本研究旨在檢驗筆者於2020年初導入個案公司之「共享工具管理模式」，以證明該模式運行多年後之成效，並結合學術理論來拓展新觀點，使其成為資源受限情境下之應用。
本研究以個案探討方式，針對半導體設備商在工具管理上面臨的挑戰，提出「問題分析—資源盤整—方法設計與執行」的研究架構。個案公司因應產業快速變動，投入大量資源採購維修與測試工具，但因缺乏統一管理，使得調度效率低落、工具遺失與重複採購、資產利用率低、風險控管困難等問題。研究經由工程師與物流人員訪談，將管理焦點聚焦於「共享工具」，並盤點現有資源，發現DHL物流合作具備跨廠運輸能力，但資訊整合仍有缺口。
本研究設計並導入「共享工具管理模式」，包含共享工具運送迴路與清單更新迴路，通過序號化、標籤化與物流紀錄整合，制定可追蹤、可視化的管理機制。模式導入後，工具共享效率（TSE）由1.19提升至3.26 (+174%)，單位工具成本（CPIB）下降54%，工具投入效率（TEI）提升115%，顯示管理制度改革有效提升資源利用率並降低成本。成本彈性分析顯示，共享效率每增加1%，CPIB平均下降0.76%，形成高效率、低成本敏感的結構。
研究亦指出執行過程中存在登錄誤差、流程遵循不全、人員經驗差異等限制，建議未來導入自動化掃描、雲端同步、生命週期管理模組及教育訓練，並擴展至跨公司或跨地區共享平台。整體而言，本研究設計了一套具可行性、低成本、可擴充性的共享工具管理模式，為半導體產業資源管理提供重要參考。

This study aims to evaluate the efficiency of the “Shared Tooling Management Model” introduced by the author to the case company in early 2020. By examining its performance after several years of implementation and integrating relevant academic theories, the study seeks to extend new perspectives and position the model as an applicable approach in resource-constrained environments.
This study employs a case-based approach to address tooling management challenges in semiconductor equipment suppliers, proposing a framework that encompasses problem analysis, resource consolidation, and execution. Despite heavy investment in maintenance and testing tooling, the case company suffered from inefficient scheduling, tooling loss, and low utilization due to a lack of unified management. Interviews identified 'shared tooling' as the primary focus and DHL logistics as a transport partner, though information integration remained insufficient.
To address these issues, a 'Shared Tooling Management Model' featuring transport and inventory update loops was implemented. By utilizing serialization, labeling, and logistics record integration, a traceable management mechanism was established. Post-implementation results demonstrated significant improvements: tooling sharing efficiency (TSE) increased from 1.19 to 3.26 (+174%), tooling cost per machine (CPIB) decreased by 54%, and tooling investment efficiency (TEI) rose by 115%. Cost elasticity analysis further revealed that a 1% increase in sharing efficiency results in a 0.76% decrease in CPIB, indicating a structure sensitive to high efficiency.
Limitations identified include registration errors and incomplete adherence to the process. Recommendations include adopting automated scanning, cloud synchronization, and lifecycle management modules. Overall, the study presents a feasible, low-cost, and scalable model that offers valuable insights into resource management in the semiconductor industry.

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