臺灣博碩士論文加值系統

企業可透過績效評估來了解過去、目前甚至是未來公司的營運狀況，亦可做為營運危機預警的指標，如何提供一個有效且客觀的方法，來做為管理者在做決策時的參考依據就顯得其重要性，而在面對產業多投入與多產出的衡量指標，如何以客觀的角度來評估績效則顯得格外重要，資料包絡法則是常被用於評估此類績效的工具，然而資料絡包絡法須有多項的決策單位同時進行比較，無法利用單一個或是少量的決策變數（DMU）進行效率的運算，而是計算一群決策單位裡彼此的效率值以進行績效評估，所以當企業只能蒐集自己的資料時，運用資料包絡法建立績效評估模型則會遇到瓶頸。因此本研究以資料探勘中之決策樹歸納法，來學習資料包絡法的評估結果，並且在推估模型中，加入類神經網路所產生之變數，以提高評估模型的正確率。本研究透過我國相關高科技產業為單位來測試本研究的模型，並探討近年來產業的績效。經由模型建構與實際案例驗證後，本研究所建構的績效預測模型確實能夠提高決策樹歸納法對於資料包絡法的績效學習能力，並能有效地達成上述之三項目的。

Performance measurement is not only a technique frequently for estimate a business’s past, present or future performance, also be a basis for decision-making. Therefore, for a manufacturing system with multi-input and multi-output performance index, it is important to develop a subjective method to evaluate system performance. Although Data Envelopment Analysis (DEA) has been considered as a good approach in establishing this type of performance measurement system, it still has difficulties in dealing with system with incomplete information or less information. In this paper, a model which integrated DEA with Data Mining (DT) is conducted to measure enterprise performance of semiconductor industry. In addition, the Artificial Neural Network (ANN) is utilized in the model to enhance the discriminations of evaluation model.
The aims of this study are: First, to utilizing decision tree induction for building the model of DEA performance measurement; Second, combine decision tree induction with Artificial Neural Network which excellent for model-building to enhance the discriminations of evaluation model. Finally, we propose a practical case with Semiconductor Industry to test the evaluation model, and discuss the performance. The result shows that the proposed model can certainly enhance the discrimination of performance.

摘要 i
Abstract ii
誌謝詞 iii
目錄 iv
表目錄 vi
圖目錄 vii
一、序論 1
1.1 研究背景與動機 1
1.2 研究目的 3
1.3 研究設計與流程 3
二、文獻探討 5
2.1 資料包絡法 5
2.1.1 資料包絡分析法之概述 5
2.1.2 資料包絡法基本模式 7
2.1.3 投入產出項關係之認定 10
2.1.4 資料包絡法應用領域 11
2.2 決策樹歸納法 13
2.2.1 決策樹歸納法之概述 13
2.2.2決策樹基本模式 15
2.2.3 決策樹應用領域 16
2.3 類神經網路 18
2.3.1類神經基本概念與模式 18
2.3.2類神經網路應用領域 19
三、績效評估模型之建立 21
3.1 演算模式簡介 21
3.2 績效評估模型 21
3.3 資料分析 29
3.3.1 效率分析 29
3.3.2 績效辨識能力分析 30
3.3.3 敏感度分析 30
3.4 決策變數探討 31
四、我國半導體相關產業之績效評估模型測試 34
4.1 資料說明 34
4.2 資料包絡法績效評估 35
4.3 BPN變數建立 37
4.4 DT績效評估模型之訓練 41
4.5 DT績效評估模型之測試 44
4.6 DEA效率分析 47
4.7 績效辨識能力分析 50
4.8 敏感度分析 53
五、結論與建議 55
參考文獻 57

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