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題名 CPFR流程下之訂單預測方法
作者 陳寬茂
Chen, Kuan-Mau
貢獻者 林我聰
Lin, Woo-Tsong
陳寬茂
Chen, Kuan-Mau
關鍵詞 協同規劃預測補貨
訂單預測
演化策略
Collaborative Planning, Forecasting and Replenishment
CPFR
Order forecasts
Evolution strategies
日期 2004
上傳時間 8-十二月-2010 16:03:06 (UTC+8)
摘要 　　協同規劃、預測與補貨（Collaborative Planning, Forecasting and Replenishment; CPFR）是協同商務中一個新發展的應用實務，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。企業需要利用協同合作所獲得之即時資訊來進行預測，減少不確定性因素之影響，提高預測之準確性。CPFR流程下協同預測階段分為銷售預測與訂單預測，兩者之預測項目與目的並不相同且所需要之資訊亦有所差異。銷售預測著重在市場需求部份的預測；訂單預測則是依據銷售預測、存貨狀況與生產面因素來做實際訂單之預測。由於訂單預測作為下個階段之實際補貨的參考，其預測準確性的要求就格外重要。然而研究文獻多偏向CPFR流程架構與導入效益等管理議題，雖有少數針對預測模型之研究，但亦以企業內部銷售預測為主，並未有文獻提出跨企業之協同訂單預測模型，故CPFR流程下訂單預測方法之研究探討有其必要性。本研究以CPFR流程中接續銷售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與訂單預測之相關文獻為基礎，歸納出協同合作下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，透過時間序列、多元迴歸與演化策略法（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。最後以國內某製造業公司與其顧客（一國際大型零售商）之訂單資料進行模型驗證，與單純使用時間序列方法或統計迴歸分析的預測結果作績效評比，實驗顯示本研究所提出之訂單預測方法較傳統使用單一時間序列或統計回歸方法之預測結果佳。
　　Collaborative Planning, Forecasting and Replenishment (CPFR) is nowadays a practice of collaborative commerce, emphasizing buyers and sellers’ coordination for the efficiency of the process in supply chain. Enterprises utilize instant information obtained from coordinate processes to forecast in order to reduce the influence of the uncertain factor and improve forecasting accuracy. The stage of the collaborative forecasting in CPFR process is divided into sales forecasting and order forecasting which make differences on forecasting objective, subject, and information needed. Sales forecasting focuses on the prediction of the market demand; order forecasting is the prediction of the real orders according to sales forecasting, stock state and productive factor. The accuracy of order forecasting is extremely important because it is regarded as the reference of the replenishment at next stag. The literatures about CPFR mostly probe into manage topics like benefits of implementation or process structures though there are some researches on the forecasting model which mainly discuss sales forecasting inside enterprises. Therefore, it is necessary to investigate into the coordinative order forecasting model under CPFR process. This paper regards order forecasting following sales forecasting in CPFR as the theme. Besides generalizing the necessary parameter of order forecasting based on literatures review, the research presents a hybrid forecasting model which considers coordinative information and order forecasting requirements. It integrates the time series model, regression model, and use evolution strategies to determine its coefficients efficiently. The validity of the forecasting model is verified by experiment on order datum from one manufacturer in Taiwan and its international retailer. The results show that the order forecasting model has better forecasting performance than not only the time series model but also the ordinary regression model.
參考文獻 中文部分
[1]　CNET 新聞專區，「麥克波特：網路協同引領下一波電子商務」，Taiwan.CNET.com 網站，2001，
http://taiwan.cnet.com/news/ec/story/0,2000022589,20019105,00.htm
[2] 林宣佐，「運用企業流程語言描述關鍵企業流程之研究－以BPEL4WS應用於CPFR 為例」，國立中正大學資訊管理研究所碩士論文，2003。
[3] 施仁和，「台灣百貨量販業供應鏈管理參考模式之研究」，國立台北科技大學商業自動化與管理研究所碩士論文，2000。
[4] 黃蘭禎，「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所碩士論文，2004。
[5] 陳世運，「淺談電子市集發展主流---協同商務」，FIND，2001。
[6] 陳曉萍，「企業電子畫下協同作業發展之研究」，國立政治大學經營管理學系碩士論文，2002。
[7] 張嘉仁，「進入即時、彈性之「協同商務」時代」，資訊情報，2002。
[8] 廖嘉偉，「一前導性協同預測架構與實施系統之研究」，東海大學工業工程與經營資訊研究所碩士論文，2003。
[9] 薛旭志，「3C零售業導入CPFR模式之研究」，國立高雄第一科技大學行銷與流通管理研究所碩士論文，2003。
[10] 魏志強，「合作式商務的經典CPFR－Wal-Mart和Sara Lee示範案例」，資策會電子商務應用推廣中心。
[11] 羅慕君，「短期訂單預測模型之研究」，中原大學資訊管理所碩士論文，2004。
英文部分
[1] Arminger, G. , “Sales and Order Forecasts in the CPFR process for Retail”, pp. 53-68, 2002.
