| 摘要: | 隨著科技持續演進及全球電子產品市場的快速擴張，電子組裝產業正面臨空前的競爭壓力。在此背景下，供應商的選擇不再只是成本取向的簡單決策，而成為企業維持生產效率、確保品質穩定與強化供應鏈韌性的關鍵。本研究以電子組裝產業中具代表性的揚聲器零組件為對象，運用層級分析法（Analytic Hierarchy Process, AHP）結合專家訪談與文獻回顧，建構科學、系統且具操作性的供應商評選架構。透過AHP將複雜的跨部門決策問題轉化為邏輯清晰的層級結構，明確釐清各構面與指標間的相對權重，提升決策的透明度與一致性。研究發現，「生產與技術能力」、「品質穩定性」及「交貨準時性」為企業最重視的三大關鍵構面，合計權重超過70%，顯示在技術與品質要求日益嚴苛的趨勢下，價格已非企業選擇供應商的首要考量。
本研究進一步指出，企業在供應商評選時應跳脫傳統成本與品質導向的思維，納入更多元的考量因素，包括技術創新能力、服務反應速度、環境與合規風險，以及與企業長期策略的契合度等，構築具備彈性與調整能力的多維度評選模型，以因應日益不確定的市場環境。該模型不僅提升企業決策的精準度，也強化整體供應鏈的穩定性與應變能力。未來研究可擴大探討該評選架構於不同產業類型、區域供應網絡及國際採購情境下的適用性，並結合如機器學習與模糊理論等資料導向的決策工具，進一步提升模型的預測力與彈性。透過跨部門合作與實證分析，可強化模型在實務中的可行性與操作性，促進供應商管理策略持續優化，最終助力企業在激烈競爭中實現永續發展。
;As technology continues to evolve and global demand for electronic products grows, the electronic assembly industry faces intensified competitive pressure. In this environment, supplier selection has become a strategic imperative for maintaining production efficiency, ensuring product quality, and safeguarding supply chain stability. This study centers on speaker components as a representative case within the industry and utilizes the Analytic Hierarchy Process (AHP), supported by expert interviews and literature review, to build a structured and operational supplier evaluation model. AHP helps transform complex, multi-criteria decisions into a hierarchical structure that clarifies the relative importance of key factors. Findings indicate that “production and technical capability,” “quality stability,” and “on-time delivery” are the top three determinants in supplier evaluation, collectively accounting for over 70% of the decision weight—underscoring a shift from cost-driven decisions toward a more quality- and capability-focused approach.
In addition, the study emphasizes the need to expand supplier evaluation criteria beyond traditional cost and quality measures. Enterprises should incorporate broader considerations such as innovation potential, responsiveness, regulatory compliance, and alignment with long-term strategic goals. By adopting a flexible, multi-dimensional evaluation framework, companies can enhance decision accuracy and build more resilient, adaptable supply chains. The study also recommends further exploration of this framework′s relevance in different industries, regions, and global sourcing scenarios. Moreover, integrating advanced tools like machine learning and fuzzy logic can improve the model’s predictive capabilities. Through cross-functional collaboration and empirical validation, this approach offers a robust foundation for optimizing supplier management strategies and fostering long-term competitive advantage in an increasingly volatile global market. |
