近年來受疫情、烏俄戰爭及中美貿易戰等事件造成供應鏈大亂，管理供應鏈議題開始受到各間公司注意，此次研究個案M公司對於自身產品皆外包給外包廠生產，目前M公司管理外包廠的面相僅有達交率、良率等單項KPI，缺乏多個項目且客觀呈現外包廠間效率值比較。
本研究預計採用資料包絡分析法(Data Envelopment Analysis，DEA)作為研究方法，此研究方法可以採納多個投入與產出數值，進行決策單元 (Decision Making Unit，DMU)間客觀計算效率值；決策單元為M公司四家外包廠搭配產品製作難易度共12項，投入項採用成本投入：代工價格、開線費總價、工單價格、外包廠人數、外包廠SMT產能等五項作為初始投入項目，產出項採用效益產生：工單售價、工單產出數量、製程嚴謹度評分等作為初始產出項目，進而得出外包廠搭配產品的效率值。
研究數據基於未來長期觀測，將以月為週期性的計算，以2022年10月的數據計算結果發現無論配合年限長短，四家外包廠皆有改善點，但細究有三家離有效率值1相差為0.1以內，預計此評估效率方法正式於M公司使用後，會以長時間監測效率值，若效率值長時間不滿1，則M公司會啟動外包廠改善專案。

This study evaluates the production efficiency of Company M's outsourced factories using the Data Envelopment Analysis (DEA) method. The research addresses the inefficiency in evaluating outsourced factories solely through individual KPIs like delivery and yield rates, lacking a comprehensive efficiency assessment. DEA is employed to integrate multiple inputs (e.g., cost, manpower, and SMT capacity) and outputs (e.g., order value and quantity) to calculate the efficiency of four outsourced factories. The study identifies areas of improvement in all factories, with three of them showing efficiency values close to the ideal value of 1. The research emphasizes long-term efficiency monitoring and suggests initiating improvement projects for factories that consistently score below 1. This approach aims to provide a more objective and detailed evaluation framework for managing outsourced factories, which can guide future investments in new outsourcing partnerships.

Amado, C. A. F., Santos, S. P., & Marques, P. M. (2012). Integrating the Data Envelopment Analysis and the Balanced Scorecard approaches for enhanced performance assessment. Omega, 40(3), 390-403.
Banker, Charnes, & Cooper. (1984). Some Models for Estimating Technical and Scale Inefficiencies in Data Envelopment Analysis. Management Science.
Barbarosoglu, G., & Yazgac, T. (1997). An application of the analytic hierarchy process to the supplier selection problem. Production and inventory managemen, 14-21.
Charnes, Cooper, & Rhodes. (1978). Measuring the efficiency of decision making units.
Emrouznejad, A., Parker, B. R., & Tavares, G. (2008). Evaluation of research in efficiency and productivity: A survey and analysis of the first 30 years of scholarly literature in DEA. Socio-Economic Planning Sciences, 42(3), 151-157.
Farrell, M. J. (1957). The Measurement of Productive Efficiency. Journal of the Royal Statistical Society.
Golany, B., & Roll, Y. (1988). An Application Procedure for DEA.
Hahn, G. J., Brandenburg, M., & Becker, J. (2021). Valuing supply chain performance within and across manufacturing industries: A DEA-based approach. International Journal of Production Economics, 240.
Harland, C., Brenchley, R., & Walker, H. (2003). Risk in supply networks. Journal of Purchasing and Supply Management, 9(2), 51-62.
Kolker, A. (2016). Data Envelopment Analysis:Comparing Performace of Different Unit with Multiple Inputs and Outputs. University of Wisconsin-Milwaukee.
Liu, J. S., Lu, L. Y. Y., Lu, W.-M., & Lin, B. J. Y. (2013). A survey of DEA applications. Omega, 41(5), 893-902.
Mirhedayatian, S. M., Azadi, M., & Farzipoor Saen, R. (2014). A novel network data envelopment analysis model for evaluating green supply chain management. International Journal of Production Economics, 147, 544-554.
Olson, D. L., Xie, C., & Wu, D. (2011). Risk management models for supply chain: a scenario analysis of outsourcing to China. Supply Chain Management: An 47
International Journal, 16(6), 401-408.
Rajesh, R., & Ravi, V. (2015). Supplier selection in resilient supply chains: a grey relational analysis approach. Journal of Cleaner Production, 86, 343-359.
Rashidi, K., Shabani, A., & Farzipoor Saen, R. (2015). Using data envelopment analysis for estimating energy saving and undesirable output abatement: a case study in the Organization for Economic Co-Operation and Development (OECD) countries. Journal of Cleaner Production, 105, 241-252.
Sadeghieh, A., Dehghanbaghi, M., Dabbaghi, A., & Barak, S. (2012). A genetic algorithm based grey goal programming (G3) approach for parts supplier evaluation and selection. International Journal of Production Research, 50(16), 4612-4630.
Tone, K., & Tsutsui, M. (2009). Network DEA: A slacks-based measure approach. European Journal of Operational Research, 197(1), 243-252.
Vargas, S., & Bricker, D. (2000). Combining DEA and factor analysis to improve evaluation of academic departments given uncertainty about the output constructs.
Weber, C. A., Current, J. R., & Benton, W. C. (1991). Vendor selection criteria and methods. European Journal of Operational Research, 2-18.
Zhu, J. (2020). DEA under big data: data enabled analytics and network data envelopment analysis. Annals of Operations Research, 309(2), 761-783.
吳有龍, & 楊倍箕. (2008). 以資料包絡分析法評估中國石油公司煉製單位經營績效之研究. 經營管理論叢, Vol.4, No.1, 43-63.
吳嘉席. (2010). 台灣上市連接器產業公司經營績效之探討- 資料包絡分析法之應用
李昆峰. (2004). 應用資料包絡法於半導體構裝外包廠之績效評比
周明璇, 陳文智, & 簡禎富. (2013). 建構半導體廠跨廠績效評估以提昇資源分配決策品質之研究. 品質學報, Vol. 20, No. 4, 427~444. https://doi.org/10.6220/joq.2013.20(4).04
洪海玲. (2002). 以資料包絡分析法作製造業之營運效率分析
孫嘉正, & 葉子明. (2020). 新型冠狀病毒肺炎（COVID-19）對於製造業長鞭與短鞭效應的影響與風險管控. 《品質月刊》 56卷8期 (202008) 22-25.
高強, 黃旭男, & Sueyoshi, T. (2022). 管理績效評估：資料包絡分析法(2版).
陳世宗. (2011). 台視、中視、華視經營績效之比較分析研究-從資源投入與產出效率觀點出發. 國立空中大學管理與資訊學系-管理與資訊學報, 1~36.
黃曜鴻. (2007). 以TDD做為外包商績效指標之研究-以某記憶體製造公司為例
趙時樑, & 張貞德. (2011). 應用資料包絡分析法評估兩岸三地國際貨櫃港經營績效之研究. 運輸計劃季刊, 第四十卷 第三期, 233～260.
蔡佳昇, & 洪秀婉. (2023). 運用資料包絡分析法與平衡計分卡於紡織業經營績效探討. 中原企管評論, 第二十一卷第一期, 57-88.