供應商議價過程與出價預測模式之研究｜國立臺灣科技大學博碩士論文系統

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研究生：	李俊青 Chun-Ching Lee
論文名稱：	供應商議價過程與出價預測模式之研究 The study of supplier negotiation process and bid prices forecasting
指導教授：	歐陽超 Chao Ou-Yang
口試委員:	周雍強 none 陳裕民 none 鄭元杰 none 葉瑞徽 none 王福琨 none 林義貴 none
學位類別：	博士 Doctor
系所名稱：	管理學院 - 工業管理系 Department of Industrial Management
論文出版年：	2008
畢業學年度：	96
語文別：	英文
論文頁數：	77
中文關鍵詞：	供應鏈管理、供應商議價過程、交互式出價策略、人工類神經網路、競標價格預測
外文關鍵詞：	Supply chain management, Supplier negotiation process, Interactive bidding strategy, Artificial neural networks, Bid prices forecast
相關次數：	點閱：486 下載：10
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在現今全球化市場競爭日益激烈之際，如何以有效率與效益的方法管理供應鏈是企業所要面對的嚴峻挑戰，供應鏈管理在企業管理文獻中已成為一個引發研究興趣的主題。在供應鏈管理中，供應商選擇的策略是一個相當緊要的議題，選擇適當的供應商對於公司的競爭力有著重要的影響。大多數的供應商選擇是以競標與議價的機制為基礎。一般而言，與供應商的議價策略僅憑管理者的經驗。因議價對方智識背景的多樣性、供需關係隱含了許多的未知變數、複雜的交互作用以及議價知識的不足，使得供應商議價成為一個複雜且具挑戰性的工作。要把議價的工作做好，有必要發展一個智慧型系統作為在供應商議價過程中的支援。
本研究關注於供應商議價過程中，某一需求者與其眾多供應者之交互式出價策略的發展，並以實驗的方法觀察需求者與供應商議價雙方在不同的訂單條件與出價策略交互影響下所得到的議價結果與效率。再者本研究發展了一個應用在供應商議價過程中預測供應商競標價格的人工類神經網路預測模式，藉由此模式，需求者可以事先洞悉其目前可選擇的競標價格與供應商下次出價的關係。另外，本模式亦可用以預測在某種議價條件的環境下，議價成功完成交易的可能性。
本論文有兩個研究目標，第一為調查在不同的訂單條件下，需求者採取不同的出價策略所獲致的議價效率以及提供需求者在每一種訂單條件下，適當的出價策略；第二為運用人工類神經網路預測模式的預測能力在需求者決定目前較佳的競標價格時，提供議價支援與建議以減少無謂的協商交涉次數、降低採購成本、改善議價效率或縮短供應商議價的前置時間。

Supplier selection strategy is a critical issue in a supply chain management (SCM) system. Selecting the proper suppliers can have a significant impact on the competitiveness of firms. Most supplier selection processes are based on bidding and negotiation mechanism. In general, the supplier negotiation decision depends on the experience of managers. Supplier negotiation decision is a sophisticated and challenged job due to the diversity of intellectual backgrounds of the negotiating parties, the many variables involved in supply-demand relationship, the complex interactions and the inadequate negotiation knowledge of project participants. To do the job well, it is necessary to develop an intelligent system for supports in supplier negotiation process (SNP).
This research is concerned with the development of the interactive bidding strategies for a demander and its multiple suppliers in SNP and observes the negotiation results and efficiency of interactions of both-side bidding strategies in different order conditions. Furthermore, an artificial neural network-based (ANN) predictive model with application for forecasting the supplier’s bid prices in SNP is developed. By means of the model, demander can foresee the relationship between its alternative bids and corresponding supplier’s next bid prices in advance. In addition, the model can also predict the possibility of a successful deal under a given negotiation environment.
There two objectives of this research. The first is to investigate the negotiation efficiency for varying bidding strategies the demander employed in different order conditions and provide the proper bidding strategy in each order condition to demander. The second is applying the ANN predictive model’s forecast ability to provide negotiation supports or recommendations for demander in deciding the better current bid price to decrease meaningless negotiation iterations, reduce procurement cost, improve negotiation efficiency or shorten supplier negotiation lead time.

