Assessing the Efficiency of a Supply Chain Network using the DEA

• Main Authors: Tzi-Yuan Chou, 周子淵
• Other Authors: Ching-Chin Chern
• Format: Others
• Language: en_US
• Published: 2012
• Online Access: http://ndltd.ncl.edu.tw/handle/82922609389569810230

碩士 === 國立臺灣大學 === 資訊管理學研究所 === 100 === Recently, due to globalization and the highly competitive and fast-changing environment, the survivability of an organization relies on not only the ability to use its resources effectively and efficiently, but the ability to cooperate with the its supply chain partners to optimize the overall profits. In order to assess the performance of an organization, the performance measurement system must be generic enough to provide an integrated view on important factors from different aspects. However, traditional means of performance measurement are either based on a small set of predefined factors, which prevent a complete view of the overall system, or limited to a single organization, which ignore the interaction between supply chain players. Data envelopment analysis (DEA) has become a popular method to evaluate the performance in terms of efficiency, and has been applied to production systems in various fields. Network DEA improves the traditional DEA models by adding constraints to maintain the internal structure and operations of the subject being evaluated. One of the main benefits of using DEA is the model accepts multiple positive quantitative data as inputs and outputs of the system, making evaluating performance using factors with unknown relationship possible. In this study, a modified network DEA model is proposed, which has the ability to (1) evaluate the efficiency of a supply chain as a whole; (2) consider the buyer-seller structure as well as multiple time periods when evaluating efficiency; (3) handle “undesirable outputs,” which cannot be used in traditional DEA models directly. The modified network DEA model may accept any quantitative factors as inputs and outputs depending on the decision makers’ needs. To prove the effectiveness of the model, a scenarios test and a real-world data test are conducted.