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題名 再製造活動策略之下之CPFR訂單預測模型探討
作者 吳三裕
貢獻者 林我聰
吳三裕
關鍵詞 逆向物流
存貨管理
CPFR
訂單預測
再製造
日期 2007
上傳時間 6-May-2016 16:37:19 (UTC+8)
摘要 協同規劃、預測與補貨（Collaborative Planning, Forecasting and Replenishment; CPFR），主要強調供應鏈上買賣雙方協同合作流程的概念，以提升供應鏈上流程的處理效率。然而CPFR流程中多數研究著重在正向供應鏈的協同預測的架構、導入效益與預測方法等議題，但對於當企業活動中包含逆向物流-再製造活動策略下時的協同預測方法少有提之，因此與其相關之研究探討有其必要性。使得在未來訂購零件不確定性增添下，希望能藉由新的短期訂單方法能找出最佳的預測值幫助企業與供應商能加強協同規劃，提升供應鏈競爭力。
     
     本研究以CPFR 流程中協同預測之訂單預測階段為研究主題，蒐集近年來國內外研究CPFR 與訂單預測與逆向物流下的存貨管理之相關文獻為基礎，歸納出再製造活動下的協同合作訂單預測所須具備之屬性與影響因素，並作為模型解釋變數，透過逆向物流最適批量模型、多元回歸與演化策略法（Evolution Strategies）的結合，建構一個統整供應鏈上、下游協同資訊與符合CPFR 流程下訂單預測特性之預測模型。最後範例資料進行計算與分析此預測模型，並提出一套方法論提供決策者在進行補貨時，能以本研究提供的決策程序進行規劃與分析訂單預測量。
參考文獻 中文文獻
     
     1. 于宗先，民61 ，經濟預測，大中國圖書有限公司
     2. 王文俊，1997，認識Fuzzy，全華科技圖書股份有限公司。
     3. 王立志，民95 系統化運籌與供應鏈管理-企業營運新典範，滄海書局。
     4. 吳子逢、民92，使用直交實驗設計的高效率演化策略及其應用，逢甲大學資訊工程所碩士論文。
     5. 呂柏賢、民89，灰色需求預測模式之研究─以易腐性商品為例，東海大學工業工程學系碩士論文。
     6. 李順益、民91，灰色理論於短期銷售預測之適用性探討，義守大學資訊工程學系碩士論文。
     7. 林坤德、民93，逆物流再製造存貨策略之最佳經濟生產與訂購批量之探討，雲林科技大學工業工程與管理研究所碩士論文。
     8. 林思宏、民89，非線性系統之預測控制研究，國立成功大學機械工程學系博士論文。
     9. 洪維謙、民95，逆向物流下供應鏈管理策略對成本之影響，國立政治大學資訊管理研究所碩士論文。
     10. 留英龍 、民89，模糊理論應用於地層下陷之預測，國立成功大學海洋工程學系碩士論文。
     11. 張育偉、民92，以知識地圖為基礎發展的遠距測驗選題策略，中原大學資訊工程研究所碩士論文。
     12. 許純君(譯)，DeLurgio,S.A.著，1999，預測的原則與應用，台灣西書有限公司。
     13. 陳寬茂、民94，CPFR流程下之訂單預測方法，國立政治大學資訊管理研究所碩士論文。
     14. 曾永勝、民94，CPFR銷售預測模式之探討，國立政治大學資訊管理研究所碩士論文。
     15. 黃肆海、民92，混合型模擬退火法於結構工程之應用，國立成功大學航空太空工程學系碩博士班碩士論文。
     16. 黃慨郁、民國95，供應鏈網路中考量回收機制之主規劃排程演算法，國立台灣大學資訊管理研究所碩士論文。
     17. 黃蘭禎、民93，CPFR流程下之銷售預測方法~混合預測模型，國立政治大學資訊管理研究所碩士論文。
     18. 溫士城、民95，逆向物流下的最佳經濟訂購批量與製造批量，國立政治大學資訊管理研究所碩士論文。
     19. 鄭偉平、民94，華文圖書出版的供應鏈管理---探討逆物流，高雄第一科技大學運輸與倉儲營運系碩士論文。
     20. 羅慕君，民93, 短期訂單預測模型之研究—PDA產業為例,中原大學資訊管理研究所碩士論文。
     
     英文文獻
     
     1. Aviv, Y. ,“Gaining Benefits from Joint Forecasting and Replenishment Processes: the Case of Auto-Correlated Demand,” Manufacturing & Service Operations Management,Vol.4, 2002,No.1,pp.55-74
     2. Aviv, Y. ,”The Effect of Collaborative Forecasting on Supply Chain Performance,” Management Science, Vol.47, No.10, 2001,pp.1326-1343,
     3. Carter CR, Ellram L M . “Reverse Logistics: a Review of the Literature and Framework for Future Investigation ” Journal of Business Logistics, 19(1) ,1998 , pp.85-102.
