建構半導體晶圓製造廠跨廠績效評估模式及其實證研究

Construct Inter-fab Efficiency Evaluation Models for Semiconductor Wafer Fabrication Facilities and its Empirical Study

周明璇(Ming-Hsuan Chou)

指導教授 ： 簡禎富

國立清華大學/工學院/工業工程與工程管理學系/碩士(2009年)

https://doi.org/10.6843/NTHU.2009.00215

摘要

半導體為資本密集且高度競爭的產業，因此為了維持企業的競爭優勢，如何有效地運用各種投入資源以提供更多產出以提升生產力即成為各企業重要的課題。回顧過去在探討半導體績效之文獻，大部分是針對半導體同業之間的比較，對於企業內部各個廠區之效率評估則鮮少著墨。本研究目的係建立半導體業之跨廠的生產績效評估，利用資料包絡分析模式使用多項製造指標以衡量各廠之相對效率，同時考量理論與實務的情況給予各項指標改善的方向與建議。藉由企業內部廠際間的評比，可以幫助管理者瞭解各個決策單位(Decision Measure Unit, DMU)的執行狀況，並作適當地資源配置，參考評估的結果設定改善的方向與幅度，更進一步，藉由廠與廠之間的學習與競爭，將有助於整體績效的提升。依據實務上資料會不斷更新的特性，更進一步使用BPNN建構跨廠績效之預測模式，使得企業能夠做即時性的評估，並針對現在或未來擬定合適的策略和資源的規劃。本研究結果指出晶圓廠持續擴廠可能會讓效率呈反向下降，因此各廠必須特別評估擴廠是否合乎效益，並以實例說明生產規模與生產績效之間的變化趨勢，分析各廠所面臨的問題及改善方向，也提供未來建廠的參考依據。跨廠績效評比亦可找出各廠的優勢與弱勢指標，提供管理者如何強化優勢與改善弱勢的方向，以更有效的資源分配來提升跨廠效率值，並可根據跨廠效率預測結果，讓管理者事先對未來效率值可能下降的廠提高警覺，加以調整資源的配置。有鑑於此，實證研究進一步驗證了本研究之效度與可行性。

關鍵字

績效評估 ； 跨廠績效 ； 資料包絡分析法 ； 紫式決策分析 ； 倒傳遞類神經網路 ； 半導體製造

並列摘要

Semiconductor industry is capital intensive and competitive. Thus, it is important to utilize various resources efficiently to generate products for maintaining competitive advantages. Most of the existing studies on semiconductor performance evaluation focused on the company-to-company comparison. This study aimed to construct efficiency evaluation among the semiconductor fabrication facilities(fabs) since little research has been done on fab-to-fab efficiency evaluation (i.e. inter-fab evaluation.) In particular, the proposed model used multiple indices to measure inter-fab relative efficiency through data envelopment analysis. Managers can understand each decision making unit’s condition to set the improvement direction and make appropriate resource allocation decision through the inter-fab comparison. In addition, we applied backpropagation neural network to construct the inter-fab performance forecast model according to the characteristic of dynamic data update. Therefore, semiconductor companies can do real-time evaluation and make better strategies and resources planning. We used empirical data to validate the proposed model by analyzing the pattern between production scale and production efficiency. We gave improvement direction for each fab to provide references for the future fab construction. Managers can strengthen their advantage and improve their disadvantage for better inter-fab performance based on the advantage index and disadvantaged index for each fab. According to the inter-fab performance forecast results, managers can be alertness and adjust their resource allocation before the future efficiency go down. Thus, the results showed the practical viability of the proposed approach.

並列關鍵字

performance evaluation ； inter-fab efficiency ； data envelopment analysis ； UNISON decision framework ； backpropagation neural network ； semiconductor manufacturing

參考文獻

高強，黃旭男，Toshiyuki Sueyoshi (2003)，管理績效評估─資料包絡分析法，華泰文化出版社，台北。

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