|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。|
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.