資料包絡分析法於汽車零配件供應商績效評估

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姓名

吳明陽(Ming-Yang Wu) 查詢紙本館藏

畢業系所

土木工程學系

論文名稱

資料包絡分析法於汽車零配件供應商績效評估
(Data Envelopment Analysis for Auto-Assembler Suppliers' Evaluation of Performance)

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摘要(中)

長式摘要
台灣汽車產業二十世紀以來，由汽車整車業開始發展，其中汽車零配件高達30000種，帶動了零配件產業的商機，由2005年7月1日經濟部電子報資料來源顯示，2004年內外銷總產值達2338億元新台幣，以超越整車業總產值(約2100億元新台幣)。而全部汽車零配件業者約2800家，車輛就業人口約20萬人，約佔製造業就業人口8%。
汽車零配件主要的銷售管道可分為原廠組裝使用的零件OEM(Original Equipment Manufacturing)與售後維修車廠所使用的零件AM(After Market)。OEM市場為原廠零配件市場，AM市場則是售後維修及改裝車使用副廠零配件市場。由於原廠汽車零配件品質要求與管控較為嚴格，再加上運送的問題，因此目前台灣汽車零配件廠商有些雖然也切入國際大車廠的原廠零件代工，但整體而言市佔率較大者，仍以AM市場為主。台灣零組件製造廠約供應全球85%-90%的AM市場零組件。2800多家汽車零件廠商中，300家左右為國內10大車廠旗下的OEM廠商，其他廠商仍屬於AM市場供應商，AM市場中的汽車零件包括車燈、鈑金、塑料及保險桿等。
江銘章 (2003)，李宏學 (2004)曾探討汽車零配件產業中廠商的發展與經營策略，但並沒有進行績效評估。而台灣的汽車零配件廠商眾多，大部分屬中小企業(Small Medium Enterprise, SMEs)，針對廠商眾多且規模小，政府積極推動的中衛中心體制已有多年，主要在促成中小企業間結合，增加產業上、中、下游合作。而中衛中心站在零件廠商輔導角色，同時廠商針對本身公司經營效率進行改善，兩者之間的互動甚為密切。有鑑於此，建立一適當績效評估方法重要性甚大。本研究嘗試以資料包絡分析法(Data Envelopment Analysis, DEA)進行台灣汽車零配件廠商績效評估。
本研究透過回顧相關文獻，包括績效評估流程、汽車產業重視的績效指標種類、DEA應用於績效評估相關文獻等，以及國內探討汽車產業文獻，針對汽車零配件業生產特性，本研究以員工人數、公司資本額、產品製造時間與存貨天數為投入變數，營業收入為產出變數。但是考慮到國內汽車零件業AM市場的廠商多屬於中小企業，上述投入與產出變數的資料蒐集困難，故本研究只能以問卷方式以取得廠商相關資料以進行績效評估。為增加廠商回覆意願，針對部分較具有機密性的資料，本研究以取得區間值的方式進行問卷設計。並以可以分析不明確資料的α-cut DEA(Alpha-Approach Data Envelopment Analysis)、PFDEA(Possibility Fuzzy Data Envelopment Analysis)與IDEA(Imprecise Data Envelopment Analysis)三種方法進行整體技術效率值(Overall Technical Efficiency, OTE)求解與分析。接下來，則是透過敏感度分析證實「產品製造時間」與「存貨天數」對廠商效率值影響甚鉅，此兩項投入加入使廠商效率值評估結果更具實務上的義涵。最後，則是以α-cut DEA分析受評估廠商的純技術效率(Pure Technical Efficiency, PTE)、規模效率(Scale Efficiency, SE)與標竿廠商等分析，以進一步提供廠商間效率比較之參考依據。
