Responsive image
博碩士論文 etd-0806111-095502 詳細資訊
Title page for etd-0806111-095502
論文名稱
Title
建置監控導向系統模擬模式以提升統計製程管制效能之研究
A Study of Deploying Monitor-Oriented System Simulation Models to Improve the Efficiency of Statistical Process Control
系所名稱
Department
畢業學年期
Year, semester
語文別
Language
學位類別
Degree
頁數
Number of pages
120
研究生
Author
指導教授
Advisor
召集委員
Convenor
口試委員
Advisory Committee
口試日期
Date of Exam
2011-07-15
繳交日期
Date of Submission
2011-08-06
關鍵字
Keywords
統計檢定、統計抽樣、系統模擬、製程創新、製程能力、統計製程管制
System Simulation, Statistical Test, SPC, Process Capacity, Process Innovation, Statistical Sampling
統計
Statistics
本論文已被瀏覽 5967 次,被下載 2585
The thesis/dissertation has been browsed 5967 times, has been downloaded 2585 times.
中文摘要
統計製程管制發展由來已久,在許多製造業生產環境中均可見其踪影。然而統計製程管制應用在製程管制普遍僅限於管制圖的導入應用,對於管制圖的深化能力如製程能力的管制、偵測與評估甚少著墨,因此常讓統計製程管制技術淪於形式。同時統計製程管制雖可了解生產製程的製程變異情形,但卻無法整合生產資源的應用能力。雖然製程監控對生產製程可達到即時管制作用,但對生產製程資源因應未來需求預測所必要的產能分析,卻顯得相當不足。
本研究針對統計製程管制技術結合系統模擬技術提出創新管理模式。透過生產製程觀測與樣本收集記錄,考慮統計製程管制能力對系統的變異狀態,結合系統模擬技術的應用,探討統計製程管制技術在資源限制與考慮資源配置下,對系統製程的可行性和應用性提出說明,並利用品質改進手法與系統動力學之因果回饋圖考慮資源動態能力對管理決策的影響。研究結果發現:
1、應用系統模擬技術能有效模擬出實際生產作業流程,並依據輸入的參數按照系統模擬模型產生有效的輸出。
2、統計工具可以提高樣本資料做為系統模擬參考的重要方法。經由適當的資料驗證,可以得到信度更高的模擬果。
3、線上統計製程管制透過系統模擬技術得以即時預測製程監控、生產資源的配置和預期產能狀況。
Abstract
The development of statistical process control has been for a long time and can be turned up in many manufacturing environments. However, statistical process control applications in process control generally limited to use the control chart applications, the deepening capacity for control charts such as process capability control, variation detection and evaluation, are rarely described so often so that statistical process control techniques is relegated. Meanwhile, statistical process control can detect the production process of the variations, but it can’t integrate the production resource capacity. Although the process control of manufacturing processes can achieve real-time control of effects, but the resources of the production process appeared to be quite inadequate in response to future demand forecast and capacity analysis.
Therefore, this study combined with statistical process control system simulation technology for innovative management. Through the process observation and sample collection, we can use simulation technology to propose the process feasibility and applicability in resource constraint and resource allocation for considering the variation of the statistical process control, and use the quality improvement tools and causal feedback map, the system dynamics tools, in the resource dynamic ability for decision-making management.
The research result appears:
1、Based on the effective input parameters of simulation model , it can effectively simulate the actual production processes and produce an effective output.
2、Through the appropriate statistical data validation, it can improve the sample reliability as an important reference to system simulation methods.
3、Using the simulation technology, we can monitor the online process control, production resources allocation and capacity prediction.
目次 Table of Contents
目錄
誌謝 ................................................................... II
中文摘要 .......................................................... III
ABSTRACT .................................................... IV
目錄 .................................................................. V
圖次 ................................................................ VII
表次 ............................................................... VIII
第一章緒論 ....................................................... 1
第一節研究背景與動機.................................... 1
第二節研究目的 ............................................... 2
第三節研究步驟 ............................................... 2
第四節章節組織 ............................................... 3
第二章文獻探討 ............................................... 5
第一節統計製程管制 ....................................... 5
第二節系統模擬技術 ..................................... 10
第三節系統模擬技術與統計製程管制 ......... 20
第三章研究架構 ............................................. 23
第一節問題與系統範圍.................................. 23
第二節樣本收集與分析.................................. 24
第三節模擬模式 ............................................. 31
第四節資源準則描述 ..................................... 42
第五節變異分析與因果回饋關係 ................. 47
第四章模擬系統建立與分析 ......................... 54
第一節個案公司簡介 ..................................... 54
第二節概念化模型 ......................................... 59
第三節模型轉換與驗證.................................. 60
第四節模擬結果分析與探討 ......................... 65
第五章結論 ..................................................... 74
參考文獻 ......................................................... 77
附錄 ................................................................. 80
參考文獻 References
參考文獻
1. 顏月珠. (1983). 應用數理統計學. 台北: 三民書局.
2.Antony, J. J., and Taner, T. G. (2003). A Conceptual Framework for the Effective Implementation of Statistical Process Control.Business Process Management Journal, 9(4), 473-489.
3. Banks, J., Carson, J. S., Nelson, B. L., and Nicol, D. M. (2009). Discrete-Event System Simulation (5th ed.): Prentice Hall.
4. Besterfield, D. H. (2008). Quality Control (8 ed.): Prentice Hall.
5. Chakraborti, S., Human, S. W., and Graham, M. A. (2009). Phase I Statistical Process Control Charts: An Overview and Some Results. Quality Engineering, 21(1),52-62.