     4. Chamber, J. C., S. K. Mullick, and D. D. Smith, “How to Choose the Right Forecasting Technique, ”H.B.R. July-Aug 1971.
     5. Charles, C.W. and Chase, Jr. ,“Composite Forecasting: Combing Forecasts for Improved Accuracy,” The journal of business forecasting, Vol.19, ,2000, pp.2,20-22,
     6. Cohen, M. Replace, “rebuild or remanufacture, Equipment Management,” 16(1), 1988 , pp.22-26.
     7. D. V. Arnold and H.-G. Beyer, “Local Performance of the (1 + 1)-ES in a Noisy Environment,” IEEE Trans. Evolutionary Computation, vol. 6, no. 1, Feb. 2002 , pp. 30-41
     8. D. V. Arnold and H.-G. Beyer, “Performance analysis of evolution strategies with multi-recombination in high-dimensional RN-search spaces disturbed by noise,” Theoretical Computer Science 289, 2002 , pp. 629–647,
     9. Delurgiio. Stephen A, “Forecasting Principles and Applications. Kansas ”：Irwin.,1999
     10. Dowlatshahi S, “Developing a Theory of Reverse Logistics,” Interfaces, Vol. 30 No.3, 2000 , pp.143-55
     11. Erwin A. van der Laan a,*, Ruud H. Teunter b,1, “Simple heuristics for push and pull remanufacturing policies”, European Journal of Operational Research 175 , 2006 , pp. 1084–1102
     12. Fleischmann, M., H. R. Krikke, R. Dekker, and S. P. Flapper, “A characterization of logistics networks for product recovery,” Omega,28(6) , 2000 , pp.653-666.
     13. Fleischmann, M. Bloemhof-Ruwarrd,J .M. Dekker,R. van der Laan,E.、van Nunen,J.A.E.E. van Wassenhove,L.N., “Quantitative models for reverse logistics: A review”, European Journal of Operational Research,” 103(1) , 1997 , pp.1-17
     14. Forecasting—Collaborative forecasting supports supply chain
     15. H.-G. Beyer, “Toward a Theory of Evolution Strategies: On the Benefits of (μ/ρ,λ)-Evolution Strategies,” Evolution Computation, vol. 3, no.1 , 1995 , pp.81-111,.
