本研究旨在探討新產品導入階段之製程風險管理問題，以伺服器散熱風扇製程為研究對象，結合失效模式與影響分析（FMEA）與多準則決策分析方法，建立一套適用於新產品開發之風險辨識與改善架構。個案公司原有產品以車用與電腦散熱風扇為主，近年開始跨入伺服器散熱風扇市場。由於伺服器散熱風扇在高轉速、高可靠度及長時間運轉等條件下，其風險特性與傳統產品存在明顯差異，原有 FMEA 架構已無法完全反映新產品之製程風險。因此，本研究以公司既有 FMEA 資料為基礎，透過具十年以上風扇製造經驗之專家，針對新產品失效模式重新進行風險評估與重要性判斷，以建立更符合伺服器散熱風扇特性之風險分析架構。本研究首先透過專家問卷蒐集各失效模式於嚴重度、發生度與偵測度三構面之評分，建立製程風險決策矩陣。其次，導入層級分析法（AHP）重新計算三構面之相對權重，以改善傳統風險優先數（RPN）假設三構面等權所造成之風險排序偏差。最後，將AHP權重導入理想解類似度偏好順序技術（TOPSIS），進行失效模式風險排序分析，以建立更具客觀性與系統性之製程風險評估模式。研究結果顯示，透過結合專家經驗、AHP與TOPSIS之整合分析架構，能有效提升新產品導入階段之風險辨識能力與風險排序合理性，並可作為企業後續新產品開發、試產驗證與量產改善之重要參考依據。除可強化品質管理系統之預防能力外，亦有助於建立可持續回饋與循環改善之製程風險管理機制。

This study investigates process risk assessment in server cooling fan manufacturing by integrating PFMEA, AHP, and TOPSIS methods. Traditional PFMEA evaluates risk through the Risk Priority Number (RPN); however, it assumes equal importance among Severity (S), Occurrence (O), and Detection (D), which may lead to inaccurate risk prioritization. Therefore, this study uses the Analytic Hierarchy Process (AHP) and the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) to improve the accuracy of process risk assessment. Expert questionnaires were collected from specialists with over ten years of experience in cooling fan manufacturing. The results show that the proposed approach can effectively distinguish critical failure modes and provide a more reliable basis for process improvement and quality management decision-making.

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