摘要……………………………………………………………………….…………..I
Abstract………………………………………...………………………...………….II
誌謝…………………………………………………………………………………III
Table of Contents…………………………………………………………………..IV
List of Tables……………………………………………………………………….VI
List of Figures………………………………………………………...…………...VII
1. Introduction…………………...…………………………………………………..1
1.1 Background………………………………….…………………………………1
1.2 Research motivation and objectives……………………………………………3
1.3 Research scope and structure…………………………………………………..4
2. Literature review………………………………………………………………….7
2.1 Supplier selection………………………………………………………………7
2.2 Negotiation……………………………………………………………………..8
2.3 Bid calculation………………………………………………………………..10
2.4 Artificial neural networks…………………………………………………….10
2.5 Literature restatement and comments………………………………………...12
3. Design of the bidding strategy and experiments.………………………….......18
3.1 Supplier negotiation process framework……………………………………..18
3.2 The bidding strategy for a demander…………………………………………21
3.3 The bidding strategy for suppliers Experiments……………………………...23
3.4 Experiments…………………………………………………………………..27
3.4.1 The settings and descriptions of parameters about demander…………..28
3.4.2 The settings and descriptions of parameters about each supplier……….28
3.4.3 The cost structure of each supplier……………………………………...31
3.4.4 The procedures of the experiments……………………………………...33
4. Evaluation of the bidding strategy……………………………………………..35
4.1 Evaluation…………………………………………………………………….35
4.2 The comparisons of negotiation efficiency…………………………………...42
4.2.1 The conditions and results of the comparisons………………………….42
4.2.2 The comment on the comparisons………………………………………46
5. Artificial neural network-based predictive model…………………………….48
5.1 Methods of bid prices forecasting…………………………………………….48
5.2 Choice of the input factors………………………………………………........49
5.3 Available input information to the ANN model………………………………50
5.4 Architecture of the ANN model………………………………………………51
5.5 Training and test procedures………………………………………………….52
5.5.1 Training and test data……………………………………………………52
5.5.2 Data normalization………………………………………………….......54
5.5.3 Training algorithm………………………………………………………54
5.5.4 Performance measures…………………………………………………..56
5.6 Results…………………………………………………………………….......57
5.6.1 Training performance……………………………………………….......57
5.6.2 Test performance………………………………………………………..58
5.7 Model applications………………………………………………………………59
5.7.1 Scenario 1……………………………………………………………………..60
5.7.2 Scenario 2……………………………………………………………………..60
6. Conclusions………………………………………………………………………63
References…………………………………………………………………………..65
Appendices………………………….…………………..…70
作者簡介…………………………………………………………………………….76
List of Tables
Table 2.1 The summary of relevant literature……………………………………….15
Table 4.1 The summary of the adaptive bidding strategy in varying order conditions…………………………………………………………………44
Table 4.2 The negotiation efficiency comparisons between both models…………..47
Table 5.1 The available information input to the ANN model at varying iterations...51
Table 5.2 The settings and the number of patterns for training and test data……….55
Table 5.3 The bid prices of both-side parties in a given negotiation process……….62
Table 5.4 The predicted supplier bid prices of whole negotiation process………….62
Table 5.5 The actual supplier bid prices of whole negotiation process……………..62
Table A.1 The data of experimental results…………………………………….........70
List of Figures
Figure 1.1 Research structure chart…………………………………………………..6
Figure 3.1 Supplier selection negotiation process framework………………………19
Figure 3.2 The bid prices curves of demander………………………………………22
Figure 3.3 The bid prices curves of supplier………………………………………...24
Figure 3.4 The interactions of  and   in a given condition……………..27
Figure 3.5 The illustration of supplier’s normal production quantity……………….30
Figure 3.6 The unit cost curve of supplier s…………………………………………33
Figure 4.1 The relationships of  and  in 9 different order conditions………36
Figure 4.2 The relationships of  and  in 9 different order conditions……...36
Figure 4.3 The relationships of  and  in 9 different order conditions…………37
Figure 4.4 The relationships of  and  in 9 different order conditions………...38
Figure 4.5 The relationships of dd and  in 3 different required quantity conditions..................................................................................................39
Figure 4.6 The relationships of dd and  in 3 different required quantity conditions………………………………………………………………..39
Figure 4.7 The relationships of  and  in 3 different due date conditions..……..40
Figure 4.8 The relationships of  and  in 3 different due date conditions..……41
Figure 5.1 Artificial neural network-based predictive concept model………………50
Figure 5.2 The ANN predictive model architecture…………………………………52
Figure 5.3 The RMSE learning curve……………………………………………….57
Figure 5.4 The desired and predicted output plots…………………………………..59
Figure 5.5 The percentage error plot of test data……………………………………59

                                

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