[2] Brown R.G., “Less Risk in Inventory Estimates”, Harvard Bussiness Review, July-August, pp. 104-116, 1959.
[3] Chaman L Jain, “How much data should we use to prepare forecasts”, The Journal of Business Forecasting Methods and System, 20(4), pp. 2-10, 2002.
[4] Chen, Y. F., Z. Drezner, J. K. Ryan and D. Simchi-Levi, “Quantifying the Bullwhip Effect in a Simple Supply Chain: The Impact of Forecasting, Lead Times and Information”, Management Science, Vol.46, pp. 436–443, 2000.
[5] Deanna Diehn, “Seven Steps to Build a Successful Collaborative Forecasting Process”, 19(4), pp. 23-25, 2001.
[6] Dirk V. Arnold, Hans-Georg Beyer , “A Comparison of Evolution Strategies with Other Direct Search Methods in the Presence of Noise”, Computational Optimization and Applications, 24(1), pp. 135, 2003.
[7] Frank Chen, Zvi Drezner, Jennifer K. Ryan, David Simchi-Levi, “Quantifying the Bullwhip Effect in a Simple Supply Chain: The Impact of Forecasting, Lead Times, and Information,” Management Science, 46(3), pp. 233-242, 2000.
[8] Frank Chen, Zvi Drezner, Jennifer K. Ryan and David Simchi-Levi, “The bullwhip effect: managerial insights on the impact of forecasting on variability in a supply chain,” Quantitative Models for Supply Chain Management, S. Tayur, R. Ganeshan and M. Magazine, Kluwer, pp. 417-439, 1998.
[9] Fred Baumann, “The Power of an Executable Single Forecast”, The JDA Software Group, 2002, available at
http://www.vccassociates.com/articles/jdaexecutable.pdf
[10] Gartner Group, “C-Commerce Q & A”, 2001, available at
http://www.gartner.com/2_events/conferences_briefings/conferences/spc5_qa.pdf.
[11] Hank Steermann, “A practical look at CPFR: The Sears-Michelin experience”, Supply Chain Management Review, 2003.
[12] Jain, “Benchmarking Forecasting Models”, The Journal of Business Forecasting Methods and System, 21(3), pp. 18-20, 2002.
[13] Jain, Chaman L, “Orders or shipments: Which ones to forecast”, The Journal of Business Forecasting Methods and System, 14(4), pp. 2, 1996.
[14] M.Luhtala, E. Kilpinen, P. Anttila, “LOGI-managing make-to-order supply chains”, Helsinki University of Technology, 1994.
[15] Matt Johnson, “Collaboration Data Modeling: CPFR Implementation Guidelines”, The 1999 Annual Conference Proceedings of the Council of Logistics Management, 1999.
[16] Michael Harris, “Defining: Collaborative Commerce”, eAI Journal, March 2002.
[17] Neter, J., Kutner M.H., Nachtsheim, C.J., & Wasserman, W., “APPied Linear Statistical Models, 4th Ed.”, Chicago: Irwin, 1996.
[18] Nolan, K., “For Marmon/Keystone, VMI Offers Real Procurement Solution”, Metal Center News, pp. 50-60, 1997.
[19] Nolan Wilson Jr, “Game plan for a successful collaborative forecasting process”, The Journal of Business Forecasting Methods and System, 20(1), pp. 3-6, 2001.
[20] Rechenberg, “Evolution Strategy”, Computational Intelligence: Imitating Life, IEEE press, 1994.
[21] Schwefel, “Evolution and Optimum Seeking”, Sixth-Generation Computer Technology Series. Wiley, New York, 1995.
[22] Surgency Inc., "Best Practices in Collaborative Design”, White Paper, 2001.
[23] Stank, T. P. and Keller, S. B.,“Supply Chain Collaboration and Logistical Service performance,” Journal of Business Logistics, 22(1), pp.29-45, 2001
[24] Thomas Bäck, “Evolutionary Algorithms in Theory and Practice: Evolution Strategies, Evolutionary Programming, Genetic Algorithms”, Oxford University Press, New York, 1996.
[25] Thomas, K., “Collaborative design: what is it?”, Automation in Construction, 9(4), pp. 409-415, 2000.
[26] Wheelwright, S.C. and Makridakis, S. , “Forecasting Methods for Management”, New York: John Wiley & Sons, Inc, 1973.
