半導體產業是台灣經濟最重要的支柱之一，資策會評估，2014年半導體產值將逼近兩兆台幣大關，佔台灣GDP比重高達十四%，若不含晶片設計業，全台灣有超過一百家半導體製造業。
由於從消費性商品一路延伸到個人資料及醫療生計商品，因此IC的功能性測試的重要性也逐步提升，試想醫療生技商品如發現功能性失敗的情形(例如：裝在心臟內部的感應器)，是何等嚴重的問題。由此可見其風險發生的原因及相對應的影響，需具備有效的控制計畫。為了使組織能永續發展，並達到客戶服務的品質要求，持續不斷地風險評估、預防管理及立即應變計劃乃企業必然之防範作為。本研究應用傳統型的FMEA(失效模式與對應分析)的評估方式，以半導體封裝測試產業客戶品質管理系統為例，找出各製程中的主要失效模式，並依照Ford 第四版手冊的評量方式，針對每一個失效模式的嚴重度、發生頻率及被偵測度評核，最後依照每一組的評量乘積計算出每一個失效模式的 RPN。然而，傳統的RPN無法評估因子之間的相互關係；因此將FMEA(失效模式與對應分析)的評等方式，利用DEA(資料包絡分析)的SBM模型找出各因子之間的相依性，以及建議的改善方向，以實施相對應的策略。
DEA(資料包絡分析)的投入與產出相依性，可以改善FMEA(失效模式與對應分析)所無法衡量投入與產出因子的相關性，且由於當FMEA(失效模式與對應分析)中的多項失效模式的最終RPN值皆同值時，企業將無法確認資源該投入何處，而DEA(資料包絡分析)可以克服此一問題。本研究可以提共企業主在開發FMEA(失效模式與對應分析)品質管制的持續改善中，進一步的策略評估與分析，此外，可以透過 e 化系統結合此一概念，同步整合FMEA(失效模式與對應分析)並透過DEA(資料包絡分析)分析給予改善指標的建議。

Semi-conductor play a very important role to contribute for Taiwan’s economics.There is a documentation pointed out that in 2014 the valued crated by semi-conductor field around two billion NT dollars which is almost 14% of Taiwan’s GDP. Excluded the IC design house, there is over one hunders semi-conductor company in Tainwan.
Due to the IC application could extend to commerical product and health care, thus importance and accurancy of the IC’s function testing is getting more and more important. Try to image that if the product of health care with the functional fail, it will become a how serious problem. Thus, the risk between the root cause and action or activities need to have a efficiency control plan. In order to enhance the competition of a complany and satisify the customer’s quality request and expectation, the continously risk assessment, preventation and prompt action is MUST DO.Through the tranditional FMEA’s analysis based on the semi-conductor’s IC testing and assembly house’s customer complaint data to apply into the Ford’s forth edition’s FMEA format and analysis. Collecting the Severity, Occurrence and Detection of each of the failure mode and use those data to calculate the RPN of each failure mode. However, the traditional FMEA can’t find out the relative between the input and output. Thus, this research use the DEA’s advantage analysis to sustibute the FMEA. Besides, throught the DEA and those data, it could find out the related improve ratio between each of the peers rather than FMEA.
Through the input and output’s related in DEA, it could provide the related ratio than FMEA. Sometimes the RPN in one FMEA may got the same value and would cause the company can’t descide how efficient to provide one resouce to improve the failure mode.
However, DEA could overcome such kind of problem in FMEA. This research could provide the company the continusouly improvement solution and stratgy. And also company could consider to enhance the system to be an automatically calculated system including the FMEA and DEA’s advantage.

一、 中文部分
1.林凱偉，「製程危害管理系統之發展-以觸控面板製程為例」，國立中央大學環境工程研究所碩士論文，2014。
2.邱慧珍，「利用DEA改進FMECA於病人安全之醫療風險評估」，國立中央大學企業管理研究所碩士論文，2007。
3.孫國隆，「應用資料包絡分析法於失效模式與效應分析以增強評估能力之研究」 ，國立中央大學企業管理研究所博士論文，2009。
4.韓慧林、王貴民、王振陽、劉庭維、鄭曳庭，「應用失效模式與效應分析評估資訊安全管理系統之風險」，國防雜誌第二十六卷，第六期，頁107-122，2011。
二、英文部分
1.Banker, R. D., Charnes, A. and Cooper, W. W. (1984). “Some Models for Estimating Technical and Scale Inefficiencies in Data Envelopment Analysis,” Management Science, 30, 1078-1092.
2.Byrnes, P. and Freeman, M, (1999), Using DEA measures of efficiency and effectiveness in contractors performance fund allocation, Public Productivity & Management Review, 23(2), p210.
3.Charnes, A., Copper, W.W., and Rhodes, E., (1978), Measuring the efficiency of decision making units, European Journal of Operation Research, 2, p429-444.
4.Charnes, A., Cooper, W.W., Lewin, A.Y. and Seiford, L.M., (1994), Data Envelopment Analysis: Theory, Methodology and Applications, Kluwer Academic Publishers: Norwell, MA.
5.Cooper, W.W., Seiford, L.M. and Zhu, J., (2004), Handbook on data envelopment analysis, Kluwer Academic Publishers: Norwell, MA.
6.Cooper, W., L. Seiford and K. Tone (2007). Data envelopment analysis: a comprehensive text with models, applications, references and DEA-solver software, Springer Verlag.
7.Cooper, W.W., Seiford, L.W. and Tone, K., (2000), Data Envelopment Analysis: A comprehensive text with model, applications, references and DEA-Solver software, Kluwer Academic Publishers: Norwell, MA.
8.DaimlerChrsler Corporation, Ford Motor Company, General Motors Corporation, Potential Failure Mode and Effects Analysis Reference Manual (2001)
9.Färe, R., S. Grosskopf and R. Brännlund (1996). Intertemporal production frontiers: with dynamic DEA, Kluwer Academic Boston.
10.Farrell, J.M. (1957) "The Measurement of Productive Efficiency," Journal of the Royal Statistical Society vol. 120, pp. 253-281.
11.Ford Motor Company, Potential Failure Mode and Effects Analysis (FMEA) Reference Manual, 1988.
12.Tone, Kaoru (2001), “A Slacks-based Measure of Efficiency in Data Envelopment Analysis,” European Journal of Operational Research, 130 (3), 498-509.
13.Tone, K. and M. Tsutsui (2010). "Dynamic DEA: A slacks-based measure approach," Omega, 38(3-4), 145-156.