6. Chan, L. K., Hapuarachchi, K. P., and Macpherson, B. D. (1988). Robustness of Xbar and R Charts. IEEE Transactions on Reliability, 37(1), 117-123.
7. Chen, H., and Cheng, Y. Y. (2009). Designing X-Bar Charts for Known Autocorrelations and Unknown Marginal Distribution. European Journal of Operational Research, 198(2), 520-529.
8. Elg, M., Olsson, J., and Dahlgaard, J. J. (2008). Implementing Statistical Process Control: an Organizational Perspective. International Journal of Quality & Reliability Management, 25(6), 545-560.
9. Frederick, S. H. (1969). X-Bar and R-Chart Control Limits Based on A Small Number of Subgroups. Journal of Quality Technology, 1(1), 17-25.
10. Grant, E., and Leavenworth, R. (1996). Statistical Quality Control (7 ed.):McGraw-Hill.
11. Hamada, M. (2003). Tolerance Interval Control Limits for X Bar, R, and S Charts.Quality Engineering, 15(3), 471-487.
12. Hawkins, D. M., and Zamba, K. D. (2005). Statistical Process Control for Shifts in Mean or Variance Using a Changepoint Formulation. Technometrics, 47(2),164-173.
13. Ishikawa, D. (1990). Introduction to Quality Control: Productivity Press.
14. Jahangirian, M., Eldabi, T., Naseer, A., Stergioulas, L. K., and Young, T. (2010). Simulation in Manufacturing and Business: A review.European Journal of Operational Research, 203(1), 1-13.
15. Kumar, R., and Andreas, R. (2004). Analyzing Variability in Continuous Processes. European Journal of Operational Research, 156(2), 312-325.
16. Langenberg, P., and Lglewicz, B. (1986). Trimmed Mean X-Bar and R Charts. Journal of Quality Technology, 18(3).
17. Law, A., and Kelton, W. D. (2000). Simulation Modeling and Analysis (3th ed.): McGraw-Hill.
18. Mason, B., and Antony, J. (2000). Statistical Process Control: An Essential Ingredient for Improving Service and Manufacuring Quality. Managing Service Quality, 10(4), 233-238.
19. Michael, S., Eamonn, M., and Eileen, D. (1996). Statistical Quality Control and Improvement. European Journal of Operational Research, 88(2), 203-214.
20. Nelson, L. S. (1984). The Shewhart Control Chart--Tests for Special Causes. Journal of Quality Technology, 16(4), 237-239.
21. Nelson, L. S. (1985). Interpreting Shewhart X-bar Control Charts. journal of Quality Technology, 17(2), 114-116.
22. Neuhardt, J. B. (1987). Effects of Correlated Sub-Samples in Statistical Process Control. IIE Transactions, 19(2), 208 - 214.
23. Pegden, C. D., Shannon, R. E., and Sadowski, R. P. (1995). Introduction to Simulation Using Siman (2th ed.): McGraw-Hill.
24. Pidd, M. (1992). Computer Simulation in Management Science (3th ed.): John Wiley & Sons.
25. Quesenberry, C. (1993). The Effect of Sample Size on Estimated Limits for and X Control Charts. Journal of Quality Technology, 25(4), 237-247.
26. Ramaker, H. J., van Sprang, E. N. M., Westerhuis, J. A., Gurden, S. P., Smilde, A. K., and van der Meulen, F. H. (2006). Performance Assessment and Improvement of Control Charts for Statistical Batch Process Monitoring. Statistica Neerlandica,60(3), 339-360.
27. Ramirez, B., and Runger, G. (2006). Quantitative Techniques to Evaluate Process Stability. Quality Engineering, 18(1), 53-68.
28. Robinson, S. (2007). A Statistical Process Control Approach to Selecting a Warm-Up Period for a Discrete-Event Simulation. European Journal of OperationalResearch, 176(1), 332-346.
29. Rockwell, A. (2010a). Arena Packaging User's Guide
30. Rockwell, A. (2010b). Arena User's Guide
31. Rockwell, A. (2010c). Arena Variables Gide
32. Ross, S. M. (2006). Simulation (4th ed.): Academic Press.
33. Wester Electric Company. (1958). Statistical Quality Control Handbook: Western Electric Co. Inc.
34. White, D. J. (1975). Decision Methodology: Formalization of the Operational Research Process: John Wiley & Sons.
35. Woodall, W. H., and Montgomery, D. C. (2000). Using Ranges to Eetimate Variability. Quality Engineering, 13(2), 211-217.
36. Xie, M., Goh, T. N., and Cai, D. Q. (2001). An Integrated SPC Approach for Manufacturing Processes. Integrated Manufacturing Systems, 12(2), 134-138.
37. Yang, J. F. (2009). Autocorrelation's Effect on Process Capability Analysis. Asian Journal on Quality, 10(3), 65-72.
38. Yang, K., and Walton, M. H. (1990). Statistical Quality Control for Correlated Samples. International Journal of Production Research, 28(3), 595.
電子全文 Fulltext
本電子全文僅授權使用者為學術研究之目的,進行個人非營利性質之檢索、閱讀、列印。請遵守中華民國著作權法之相關規定,切勿任意重製、散佈、改作、轉貼、播送,以免觸法。
論文使用權限 Thesis access permission:自定論文開放時間 user define
開放時間 Available:
校內 Campus: 已公開 available
校外 Off-campus: 已公開 available


紙本論文 Printed copies
紙本論文的公開資訊在102學年度以後相對較為完整。如果需要查詢101學年度以前的紙本論文公開資訊,請聯繫圖資處紙本論文服務櫃台。如有不便之處敬請見諒。
開放時間 available 已公開 available

QR Code