企業的採購活動必須考慮產業及市場的因素，並以企業的策略目標作為指導原則，尋找最佳的供應商與產品或服務。多屬性決策分析技術是企業常用於選擇最佳方案的工具，尤其是SMART簡易多屬性評等技術是最早發展也最常被應用的量化分析工具。但是多屬性的決策分析工具皆是以傳統數學模型發展出來的量化分析工具，對於半結構或多元性的方案選擇，缺乏具有系統性且客觀的評估屬性建立方法，並且欠缺值化屬性的權重設定依據。 策略行銷分析4C理論的四項成本變數，可以作為SMART簡易多屬性評等技術的根本目標，並且可以各項成本變數的因子作為建立評估屬性的根據；4C理論同時也提供了影響各項成本的因素，可以作為權重設定的參考。 本研究嘗試結合策略行銷分析4C理論與SMART簡易多屬性評等技術，以建立4C-SMART企業採購決策分析模型。並且透過兩個簡單但實際的案例演練，以4C-SMART模型產生量化的數據結果，作為採購決策者的分析依據，來推導並且證實4C-SMART應用於企業採購實務的可能性。 / The factors of industry and market are usually taken into account in corporate purchasing activities. Purchasing tends to find the best suppliers as well as best products and services under the guidelines of corporate strategic objectives. Multiple Attribute Decision Analysis, MADA, is the most common tool to select the best solution among options. Simple Multi-Attribute Ranking Technique, SMART, is the earliest developed quantitative tool of MADA and has been widely adopted by corporate purchasing. However, MADA tools are constructed on the basis of traditional mathematic models. When coping with semi-structural and diversified options, the MADA tools lack of systematic and objective methods of constructing attributes and do not provide criteria for setting weights against qualitative attributes. The four cost variables in Strategic Marketing Analysis, also known as 4C theory, can be used for setting the fundamental objectives of SMART model. 4C theory also provides factors under the four cost variables as criteria for setting attributes. The factors which influence the variation of the four costs can be adopted as criteria for setting weights. This research tried to integrate 4C theory and SMART technique to create a 4C-SMART purchasing analysis model. Through the practices of two simple but practical cases, the model helped to generate quantitative results as evidences for purchasing decision maker’s analysis and deduced the possibility of applying the model in practical corporate purchasing.

關於供應商評選模式的探討與研究，是現今於供應鏈管理中非常重要的議題，而對於國內外供應商評選的相關性研究中，往往只考量供應商本身績效，而缺乏整體性的考量，有鑑於此，當企業在評選供應商時，不僅是針對單一供應商績效的考量，而對於供應商本身上下游廠商的整體績效的考量，也是相當重要的評選因素。本研究運用資料包絡分析法(Data Envelopment Analysis, DEA)與簡易多屬性評等技術(Simple Multi-Attribute Rating, SMART)，以建構較完整的供應商評選模式，協助企業的決策者選擇最適的供應商。 在進行供應商與供應商所屬的供應鏈之績效評估時，利用資料包絡分析法中的交叉模式計算供應商與供應商所屬的供應鏈之平均效率，再藉由平均效率值以排序供應商與供應商所屬的供應鏈之優先順位，但因兩者之順序會產生不一致的情形，利用SMART法中的排序加總法計算兩者之權重值，最後以加總法計算綜合效率值，依據綜合效率值加以排序以評選最適的供應商，而此兩種方法的結合， 則需透過驗證的方式以確定運用之正確性，利用系統動力學(System Dynamics, SD)以建構供應鏈之整體廠商的經營環境，模擬供應鏈整體廠商之績效評估模式，以產生模擬驗證排序，最後再將綜合排序與驗證排序，利用Spearman等級相關係數驗證其相關性，而最終證明兩排序具有高度相關性，也證實本研究所提出的供應商評選模式富有參考價值。 / The research of supplier selection model is a very important issue in supply chain management (SCM). Even though the research on supplier selection is abundant, the most works usually only consider the performance of the supplier. This situation can result in a lack of integral deliberation. Therefore, the decision-maker wants to select his vendor in the company, it is important that the decision-maker not only focus on the business unit but also add to the overall organization of the supply chain. In this paper the Data Envelopment Analysis (DEA) and the Simple Multi-Attribute Rating (SMART) are developed to construct the effective supplier evaluation model depends upon the constituent parts of the supply chain, which may be helpful for selecting the appropriate vendor. When evaluating the performance of the supplier and the supplier’s supply chain, the cross-efficiency model of DEA is applied to compute average efficiency both the supplier and the supply chain. According to average efficiency, we can arrange the priority order that may be have the inconsistent order for the suppliers and their supply chains. The rank sum weighting of SMART is employed to determine the weights of suppliers and their chains and then the weighted method is used to calculate overall efficiency that ranking of the suppliers is obtained. Above of two methods, we must be verify the model. The System Dynamics (SD) is designed to implement the whole components of the supply chain for business environment and simulate the performance model. Based on this performance model, we can acquire a confirmation list. In the end, the Spearman’s rank correlation coefficient is provided to testify the correlation between the overall order and the confirmation list. The result of the statistic analysis is illustrated the strength of the association. The model can be tailored and applied by firms that are making decisions on supplier selection.