網站部分
[1] http://taiwan.cnet.com
[2] http://www.retailsystems.com
[3] http://www.vics.org
描述 碩士
國立政治大學
資訊管理研究所
92356009
93
資料來源 http://thesis.lib.nccu.edu.tw/record/#G0923560091
資料類型 thesis
dc.contributor.advisor 林我聰zh_TW
dc.contributor.advisor Lin, Woo-Tsongen_US
dc.contributor.author (作者) 陳寬茂zh_TW
dc.contributor.author (作者) Chen, Kuan-Mauen_US
dc.creator (作者) 陳寬茂zh_TW
dc.creator (作者) Chen, Kuan-Mauen_US
dc.date (日期) 2004en_US
dc.date.accessioned 8-十二月-2010 16:03:06 (UTC+8)-
dc.date.available 8-十二月-2010 16:03:06 (UTC+8)-
dc.date.issued (上傳時間) 8-十二月-2010 16:03:06 (UTC+8)-
dc.identifier (其他 識別碼) G0923560091en_US
dc.identifier.uri (URI) http://nccur.lib.nccu.edu.tw/handle/140.119/49658-
dc.description (描述) 碩士zh_TW
dc.description (描述) 國立政治大學zh_TW
dc.description (描述) 資訊管理研究所zh_TW
dc.description (描述) 92356009zh_TW
dc.description (描述) 93zh_TW
dc.description.abstract (摘要) 　　協同規劃、預測與補貨（Collaborative Planning, Forecasting and Replenishment; CPFR）是協同商務中一個新發展的應用實務，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。企業需要利用協同合作所獲得之即時資訊來進行預測，減少不確定性因素之影響，提高預測之準確性。CPFR流程下協同預測階段分為銷售預測與訂單預測，兩者之預測項目與目的並不相同且所需要之資訊亦有所差異。銷售預測著重在市場需求部份的預測；訂單預測則是依據銷售預測、存貨狀況與生產面因素來做實際訂單之預測。由於訂單預測作為下個階段之實際補貨的參考，其預測準確性的要求就格外重要。然而研究文獻多偏向CPFR流程架構與導入效益等管理議題，雖有少數針對預測模型之研究，但亦以企業內部銷售預測為主，並未有文獻提出跨企業之協同訂單預測模型，故CPFR流程下訂單預測方法之研究探討有其必要性。本研究以CPFR流程中接續銷售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與訂單預測之相關文獻為基礎，歸納出協同合作下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，透過時間序列、多元迴歸與演化策略法（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。最後以國內某製造業公司與其顧客（一國際大型零售商）之訂單資料進行模型驗證，與單純使用時間序列方法或統計迴歸分析的預測結果作績效評比，實驗顯示本研究所提出之訂單預測方法較傳統使用單一時間序列或統計回歸方法之預測結果佳。zh_TW
dc.description.abstract (摘要) 　　Collaborative Planning, Forecasting and Replenishment (CPFR) is nowadays a practice of collaborative commerce, emphasizing buyers and sellers’ coordination for the efficiency of the process in supply chain. Enterprises utilize instant information obtained from coordinate processes to forecast in order to reduce the influence of the uncertain factor and improve forecasting accuracy. The stage of the collaborative forecasting in CPFR process is divided into sales forecasting and order forecasting which make differences on forecasting objective, subject, and information needed. Sales forecasting focuses on the prediction of the market demand; order forecasting is the prediction of the real orders according to sales forecasting, stock state and productive factor. The accuracy of order forecasting is extremely important because it is regarded as the reference of the replenishment at next stag. The literatures about CPFR mostly probe into manage topics like benefits of implementation or process structures though there are some researches on the forecasting model which mainly discuss sales forecasting inside enterprises. Therefore, it is necessary to investigate into the coordinative order forecasting model under CPFR process. This paper regards order forecasting following sales forecasting in CPFR as the theme. Besides generalizing the necessary parameter of order forecasting based on literatures