本研究所採用之績效評估方法的特性如下：α-cut DEA的優點為：有worst-worst、best-best、best-worst與worst-best四種分析角度，增加廠商間效率值的參考依據，若某一廠商在四種分析角度中，效率值均為”1”，可判斷該廠商為絕對標竿廠商。但缺點是結果容易受隸屬函數的類型所影響。以本研究而言，若方形隸屬函數區間值選取不當，由模式試算結果發現，將會造成廠商間效率差異過大。而PFDEA的優點為：有悲觀(pessimistic)與樂觀(optimistic)兩種分析角度，可以使效率值為”1”的廠商能進一步比較優劣。但缺點是容易造成部分廠商效率差異過大，使廠商效率比較基準不合理，且效率差與效率極佳的廠商在改進方向差距太大，失去效率比較的意義。此外，IDEA的優點為：若投入產出項資料屬於區間資料，以最原始處理不精確資料的IDEA模式進行求解，較為合理。但缺點是：分析結果容易受區間值的影響，使得多數廠商效率值均相等。以本研究而言，大部分廠商效率值均為”1”，若評估對象增多且投入產出項區間值均相似，則廠商間優劣無法分辨，以致無效率改進方向可循。
最後，針對本研究內容歸納出下列六點結論，做為國內汽車零組件廠商績效評估與改善之參考。
1. 本研究中以α-cut DEA、PFDEA與IDEA三種方法進行廠商效率分析，來處理因問卷回覆資料具機密性或資料不完整問題，使求解更具彈性能處理不同形態或不齊全的資料。
2. 本研究探討了台灣汽車零配件產業特性後，除了DEA模式常用的資本額、員工人數為投入項外，加入了產品製造時間和產品存貨天數兩投入項，更能反映產業的特性，使評估的結果更具說服力，另外以年營業收入為產出項，總計共四項投入，一項產出。
3. 本研究針對製造時間和產品存貨天數兩項變數，針對α-cut DEA、PFDEA與IDEA三種方法進行總體技術效率值敏感度分析，結果發現此兩項變數對效率值的影響甚鉅，證實此兩項投入加入使廠商效率值評估結果更具意義。
4. 在實證分析中，考量問卷回收與部分公司財務資料無法取得，針對員工人數與營業收入為區間資料的特性，以α-cut DEA、PFDEA與IDEA三種方法計算各廠商之總體技術效率，探討三種方法的在進行效率評估時的優缺點，此分析發現具代表性，可供後續研究參考。
5. 考量問卷回收資料完整性，本研究以39家汽車零配件廠商為例進行分析，包含了20家汽車零件廠商與19家汽車配件廠商。針對α-cut DEA效率分析結果，提出管理上的意含，發現大部分廠商均已達純技術效率，整體技術無效率來源為規模無效率。同時，由best-best、worst-worst、best-worst、worst-best四種角度分析廠商效率，找出具有絕對效率的標竿廠商。
最後，提出下列兩點建議以供後續研究之參考。
1. 因為本研究以DEA模型為基礎分析投入產出之間的效率轉換情況，來衡量廠商相對效率，總計四項投入，一項產出。建議未來可增加服務品質產出項，以增加後續DEA績效評估於此汽車零配件產業之實用性。
2. 本研究僅針對39家汽車零配件廠商進行效率求解，主要礙於問卷收集資料較為緩慢，以及部份廠商問卷回覆資料提供不全。然本研究績效評估步驟，應可適用各汽車零配件廠商，針對大部分廠商屬於中小型企業，在收集實際營運上的資料本屬較為困難，未來若能建立更快速且完整的收集資料動作，對整體汽車零配件產業有一全面性的分析，俾能使DEA模式效率評估的對象更全面，評估結果更客觀。

