供應商評估和選擇是需要考慮各種準則的關鍵決策過程。許多研究通常利用線性加權法，數學規劃模型，統計方法和人工智慧為基礎的技術的許多技術，來達到有效的供應商評估與選擇。
本研究主要是提出了一種新的混合模型，藉由整合模糊德菲爾法、模糊決策實驗室分析法、模糊網路分析法、資料包絡分析法和神經網路來評估供應商的績效。首先，模糊德菲爾法和模糊實驗設計法被用來定義和構建網路分析法的網路架構。接著，利用模糊網路分析法來考慮準則之間的相互重要性，以做為資料包絡分析法的準則權重限制範圍，然後使用資料包絡分析法得到供應商的效率值。最後，企業績效相關的數據被用來訓練神經網絡模型，並且用於預測新的供應商績效表現。研究結果顯示，本研究所提出的方法在供應商數目減少的情況下，仍能對供應商進行排序，突破舊有資料包絡分析法對決策單位個數的限制並考量決策者的偏好，評選出最佳供應商，使其適用於供應商數目較少之中小型企業或只有少數可供選擇之供應商的情況。

Supplier evaluation and selection are critical decision making processes that require consideration of a variety of attributes. Numerous studies have been presented for effective evaluation and selection of suppliers by utilizing many techniques such as linear weighting methods, mathematical programming models, statistical methods and artificial intelligence-based techniques.
This study presents a novel hybrid model which integrates fuzzy Delphi, fuzzy decision making trial and evaluation laboratory (DEMATEL), fuzzy analytical network process (ANP), data envelopment analysis (DEA) and neural network to assess supplier performance. First, fuzzy Delphi and fuzzy DEMATEL are used to define and construct fuzzy ANP structure which is employed to determine corresponding weight for each criterion. These weights are further used by DEA to overcome that the users cannot set up criteria weight preferences for DEA. Then, DEA is applied to classify the existing suppliers based on the resulting efficiency scores. Finally, firm performance-related data is utilized to train neural network model and we can apply the trained model to evaluate new suppliers. The experiment result indicates that even though potential suppliers are not many, the proposed model still can rank the potential suppliers effectively. The model solves DEA’s limit of the decision making unit (DMU) number and considers decision makers preferences. It yields a favorable prediction accuracy rate of efficiency score to rank the potential suppliers. This proposed model is more feasible for the small and medium enterprise which often has few suppliers.

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