review, the research presents a hybrid forecasting model which considers coordinative information and order forecasting requirements. It integrates the time series model, regression model, and use evolution strategies to determine its coefficients efficiently. The validity of the forecasting model is verified by experiment on order datum from one manufacturer in Taiwan and its international retailer. The results show that the order forecasting model has better forecasting performance than not only the time series model but also the ordinary regression model.en_US
dc.description.tableofcontents 中文摘要　　　　I
英文摘要　　　　II
目錄　　　　III
表目錄　　　　V
圖目錄　　　　VI
第壹章　緒論　　　　1
1.1　研究背景　　　　1
1.2　研究動機　　　　2
1.3　研究目的　　　　3
1.4　研究範圍　　　　3
1.5　研究流程　　　　3
1.6　章節架構　　　　5
第貳章　文獻探討　　　　6
2.1　協同商務　　　　6
2.2　協同規劃預與補貨（CPFR）　　　　8
2.3　預測概論　　　　15
2.4　訂單預測　　　　18
2.5　演化策略法　　　　21
第參章　模型建構　　　　25
3.1　協同訂單預測模型整體架構　　　　25
3.2　驗證方法與績效指標　　　　　28
3.3　時間序列模型　　　　30
3.4　多元迴歸模型　　　　33
3.5　演化策略模型　　　　38
第肆章　實驗分析與模型驗證　　　　47
4.1　資料蒐集與敘述　　　　47
4.2　產品A預測實驗與績效　　　　48
4.3　產品B預測實驗與績效　　　　56
第伍章 結論與建議　　　　63
5.1　結論　　　　63
5.2　　後續研究方向　　　　64
參考文獻　　　　65

表目錄
表2-1 CPFR流程中針對主導角色的四個情境　　　　9
表2-2 預測模型特性整理　　　　17
表2-3 演化策略法與基因演算法之比較表　　　　24
表3-1 CPFR流程下銷售預測與訂單預測影響因素之差異　　　　37
表4-1 產品Ａ之階段一時間序列模型平滑係數敏感度分析　　　　49
表4-2 產品A之階段二多元迴歸模型係數表　　　　51
表4-3 產品A之階段三演化策略模型第一期初始值　　　　52
表4-4 產品A之演化策略法突變率績效測試前3名　　　　53
表4-5 三階段訂單預測模型產品A之10次實驗績效　　　　53
表4-6 產品A之一般迴歸模型係數表　　　　55
表4-7 產品A之4週預測績效比較　　　　55
表4-8 產品A之8週預測績效比較　　　　56
表4-9 產品B之階段一時間序列模型平滑係數敏感度分析　　　　57
表4-10 產品B之階段二多元迴歸模型係數表　　　　58
表4-11 產品B之階段三演化策略模型第一期初始值　　　　59
表4-12 產品B之演化策略法突變率績效測試前3名　　　　60
表4-13 三階段訂單預測模型產品B之10次實驗績效　　　　60
表4-14 產品B之一般迴歸模型係數表　　　　61
表4-15 產品B之4週預測績效比較　　　　61
表4-16 產品B之8週預測績效比較　　　　62

圗目錄
圖1-1 研究流程圖　　　　4
圖2-1 CPFR協同合作的八個主要任務　　　　10
圖2-2 協同預測關係圖　　　　18
圖2-3 產生訂單預測的資料流　　　　20
圗2-4 演化策略法之簡化流程圖　　　　22
圖3-1 協同訂單預測模型架構圖　　　　26
圖3-2 離散型重組運算結果　　　　41
圖3-3 中間產物型重組運算結果　　　　42
圖3-4 本研究演化策略模型虛擬碼　　　　45
圖3-5 本研究演化策略模型流程圖　　　　46
圗4-1 本研究模型各階段實驗資料區間示意圖　　　　47
圗4-2 產品Ａ之實際訂單量與時間序列訂單預測曲線圖　　　　49
圗4-3 演化策略法演化代數之績效收斂趨勢　　　　52
圗4-4 產品B之實際訂單量與時間序列訂單預測曲線圖　　　　57		zh_TW
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dc.source.uri (資料來源) http://thesis.lib.nccu.edu.tw/record/#G0923560091en_US
dc.subject (關鍵詞) 協同規劃預測補貨zh_TW
dc.subject (關鍵詞) 訂單預測zh_TW
dc.subject (關鍵詞) 演化策略zh_TW
dc.subject (關鍵詞) Collaborative Planning, Forecasting and Replenishmenten_US
dc.subject (關鍵詞) CPFRen_US
dc.subject (關鍵詞) Order forecastsen_US
dc.subject (關鍵詞) Evolution strategiesen_US
dc.title (題名) CPFR流程下之訂單預測方法zh_TW
dc.type (資料類型) thesisen
dc.relation.reference (參考文獻) 中文部分zh_TW
dc.relation.reference (參考文獻) [1]　CNET 新聞專區，「麥克波特：網路協同引領下一波電子商務」，Taiwan.CNET.com 網站，2001，zh_TW
dc.relation.reference (參考文獻) http://taiwan.cnet.com/news/ec/story/0,2000022589,20019105,00.htmzh_TW
dc.relation.reference (參考文獻) [2] 林宣佐，「運用企業流程語言描述關鍵企業流程之研究－以BPEL4WS應用於CPFR 為例」，國立中正大學資訊管理研究所碩士論文，2003。zh_TW
dc.relation.reference (參考文獻) [3] 施仁和，「台灣百貨量販業供應鏈管理參考模式之研究」，國立台北科技大學商業自動化與管理研究所碩士論文，2000。zh_TW
dc.relation.reference (參考文獻) [4] 黃蘭禎，「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所碩士論文，2004。zh_TW
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