供應鏈協同運輸管理之出貨預測與貨運需求預測模式研究

Shipment Forecasting and Freight Demand Forecasting Models for Collaborative Transportation Management in Supply Chain

李書賢(Shu-Hsien Li)

指導教授 ： 溫裕弘

淡江大學/管理學院/運輸管理學系碩士班/碩士(2008年)

https://doi.org/10.6846/TKU.2008.00451

摘要

因應全球市場環境的競爭壓力，為避免長鞭效應所造成的供應鏈成本浪費，企業開始重視所謂供應鏈協同。供應鏈協同目前最受矚目的是由VICS所發展之「協同規劃、預測與補貨系統(Collaborative Planning Forecasting Replenishment, CPFR」，並延伸物流運輸環節提出「協同運輸管理(Collaborative Transportation Management, CTM)」。CTM旨在解決供應鏈運輸程序無效率為目的，在CTM架構下，出貨預測與貨運需求預測係整體業務流程架構之關鍵核心基礎，物流運送業者推估未來出貨量與貨運需求動態波動與發展態勢，進行運輸網路規劃、路線排程、車隊規劃等涵蓋戰略規劃與作業規劃之基礎。然而過去相關文獻尚未從預測模式與數學理論模式探討協同運輸管理，故發展一套協同運輸管理架構下之出貨預測與貨運需求預測模式以提供供應鏈實務上之應用實為一項重要課題。
本研究整合一系列灰色預測模式，包括灰色數列預測、灰色多元系統預測與灰色異常值預測，發展一系列CTM架構下之出貨預測與貨運需求預測模式。本研究在出貨預測模式上，因應不同供應鏈協同機制，分為數列預測與多元系統預測，並將灰數(Grey Number)的概念引入預測模式，分析協同運輸管理架構之不同程度資訊共享下，物流運送業者進行出貨預測之理論模式基礎。貨運需求預測分別建構數列預測與貨運需求加總模式，貨運需求加總模式係以出貨預測為基礎，分別建立各廠商出貨預測模式，並將所得之預測結果進行加總，計算物流運送業者未來總體貨運需求。進一步本研究因應協同運輸管理異常處理機制，以灰色異常值預測為基礎，發展出貨異常時點預測模式，以預測未來異常可能發生時點，提供物流運送業者提前掌握異常時點之決策基礎。藉由實證個案分析，本研究所建構之出貨預測與貨運需求模式預測能力較多元迴歸模式、時間序列模式與類神經網路模式佳；而協同情境分析在資訊共享程度越高下，物流運送業者對於未來出貨量幅值範圍掌握能力越佳，引領出協同運輸管理之重要性。而異常值發生時點預測上，本研究所建構之預測模式能有效掌握未來可能發生異常時點。
本研究成果不僅在學術上為供應鏈協同運輸管理之出貨預測與貨運需求預測模式相關研究之參考，所發展之模式亦可提供CTM系統預測模組開發之模式基礎。

關鍵字

供應鏈協同；協同運輸管理；出貨預測；貨運需求預測；灰色預測模式

並列摘要

Under the keenly competitive environment and avoid to waste cost by bullwhip effect, the enterprises beginning to join the supply chain collaboration. The recent collaborative initiative, termed Collaborative Planning, Forecasting, and Replenishment (CPFR®　　), has begun to gain wide acclaim for the benefits it delivers. The new evolution of CPFR is to extend the core elements to include the transportation component, termed Collaborative Transportation Management (CTM). CTM is a holistic process that improve the operating performance of all parties involved in the relationship by eliminating inefficiencies in the transportation component of the supply chain through collaboration. CTM shipment forecasting and freight demand forecasting are critical foundation in the CTM business process, that are prerequisite to carriers’ tactical and operational planning, such as network planning, routing, scheduling, and fleet planning and assignment. However, few literatures have been paid to the forecasting modeling for CTM. This study attempts to develop a series of forecasting models for shipment and freight demand forecasting under the CTM framework.
This study extends and improves grey forecasting theory and constructs hybrid models to develop a series of shipment forecasting and freight demand forecasting models for CTM. In shipment forecasting, consider different collaborative frameworks, both grey systematic forecasting and grey time-series forecasting are developed. This study first attempts to integrate the grey number in forecasting models, in order to analyze shipment forecasting under partical information sharing in CTM framework. Furthermore, an aggregated freight demand forecasting model was also developed. This study then use grey calamity forecasting model to predicting the shipment exceptions. A case study with an IC (Integrated Circuit) supply chain and other relevant data was provided to illustrate the results. These models are shown to be more accurate prediction results than multiple regression, ARIMA and neural network models, as well as shipment exception forecasting. Finally, the results indicate that the more information sharing under CTM, the carriers can predict more accurately.
This study demonstrates how the proposed forecasting models might be applied to the CTM system and provides as the model theoretical basis for the forecasting module developed for the CTM.

並列關鍵字

Supply Chain Collaboration；Collaborative Transportation Management；Shipment Forecasting；Freight Demand Forecasting；Grey Forecasting Models

參考文獻

6. 吳綺芳 (2006)，「整合類神經網路與灰色理論之預測模型設計」，元智大學工業工程與管理學系碩士論文，民國95年。

13. 黃蘭禎 (2004)，「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，民國93年。

53. Zhang, C., Tan, G. W., Robb, D. J., and Zheng, X., “Sharing Shipment Quantity Information in the Supply Chain,” Omega-The International Journal of Management Science, Vol. 34, pp.427-438, 2006.

21. Browning, B. and White, A., “Collaborative Transportation Management,” Logility, Inc. White paper, (2000). http://www.vics.org

16. Akay, D. and Atak, M., “Grey Prediction with Rolling Mechanism for Electricity Demand Forecasting of Turkey,” Energy, Vol. 32, pp.1670-1675, 2007.