Loading...
|
Please use this identifier to cite or link to this item:
https://nccur.lib.nccu.edu.tw/handle/140.119/31128
|
| Title: | CPFR銷售預測模式之探討 |
| Authors: | 曾永勝 |
| Contributors: | 林我聰
曾永勝 |
| Keywords: | 協同規劃、預測與再補貨
銷售預測
混合預測模型
類神經網路
演化策略法
Collaborative Planning, Forecasting and Replenishment
Sales Forecasting
Mixed Forecasting Structure
Artificial Neural Network
Evolution Strategy |
| Date: | 2005 |
| Issue Date: | 2009-09-14 09:18:30 (UTC+8) |
| Abstract: | 協同規劃、預測與再補貨(Collaborative Planning, Forecasting and Replenishment; CPFR)，是目前供應鏈管理下重要的討論議題；台灣近年來由於加入WTO與製造業外移使競爭壓力加劇，全球運籌需求提升，使廠商間的合作更加密切，且近年來企業資訊環境與基礎建設逐漸成熟，有助於協同商務之發展。在CPFR流程與供應鏈協同作業環境下，一個供需雙方協同且績效良好的銷售預測具有關鍵的重要性，是管理決策與協同合作時的重要依據；但是多數的企業並沒有一個結構化、有系統化的預測流程及方法，進行多點且不同方法之預測，這樣的銷售預測較無穩定的品質，亦較難提供管理者合理的數據解釋。
在CPFR流程下，強調買賣雙方透過完整、即時資訊的交流，進行短期、單一銷售預測，以提供雙方後續訂單預測、訂單補貨等決策的依據。本研究利用演算法（類神經網路和演化策略法）找出更適合混合性預測架構的解釋變數，再以較適合於實數解之演化策略法於修改黃蘭禎（2004）的三階段之預測模型架構，最後採用實驗方法，進行模型績效驗證。
Collaborative Planning, forecasting and replenishment (CPFR) is an important issue of supply chain management currently. Because of the severer competition resulted from entrance into WTO and industry integration, cooperation between Taiwanese companies becomes more intensely; enterprises’ information environment and foundation construction attain to maturity also boost the development of collaboration business. In CPRF process and supply chain operation environment, it is critical that a good performance sale forecasting collaborated by both supplier and buyer sides, and it is also the basis of policy decision and collaboration. However, the majority of the companies lack for a structural and systematical forecasting process to proceed with a multi-points forecasting with different methods. This kind of sale forecasting is less of stable quality and is harder to provide the managers a reasonable statistics explanation.
Under the CPRF process, both buyers and sellers are able to obtain the short-term and single sale forecasting by real time information communication. Furthermore, the follow-up order forecasting and replenishment strategy decision can be also established through this process. This research finds the variables that are more suitable to the mixed structure by usage of the algorithms, ANN and Evolution Strategy. And this research uses Evolution Strategy that is more suitable to real question to improve the mixed structure of Huang (2004). In the end, experimentation is adopted in order to verify the performance of the model. |
| Reference: | 中文參考文獻
[1] 林郁文，「以產品生命週期為基礎之多世代產品競爭主動式雙贏價模式」，東海大學工業工程與經營資訊研究所碩士論文，2003年6月。
[2] 陳建安，「整合類神經往路與遺傳演算法為輔之模糊類神經網路於智慧型訂單選取之應用」，國立台北科技大學生產系統工程與管理研究所碩士論文， 2000年6月。
[3] 蘇木村、張孝德，「機器學習類神經網路、模糊系統以及基因演算法則」，全華科技圖書股份有限公司出版，2003年2版。
[4] 姚銘忠、張倫、林晏妃、黃曉玲，「工具機業導入協同規劃與補貨模式之探討」，第一屆知識管理與與協同規劃研討會，2002年。
[5] 張炳螣、張晴翔、廖嘉偉，「協同預測應用於IC 半導體之整合模式」，第一屆知識管理與與協同規劃研討會，2002年。
[6] 廖嘉偉，「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年。
[7] 黃蘭禎，「CPFR流程下之短期預測模型」，政治大學資訊管理所碩士論文， 2004年。
[8] 葉怡成，「類神經網路模式應用與實作」，儒林圖書有限公司，2003年8版。
英文參考文獻
[1] Anderson, D. and Lee, H., White paper: The Internet-enabled supply chain: from the first click to the Last Mile, available at http://www.manufacturing.net/scm/contents/pdf/anderson_lee_wp.pdf, 1999.
[2] Arminger, G., Sales and Order Forecasts in the CPFR Process for Retail, Collaborative planning, forecasting, and replenishment –How to create a supply chain advantage, American Management Association, New York Publishing, pp. 53-68, 2002.
[3] Aviv, Y., The Effect of Collaborative Forecasting on Supply Chain Performance, Management Science, Vol. 47, No. 10, pp. 1326-1343, 2001.
[4] Aviv, Y., Gaining Benefits from Joint Forecasting and Replenishment Processes: the Case of Auto-Correlated Demand, Manufacturing & Service Operations Management, Vol. 4, No. 1, pp. 55-74, 2002.
[5] Arminger, G., Sales and order forecasts in the CPFR process for retail, inSeifert, D. (Ed.), 2003.
[6] Barry and Linoff, Data Mining Technologies, NY: Wisely, 1997.
[7] Chen, F., Drezner, Z., and Ryan, J. K., Simchi-Levi, D., Quantifying the Bullwhip Effect in a Simple Supply Chain: The Impact of Forecasting, Lead Time and Information, Management Science, Vol. 46, pp.436-443, 2000.
