). Simulation modeling and analysis for stitching line of footwear industry. International Conference on Industrial Engineering and Operations Management, 1099-1106.
Chen, J. C., Putra, A. P., Anggono, N., Wang, K. J., Lin, S. W., & Chen, C. C. (2014). Flexible simulation model for multi-style shoes manufacturing: a case study. The Fourth International Asia Conference on Industrial Engineering and Management Innovation, 999-1008.
Chen, J. C., Sun, C. J., & Chen, T. L. (2014). Capacity planning for integrated circuit final test plants. International Journal of Computer Integrated Manufacturing, (ahead-of-print), 1-13.
Chen, J. C., Wirasambada, S., Pujawan, I. N., Wang, K. J., Wu, C. W., Lin C. J., & Chiu, S. (2012). Minimizing total tardiness and makespan for hybrid flow shop problem with resource constraint in footwear manufacturing. Asia Pacific Industrial Engineering & Management Systems Conference, 1093-1099.
Eryilmaz, M. S., Kuşakci, A. O., Gavranovic, H., & Findik, F. (2012). Analysis of shoe manufaturing factory by simulation of production processes. Southeast Europe Journal of Soft Computing, 1(1), 120-127.
Fornasiero, R., Zangiacomi, A., & Avai, A. (2004). Web cost simulation service for footwear sector. International Journal of Computer Integrated Manufacturing, 17(7), 661-667.
Hopp, W. J., & Roof, M. L. (1998). Setting WIP levels with statistical throughput control (STC) in CONWIP production lines. International Journal of Production Research, 36(4), 867-882.
Hung, Y. F., & Leachman, R. C. (2012). A production planning methodology for semiconductor manufacturing based on iterative simulation and linear programming calculations. IEEE Transactions on Semiconductor Manufacturing, 9(2), 257-269.
Jacobson, I. D., Townsend, M. A., & Allatta, J. T. (1993). Evaluation of new technologies in a shoe manufacturing plant using simulation. Simulation, 61(5), 303-313.
Law, A. M. (2009). How to build valid and credible simulation models. Winter Simulation Conference, 24-33.
Law, A. M., & McComas, M. G. (1991). Secrets of successful simulation studies. Winter Simulation Conference, 21-27.
Lin, J. T., Wang, F. K., & Wu, C. K. (2003). Simulation analysis of the connecting transport AMHS in a wafer fab. IEEE Transactions on Semiconductor Manufacturing,16(3), 555-564.
Lin, Y. K., Chang, P. C., & Chen, J. C. (2013). Performance evaluation for a footwear manufacturing system with multiple production lines and different station failure rates. International Journal of Production Research, 51(5), 1603-1617.
Mak, L. C., Wong, W. K., & Leung, Y. S. (2014). A simulation analysis of the impact of production lot size and its interaction with operator competence on manufacturing system performance. Simulation Modelling Practice and Theory,49, 203-214.
Marques, A. F., Mujica, M., de Sousa, J. P., Marques, P. S., Rebelo, R., & Alves, A. C. (2013). An innovative framework for the simulation of manufacturing systems: an application to the footwear industry. Advances in Sustainable and Competitive Manufacturing Systems, 219-231.
Nisanci, H. I., & Sury, R. J. (1980). Production analysis by simulation in a shoe manufacturing factory, International Journal of Production Research, 18(1), 31-41.
Portuguese Footwear, Components, Leather Goods Manufacturers’ Association (APICCAPS). (2014). World Footwear Yearbook.
Sivakumar, A. I., & Chong, C. S. (2001). A simulation based analysis of cycle time distribution, and throughput in semiconductor backend manufacturing. Computers in Industry, 45(1), 59-78.
Voorhorst, F. A., Avai, A., & Boër, C. R. (2012). Optimizing a highly flexible shoe production plant using simulation. Use Cases of Discrete Event Simulation, 131-145.