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    Please use this identifier to cite or link to this item: http://tkuir.lib.tku.edu.tw:8080/dspace/handle/987654321/58514

    Title: Shipment forecasting for supply chain collaborative transportation management using grey models with grey numbers
    Authors: Wen, Yuh-Horng
    Contributors: 淡江大學運輸管理學系
    Keywords: Supply chain collaboration;Collaborative transportation management;Shipment forecasting;Grey models;Grey numbers;供應鏈協同;協同運同管理;出貨預測;灰色模式
    Date: 2011-08
    Issue Date: 2011-10-01 12:07:49 (UTC+8)
    Publisher: Abingdon: Taylor & Francis Ltd.
    Abstract: The sharing of forecasts is vital to supply chain collaborative transportation management (CTM). Shipment forecasting is fundamental to CTM, and is essential to carrier tactical and operational planning processes such as network planning, routing, scheduling, and fleet planning and assignment. By applying and extending grey forecasting theory, this paper develops a series of shipment forecasting models for supply chain CTM. Grey time-series forecasting and grey systematic forecasting models are developed for shipment forecasting under different collaborative frameworks. This paper also integrates grey numbers with grey models for analyzing shipment forecasting under partial information sharing in CTM frameworks. An example of an integrated circuit (IC) supply chain and relevant data are provided. The proposed models yield more accurate prediction results than regression, autoregressive integrated moving average (ARIMA), and neural network models. Finally, numerical results indicate that as the degree of information sharing increases under CTM, carrier prediction accuracy increases. This paper demonstrates how the proposed forecasting models can be applied to the CTM system and provides the theoretical basis for the forecasting module developed for supply chain CTM.
    為因應企業供應鏈激烈的競爭環境,如何強化供應鏈協同合作關係,以降低供應鏈成本、提升整體供應鏈效率,已成為供應鏈競爭致勝的關鍵課題。目前供應鏈「協同規劃、預測與補貨系統(Collaborative Planning Forecasting Replenishment, CPFR)」的新發展為延伸整合供應鏈運輸環節的「協同運輸管理(Collaborative Transportation Management, CTM)」。協同運輸管理旨在於解決供應鏈運輸程序之無效率,在CTM架構下,出貨預測為延伸CPFR銷售與訂單預測而預估未來出貨貨運量,而共享出貨預測資訊係為CTM之核心關鍵。出貨預測為CTM之基礎,為物流運送業者預測貨主未來出貨量、發展趨勢與波動,作為其運輸網路規劃、路線排程、車隊規劃之基礎。本研究因應不同供應鏈協同關係機制,建構數列預測與多元系統預測模式;數列預測主要建構在物流運送人與貨主廠商之CTM協同機制下,利用歷史出貨量與協同資訊共享預測出貨量;多源系統預測則整合供應鏈上下游廠商之協同架構,考慮貨主廠商上下游供應鏈活動特性與協同資訊共享,物流運送業者進行協同出貨預測。本研究並整合灰數(grey number)於灰色出貨預測中,分析協同運輸管理架構中不同程度資訊共享下,物流運送業者進行協同出貨預測與出貨量波動範圍之掌握。本研究以一IC供應鏈為實證數值範例,進行數值分析,驗證本研究建構之出貨預測模式預測能力較多元回歸模式、ARIMA時間數列模式與類神經網路模式佳;而分析結果亦顯示在協同資訊共享程度越超下,物流運送業者對於未來出貨量幅值範圍掌握能力越佳。本研究不僅驗證所發展之預測模式如何應用於供應鏈協同運輸管理之出貨預測上,在學術上可做為供應鏈協同運輸管理之出貨預測研究之參考,所發展之模式亦可提供相關供應鏈協同運輸管理系統預測模組開發之模式理論基礎。
    Relation: Transportation Planning and Technology 34(6), pp.605-624
    DOI: 10.1080/03081060.2011.600089
    Appears in Collections:[Graduate Institute & Department of Transportation Management] Journal Article

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