摘要(英)

Abstract
This thesis specially addresses performance evaluation of auto-assembler suppliers in after market (AM). In order to tackle these issues, this study adopts data envelopment analysis (DEA) to measure the relative efficiency of resource utilization of auto assemblers in Taiwan. The input variables of the proposed DEA model include the operational indices and the financial indices. They are “the number of employees”, “the annual asset”, “the average inventory time”, and “the average production time”. The output variable is “the annual revenue” Considering the data imprecision about these two variables, “the number of employees” and “the annual revenue”, this thesis adopts three approaches of imprecise DEA to evaluate auto-assembler suppliers’ relative efficiency and to deal with the data imprecision. All approaches are based on the DEA methodology, including α-cut DEA approach, possibility FDEA approach and IDEA approach.
In order to follow the principle “homogeneous” of DMUs when applying DEA model to evaluate efficiency, this study categorizes auto-assembler suppliers into auto-component suppliers and auto-accessories suppliers, by means of the products. This study sends questionnaires to the 303 Taiwan auto assemblers of after market, and 75 questionnaires are received. 39 auto-assembler suppliers are selected out of 75 responses. All of our survey firms are Small Medium Enterprises (SEMs).
This thesis has successfully applied α-cut DEA, PFDEA and IDEA approaches to evaluate auto assemblers’ performance. We also find advantage and disadvantage of each approach. Besides, the efficiency score of comparison analysis, OTE analysis and PTE analysis are provided to the auto assemblers for their reference. The main research finding indicates that Taiwan auto assemblers are already operating at respectable levels of pure technical efficiency. The thesis finds that the overall technical inefficiency of auto assemblers of Taiwan is caused from the scale inefficiency. The implication of results reflect the auto assemblers operate well in company management even if the most part of auto assemblers in Taiwan are SMEs in realistic situation.

關鍵字(中)

★ 績效評估
★ 汽車零配件供應商
★ 資料包絡分析法

關鍵字(英)

★ Data Envelopment Analysis
★ Performance Evaluation
★ Auto-Assembler Suppliers

論文目次

Table of Contents
長式摘要 I
Abstract IV
Acknowledgement V
Table of Contents VI
List of Figures VIII
List of Tables VIII
Chapter 1 Introduction 1
1.1 Research Background and Motivation 1
1.2 Research Objectives 2
1.3 Research Methods and Framework 3
1.4 Organization of the Thesis 5
Chapter 2 Literature Review 6
2.1 Performance Criteria of Suppliers’ Efficiency Evaluation 6
2.2 Application of DEA Methodology on Performance Evaluation 9
2.3 Summary of This Chapter 11
Chapter 3 Data Envelopment Analysis 12
3.1 Literature Review of DEA 12
3.2 Some Approaches for the Fuzzy Data 16
3.2.1 Defuzzification Approach 16
3.2.2 α-Cut Approach 17
3.3 Literature Review of Fuzzy DEA 18
3.4 Possibility Approach to CCR Model 21
3.4.1 Possibility Approach- an Optimistic Viewpoint 21
3.4.2 Possibility Approach- a Pessimistic Viewpoint 24
3.5 IDEA Approach 26
3.6 Summary of This Chapter 26
Chapter 4 Taiwan Auto Assemblers’ Industry Environment 28
4.1 Literature Review of Taiwan Automotive Industry 28
4.2 Brief Discussions of the Taiwan Auto Assemblers’ Environment 29
4.3 Challenges of Taiwan Auto Assemblers 31
4.4 Survey Questionnaire Data of Auto Assemblers 32
Chapter 5 Case Study 38
5.1 The Sampling Procedure and Description 38
5.2 Input/Output Variables of DEA Model 39
5.3 Interval Data of the Two Variables 39
5.4 Influence Degree of Input Variables on the Efficiency Score 44
5.5 Comparison of the Three Approaches 47
5.6 Data Analysis and Results 52
5.6.1 Efficiency Score of CCR-I Model 52
5.6.2 Efficiency Score of BCC-I Model 55
5.6.3 Overall Efficiency Scores 57
5.6.4 Reference Comparison Analysis 60
5.6.5 Sensitivity Analysis 62
5.7 Results Implication of This Chapter 64
Chapter 6 Conclusions and Future Research 65
6.1 Conclusions 65
6.2 Suggestion of Future Research 66
Reference 68
Appendix I 71
Appendix II 75

參考文獻

Reference
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指導教授

陳惠國、張美香(Huey-Kuo Chen)

審核日期

2006-7-3

推文