[8] Chuen-Lung Chen, David B. K. and Patrick G. D., A new approach to applying feedforward neural networks to the prediction of musculoskeletal disorder risk, Applied Ergonomics, Vol. 31 pp.269-282, 2004.
[9] Chuen-Lung Chen, David B. K. and Patrick G. D., Using Feedforward Neural Networks and Forward Selection of Input Variables for an Ergonomics Data Classification Problem, Human Factors and Ergonomics in Manufacturing, Vol. 14 pp.31-49, 2004.
[10] Ellram, L. M., Supply Chain Management - The Industrial Organization Perspective, International Journal of Physical Distribution & Logistics Management, Vol. 21, No. 1, 13-22, 1991.
[11] Goldberg, D. E., Generic Algorithms in Search, Optimization and Machine Learning, Addison-Wesley Publishing, 1989.
[12] Jeong, B. ,Jung, H. S. and Park, N. K., A computerized casual forecasting system using genetic algorithms in supply chain mgmt, the Journal of Systems and Software, Vol. 60, pp. 223-237, 2002.
[13] Johnson, James, Wood, Donald, Contemporary Logiatics 6th edition， Upper Saddle River, NJ: Prentice Hall, 1996.
[14] Herrera, M. L. and Verdegay, J. H., Fuzzy connectives based crossover operation to model genetic algorithms population diversity, Fuzzy Set and Systems, Vol. 92, pp. 21-30, 1997.
[15] Helms, M., Ettkin, L. P. and Chapman, S., Supply Chain Forecasting-Collaborative forecasting supports supply chain management, Business Process Management Journal, Vol. 6, Iss. 5, pp.392-394, 2000.
[16] Hoffmeister, F. Beack, T. and Schwefel, H.-P., A Survey of Evolution Strategies, Proceedings of the Fourth International Conference on Genetic Algorithms, R. Belew and L. B. Booker (Eds.), Morgan Kaufmann, San Mateo, pp. 2-9, 1991.
[17] Hoffmeister, F., Beack, T., Genetic algorithms and evolution strategies: similarities and differences, Tech. Report no. SYS-1/92, University of Dortmund, 1992.
[18] Holmstrom, J. , Framling, 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, pp. 136-145, 2002.
[19] Jain, L., Which Forecasting Model should We Use? The journal of business forecasting, Vol.19, No. 3, pp. 2, 28, 35, 2000.
[20] Jain, L., Benchmarking forecasting models, The Journal of Business Forecasting, Methods and System, Vol.21, No. 3 , pp.18-20,30, 2002.
[21] Kolter, P., Marketing management-Analysis, Planning, Implementation and Control, 9th Ed, Englewood Cliffs, N.J., Prentice-Hall Inc., 1991.
[22] Lambert, Douglas M. and Martha C. Cooper, Issues in Supply Chain Management, Industrial Marketing Management, Vol. 29, pp.65-83, 2000.
[23] Lapide, L., New developments in business forecasting: Debunking executive conventional wisdom, The journal of business forecasting, vol.19, No.2, pp.16-17, 2000.
[24] LeVee, G. S., The Key to Understanding the Forecasting Process, Journal of Business Forecasting, Vol.11, No.4, pp.12-16, 1992.
[25] Master, T., Practical Neural Network Recipes in C++, Academic Press Inc., San Diego, CA. 1993.
[26] Mills, T. C., Time Series Techniques for Economics, Cambridge University Press, United Kingdom, 1990.
[27] Mulhern, F. J., Williams, J. D. and Leone, R. P., Variability of Brand Price Elasticity across Retail Stores: Ethnic, Income, and Brand Determinants, Journal of Retailing, Vol.74, No. 3, pp. 427-446, 1998.
[28] Nolan, W. Jr., Game Plan for A Successful Collaboration Forecasting process, the Journal of Business Forecasting, Spring, pp.2-6, 2001.
[29] Ozturkmen, Z. A., Forecasting in the Rapid Changing Telecommunications Industry: AT&T`s Experience, The journal of business forecasting, Vol.19, No.3, pp.3-4, 2000.
[30] Shankar, V. and Krishnamurthi, L., Relating Price Sensitivity to Retail Promotional Variables and Pricing Policy: An Empirical Analysis, Journal of Retailing, Vol.72, No. 3, pp. 249-272, 1996.
[31] Seifert, D., Collaborative Planning, Forecasting and Replenishment, Preprint Edition, pp.39-52, 2002.
[32] Stank, T. P. and Keller, S. B., Supply Chain Collaboration and Logistical Service performance, Journal of Business Logistics, Vol. 22, No.1, pp.29-45, 2001.
[33] Shankar, Venkatesh and Lakshman Krishnamurthi, Relating Price Sensitivity to Retailer Promotional Variables and Priceing Policy: An Empiricial Analysis, Journal of Retailing, Vol. 72, No.3, pp. 249-272, 1996.
[34] Voss, G. B. and Seiders, K., Exploring the Effect of Retail Sector and Firm Characteristics on Retail Price Promotion Strategy, Journal of Retailing, Vol. 79, pp.37-52, 2003.
[35] Thomas Back, Frank Hoffmeister, and Hans-Paul Schwefel, A survey of evolution strategies, Proceedings of the 4th International Conference on Genetic Algorithms, pp. 2-9, July 1991. |
| Description: | 碩士
國立政治大學
資訊管理研究所
92356030
94 |
| Source URI: | http://thesis.lib.nccu.edu.tw/record/#G0923560301 |
| Data Type: | thesis |
| Appears in Collections: | [資訊管理學系] 學位論文
|
Files in This Item:
| File |
Size | Format | |
|---|
| index.html | 0Kb | HTML2 | 436 | View/Open |
|
All items in 政大典藏 are protected by copyright, with all rights reserved.
|