     16. H.-G. Beyer, The Theory of Evolution Strategies. Springer, 2001.
     17. Helms, M. ,Ettkin, L. P. and Chapman, S. ,“Supply Chain
     18. Holmström, J. ,Främling, K. ,Kaipia, R. and Saranen, J. ,“Collaborative Planning Forecasting and Replenishment: New Solutions Needed for Mass Collaboration,” the Journal of Supply Chain Management ,Vol.7, No.3, 2002, pp.136-145
     19. Inderfurth K. 1 ; van der Laan E. ,“Leadtime effects and policy improvement for stochastic inventory control with remanufacturing”. International Journal of Production Economics, 6 May 2001, vol. 71, no. 1, pp. 381-390(10)
     20. Jain, L. ,”Benchmarking forecasting models,” The Journal of Business Forecasting, Methods and System,Vol.21,No.3, 2002 , pp.18-20,30,
     21. Koh, Shie-Gheun; Hwang, Hark; Sohn, Kwon-Ik; and Ko Chang-Seong ,“An optimal ordering and recovery policy for reusable items ”, Computers & Industrial Engineering, 43, 2002 , pp.59-43
     22. Levary R.R, and Han D. ,“Choosing a Technological Forecasting Method”, Foecasting January, February, 1995 ,pp.14-18
     23. MaCarthy, T. M. and Golicic, S. L.,”Implementing Collaborative Forecasting to Improve Supply Chain Performance,” International Journal of Physical Distribution & Logistics Management, Vol.32,No.6, 2002,pp.431-454
     24. Makridakis S, Hibon M, “The M3-competition and implications“, International Journal of Forecasting, 2000 ,pp.451-476
     25. Makridakis, S. and Wheelwright, S. C. ,“Forecasting Methods for Management,” 5th ed., N.Y.: John Wiley & Sons, 1997 , pp.571-582.
     26. management,” Business Process Management Journal, Vol.6, Iss.5, 2000, pp.392-394
     27. Marien, E. J. ,“Reverse logistics as competitive strateg,”, Supply Chain management review, 2(1), 1998 , pp.43-52.
     28. MC Mabini, LM Pintelon, LF Gelders ,“EOQ type formulations for controlling repairable inventories ,“ International Journal of Production Economics ,1992 , pp.21-33
     29. Mentzer T. J and Achrol R. S ,“The Structure of Commitment in Exchange” journal of Marketing, 1995, pp.78-92
     30. Ozturkmen, Z. A. ,“Forecasting in the Rapid Changing Telecommunications Industry: AT&T’s Experience,” The journal of business forecasting,Vol.19,No.3, 2000, pp.3-4,
     31. RH Teunter*, “Lot-sizing for inventory systems with product recovery,” Computers & Industrial Engineering 46 , 2004 , pp.431-441
     32. RH Teunter, D. Vlachos, “On the necessity of a disposal option for returned items that can be remanufactured,” International Journal of Production Economics, vol. 75, 2002 , pp 257-266,
     33. Richter,K.,“The EOQ Repair and Waste Disposal Model with Variable Setup Numbers”. European Journal of Operational Research, 95(2), 1996 , pp.313-324.
     34. S Nahmias, H Rivera , “A deterministic model for a repairable item inventory system with a finite repair rate,” International Journal of Production Research, 1979
     35. Schrady DA. , “A deterministic inventory model for repairable items,” Naval Research Logistics Quarterly 14,1967 ,pp.391-398
     36. Seifert, D, “Collaborative Planning, Forecasting, and Replenishment: How to Create a Supply Chain Advantage, ” Foresight: The International Journal of Applied Forecasting , 2 (October), 2005 pp.48-49
     37. van der Laan, E., and M. Salomon, Production planning and inventory control with remanufacturing and disposal, European Journal of Operation Research, 102(2): , 1997 , pp.64-278.
