隨著製造業面臨多樣化、高效率及高品質的競爭趨勢，應用資訊科技整合企業內部製造資源的電腦整合製造系統逐漸扮演著重要角色。CIM服務台為製造工廠資訊系統的整合性服務窗口，每天需接收到大量的事件反應，為了降低事件對企業所造成的商業損失以及提高使用者對系統的滿意度，服務台必須盡速解決事件以恢復系統服務運作，這也是IT事件管理的首要目標。在過往事件的處理中，主要是仰賴服務台人員本身的經驗與專業，透過人工的方式去執行事件管理流程的各項步驟，但隨著人員流動與知識分散，如何維持系統服務的支援水平已成為服務台重要的挑戰。
本研究將建置一個自動化支援事件管理之模式，藉由過去累積的歷史事件，運用文字探勘與機器學習方法來預測新事件的分類，並且基於分類結果與已知的事件屬性，透過層級分析法與案例式推理建立一個推理模型，從案例知識庫檢索過去相似的事件，協助服務台快速完成事件分類與找出解決方案之建議。

In today's manufacturing industry, computer integrated manufacturing system gradually plays an important role, in order to reduce the negative business impact caused by the interruption of system services, the service desk must resolve the incident as soon as possible to restore the system service operations, which is also the primary goal of IT incident management. In the handling of past incidents, the steps of the incident management process are performed in a manual way, resulting in incident handling being time-consuming and dependent on the knowledge and experience of service desk personnel. Therefore, an effective automated decision support approach is urgently needed to ensure response time and support levels. This study will propose an incident decision support model through machine learning classification technology and case-based reasoning to optimize the overall IT services of computer integrated manufacturing systems and improve user satisfaction.

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