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。

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.

中文摘要 	i
英文摘要 	ii
誌謝 	iv
目錄 	vi
表目錄 	viii
圖目錄 	ix
符號說明 	xi

第一章 緒論 	1
1.1 研究背景與動機 	1
1.2 研究目的 	3
1.3 研究範圍 	4
1.4 研究流程與架構 	5
第二章 文獻回顧 	8
2.1 供應鏈協同管理 	8
2.2 協同運輸管理 	9
2.3 供應鏈預測模式 	14
2.4 供應鏈資訊分享 	19
2.5 灰色預測 	22
2.6 綜合評析 	25
第三章 協同運輸管理之出貨預測與貨運需求預測模式 	27
3.1 灰色系統與協同供應鏈管理協同程度 	29
3.2 灰色預測理論、灰數與灰色統計 	32
3.2.1 灰數與灰色統計 	32
3.3 出貨預測模式 	35
3.3.1 出貨灰色數列預測模式 	35
3.3.2 馬爾可夫鏈殘差修正模式與灰色包絡模式 	37
3.3.3 滾動灰色預測模式	39
3.3.4 出貨灰色多元系統預測模式 	42
3.4 貨運需求預測模式 	49
3.4.1 貨運需求灰色數列預測模式 	49
3.4.2 貨運需求加總模式 	51
3.5 灰色異常值預測模式 	53
3.6 小結 	57
第四章 個案分析 	58
4.1 出貨預測之個案分析 	58
4.1.1 出貨GM(1,1)數列預測	60
4.1.2 協同運輸管理架構之出貨GM(1,1)數列預測	68
4.1.3 出貨GM(1,N)灰色多元系統預測	72
4.1.4 協同運輸管理架構之出貨GM(1,3)多元系統預測 	79
4.2 貨運需求預測個案分析 	90
4.2.1 貨運需求GM(1,1)數列預測	90
4.2.2 貨運需求加總模式 	97
4.3 出貨異常值預測個案分析 	100
第五章 結論與建議 	104
5.1 結論 	104
5.1.1 出貨預測 	104
5.1.2 貨運需求預測 	105
5.1.3 出貨異常值預測 	106
5.2 建議 	107
參考文獻 	108
表目錄
表2.1 供應鏈協同與協同運輸管理文獻回顧整理 	13
表2.2 供應鏈預測文獻回顧整理 	18
表2.3 供應鏈資訊分享文獻回顧整理 	21
表2.4 灰預測文獻回顧整理 	24
表3.1 精度檢驗綜合評定等級表 	37
表4.1  IC製造出貨預測值與誤差(k=12) 	62
表4.2 出貨數列預測精度檢驗綜合評定等級表 	62
表4.3  滾動GM(1,1)、ARIMA與多元線性迴歸趨勢預測比較 	64
表4.4  IC製造出貨預測值與包絡曲線 	65
表4.5 灰色多元系統解釋變數之關聯度比較 	72
表4.6  IC製造出貨量、晶圓代工出貨量與IC相關產品銷售總量實際值 	74
表4.7  IC製造出貨多元系統預測值與誤差(k=12) 	75
表4.8 各多元系統預測模式預測結果 	75
表4.9 類神經網路訓練模式相關參數設定表 	77
表4.10  GM(1,3)、多元線性迴歸與類神經網路預測結果比較 	77
表4.11 解釋變數實際值與上下包絡值 	80
表4.12  IC相關產品銷售總量與晶圓出貨量預測值與上下包絡值 	80
表4.13  IC製造出貨量實際值、預測值與上下包絡值 	85
表4.14 不同資訊共享程度下IC製造出貨預測上下包絡值 	88
表4.15  T貨運公司貨運需求預測值與誤差(k=9) 	92
表4.16 貨運需求預測精度檢驗綜合評定等級表 	92
表4.17 貨運需求上下包絡值 	93
表4.18  Improved GM(1,1)、ARIMA與線性迴歸趨勢預測結果比較 	94
表4.19 貨運需求不同產業出貨量預測 	99
 
圖目錄
圖1.1 研究流程圖 	6
圖1.2 研究架構圖 	7
圖2.1 CTM業務流程整合架構 	10
圖2.2 (續) CTM業務流程整合架構 	11
圖2.3 混合預測模型架構圖 	14
圖2.4 本研究於供應鏈架構中之定位 	26
圖3.1 協同運輸管理預測模式流程圖 	28
圖3.2 協同程度與CTM價值提升關係圖 	30
圖3.3 白化權函數三種型態 	33
圖3.4 出貨滾動灰色預測模式構建程序 	41
圖3.5 出貨白化權函數 	46
圖3.6 出貨預測模式流程架構圖	48
圖3.7 貨運需求滾動灰色預測模式構建程序	50
圖3.8 貨運需求加總模式建構程序	51
圖3.9 貨運需求預測模式流程架構圖 	52
圖3.10 出貨異常值預測模式流程架構圖	56
圖4.1  IC製造產業供應鏈架構圖 	59
圖4.2  Improved GM(1,1)、線性迴歸趨勢預測與ARIMA模式預測比較 	66
圖4.3  Improved GM(1,1)預測值與上下包絡曲線 	67
圖4.4 協同資訊共享概念圖 	69
圖4.5(a) 影響出貨量事件之白化權函數 	70
圖4.5(b) 影響出貨量事件之白化權函數 	70
圖4.6  GM(1,3)與多元線性迴歸、類神經網路預測結果比較 	78
圖4.7 晶圓出貨實際值與上下包絡曲線 	81
圖4.8  IC相關產業產品銷售總量實際值與上下包絡曲線 	81
圖4.9 晶圓出貨量白化權函數 	83
圖4.10  IC相關產品銷售總量白化權函數 	83
圖4.11  IC製造出貨預測與上下包絡預測比較 	86
圖4.12 不同資訊共享程度下IC製造出貨預測值與上下包絡預測值 	89
圖4.13 貨運需求Improved GM(1,1)預測值與上下包絡曲線 	95
圖4.14  Improved GM(1,1)、ARIMA與線性迴歸趨勢預測結果比較 	96
圖4.15 貨運需求加總情境假設 	98
圖4.16  IC製造出貨歷史資料與上下限門檻值 	101
圖4.17 上異常值預測時點分布 	103
圖4.18 下異常值預測時點分布 	103

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