     38. van der Laan,E. and Salomon, M., “Production planning and inventory control with remanufacturing and disposal”, European Journal of Operational Research,102(2), 1997 , pp.264-278
描述 碩士
國立政治大學
資訊管理學系
94356028
資料來源 http://thesis.lib.nccu.edu.tw/record/#G0094356028
資料類型 thesis
dc.contributor.advisor 林我聰zh_TW
dc.contributor.author (Authors) 吳三裕zh_TW
dc.creator (作者) 吳三裕zh_TW
dc.date (日期) 2007en_US
dc.date.accessioned 6-May-2016 16:37:19 (UTC+8)-
dc.date.available 6-May-2016 16:37:19 (UTC+8)-
dc.date.issued (上傳時間) 6-May-2016 16:37:19 (UTC+8)-
dc.identifier (Other Identifiers) G0094356028en_US
dc.identifier.uri (URI) http://nccur.lib.nccu.edu.tw/handle/140.119/94434-
dc.description (描述) 碩士zh_TW
dc.description (描述) 國立政治大學zh_TW
dc.description (描述) 資訊管理學系zh_TW
dc.description (描述) 94356028zh_TW
dc.description.abstract (摘要) 協同規劃、預測與補貨（Collaborative Planning, Forecasting and Replenishment; CPFR），主要強調供應鏈上買賣雙方協同合作流程的概念，以提升供應鏈上流程的處理效率。然而CPFR流程中多數研究著重在正向供應鏈的協同預測的架構、導入效益與預測方法等議題，但對於當企業活動中包含逆向物流-再製造活動策略下時的協同預測方法少有提之，因此與其相關之研究探討有其必要性。使得在未來訂購零件不確定性增添下，希望能藉由新的短期訂單方法能找出最佳的預測值幫助企業與供應商能加強協同規劃，提升供應鏈競爭力。
     
     本研究以CPFR 流程中協同預測之訂單預測階段為研究主題，蒐集近年來國內外研究CPFR 與訂單預測與逆向物流下的存貨管理之相關文獻為基礎，歸納出再製造活動下的協同合作訂單預測所須具備之屬性與影響因素，並作為模型解釋變數，透過逆向物流最適批量模型、多元回歸與演化策略法（Evolution Strategies）的結合，建構一個統整供應鏈上、下游協同資訊與符合CPFR 流程下訂單預測特性之預測模型。最後範例資料進行計算與分析此預測模型，並提出一套方法論提供決策者在進行補貨時，能以本研究提供的決策程序進行規劃與分析訂單預測量。		zh_TW
dc.description.tableofcontents 第一章 緒論..................................................................................................................1
     1.1 研究背景.........................................................................................................1
     1.2 研究動機.........................................................................................................3
     1.3 研究目的.........................................................................................................5
     1.4 研究方法與研究範圍.....................................................................................5
     1.5 論文架構..........................................................................................................6
     第二章 文獻探討..........................................................................................................8
     2.1協同規劃、預測與補貨.....................................................................................8
     2.1.1協同規劃、預測與補貨之定義................................................................8
     2.1.2 CPFR之處理流程....................................................................................10
     2.1.3 CPFR之效益............................................................................................12
     2.2預測方法........................................................................................................13
     2.2.1 預測概論................................................................................................13
     2.2.2預測方法選擇與趨勢.............................................................................15
     2.2.3因果分析-多元回歸分析.......................................................................17
     2.3最佳演算法....................................................................................................19
     2.3.1最佳演算法之比較.................................................................................19
     2.3.2演化策略法之介紹.................................................................................21
     2.3.3編碼與適應函式.....................................................................................22
     2.3.4重組(recombination)...........................................................................22
     2.3.5突變.........................................................................................................23
     2.3.6選擇與終止.............................................................................................24
     2.4逆向物流管理(reverse logistics management)..........................26
     2.4.2逆向物流定義.........................................................................................26
     2.4.3逆物流的存貨管理.................................................................................26
     2.4.4決定性存貨管理.....................................................................................28
     2.4.5隨機存貨管理.........................................................................................32
     2.5模糊理論.........................................................................................................36
     2.5.1模糊理論之介紹.....................................................................................36
     2.5.2模糊控制系統.........................................................................................38
     2.5.3模糊化（Fuzzifierion）.........................................................................39
     2.5.4模糊規則庫（Fuzzy Rule Base）.............................................................42
     2.5.5推理引擎(Inference Engine)..............................................................43
     2.5.6解模糊化（Defuzzifierion）.................................................................45
     2.5.7小結.........................................................................................................45
     第三章 研究步驟與研究模型建置.......................................................................47
     3.1 研究步驟與流程............................................................................................47
     3.2混合預測整體模式架構.................................................................................50
     3.2.1步驟一 多元回歸模型變數建立...........................................................51
     3.2.2步驟二 模糊物料訂購總量模式...........................................................55
     3.2.3步驟三、四 演化策略法求最佳化之混合預測模型.............................66
     第四章 實驗設計........................................................................................................71
     4.1實驗績效與驗證.............................................................................................71
     4.2資料來源與使用之分配.................................................................................71
     4.3模糊物料訂購總量績效驗證.........................................................................73
     4.3.1 1-28週系統參數績效驗證....................................................................73
     4.3.2演化代數選擇.........................................................................................73
     4.3.3突變率.....................................................................................................77
     4.3.4模糊物料訂購總量模式績效.................................................................79
     
     4.4多元回歸.........................................................................................................85
     4.4.1多元回歸參數分析.................................................................................85
     4.4.2 演化策略模型........................................................................................86
     4.4.3 績效檢視................................................................................................88
     第五章 結論與未來研究方向....................................................................................92
     5.1 研究結論........................................................................................................92
     5.2未來研究方向.................................................................................................93
     參考文獻......................................................................................................................94		zh_TW
dc.source.uri (資料來源) http://thesis.lib.nccu.edu.tw/record/#G0094356028en_US
dc.subject (關鍵詞) 逆向物流zh_TW
dc.subject (關鍵詞) 存貨管理zh_TW
dc.subject (關鍵詞) CPFRzh_TW
dc.subject (關鍵詞) 訂單預測zh_TW
dc.subject (關鍵詞) 再製造zh_TW
dc.title (題名) 再製造活動策略之下之CPFR訂單預測模型探討zh_TW
dc.type (資料類型) thesisen_US
dc.relation.reference (參考文獻) 中文文獻
     
     1. 于宗先，民61 ，經濟預測，大中國圖書有限公司
     2. 王文俊，1997，認識Fuzzy，全華科技圖書股份有限公司。
     3. 王立志，民95 系統化運籌與供應鏈管理-企業營運新典範，滄海書局。
     4. 吳子逢、民92，使用直交實驗設計的高效率演化策略及其應用，逢甲大學資訊工程所碩士論文。
     5. 呂柏賢、民89，灰色需求預測模式之研究─以易腐性商品為例，東海大學工業工程學系碩士論文。
     6. 李順益、民91，灰色理論於短期銷售預測之適用性探討，義守大學資訊工程學系碩士論文。
     7. 林坤德、民93，逆物流再製造存貨策略之最佳經濟生產與訂購批量之探討，雲林科技大學工業工程與管理研究所碩士論文。
     8. 林思宏、民89，非線性系統之預測控制研究，國立成功大學機械工程學系博士論文。
     9. 洪維謙、民95，逆向物流下供應鏈管理策略對成本之影響，國立政治大學資訊管理研究所碩士論文。
     10. 留英龍 、民89，模糊理論應用於地層下陷之預測，國立成功大學海洋工程學系碩士論文。
     11. 張育偉、民92，以知識地圖為基礎發展的遠距測驗選題策略，中原大學資訊工程研究所碩士論文。
     12. 許純君(譯)，DeLurgio,S.A.著，1999，預測的原則與應用，台灣西書有限公司。
     13. 陳寬茂、民94，CPFR流程下之訂單預測方法，國立政治大學資訊管理研究所碩士論文。
     14. 曾永勝、民94，CPFR銷售預測模式之探討，國立政治大學資訊管理研究所碩士論文。
     15. 黃肆海、民92，混合型模擬退火法於結構工程之應用，國立成功大學航空太空工程學系碩博士班碩士論文。
     16. 黃慨郁、民國95，供應鏈網路中考量回收機制之主規劃排程演算法，國立台灣大學資訊管理研究所碩士論文。
     17. 黃蘭禎、民93，CPFR流程下之銷售預測方法~混合預測模型，國立政治大學資訊管理研究所碩士論文。
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