電腦整合製造(Computer Integrated Manufacturing, CIM) 是利用電腦控制網路與管理技術，將產品由工廠投入到半成品產出的所有生產過程整合在一起。例如在薄膜電晶體液晶顯示器(Thin Film Transistor Liquid Crystal Display, TFT-LCD) 工廠中，CIM利用電腦及網路系統整合工廠內生產過程的資訊流與物流等功能，期望在最短時間內，用最低的成本發揮最大的產能，並產出適當的產品，以幫助工廠的生產過程達到高效率及高品質。然而，CIM系統因整合各式子系統而變得龐大複雜，且各子系統的異常處理分屬各個不同子部門負責，因此需要客服中心 (Help Desk) 成為CIM系統整合性的服務窗口，但由於客服中心的人力有限且不一定是該子系統的負責人，往往只能靠長期的值班經驗累積和員工訓練來有效判斷問題的分類並推斷出適當解決窗口。因此現行系統問題需要人工進行判斷與處理。而為了保留員工的知識與經驗，避免這些隱性的知識資產因人員異動而流失，或是因資訊過於分散而無法有效分享，我們以建構知識管理系統的方式來解決此問題。
本研究將個案文字案例化，並以案例式推理(Case-Based Reasoning, CBR) 建構一個案例知識的推理模組。經由我們的實驗結果顯示，在 2000 筆案例樣本的測試中，我們所提出的方法可以達到平均 80% 的正確率。

Computer integrated manufacturing (CIM) system using the most modern computer and network technology to manage and control the whole production activities in a thin film transistor liquid crystal display (TFT-LCD) factory. The Purpose of CIM system is to integrate the functions of material control and information flows to improve the production efficiency and to optimize the product quality. Since the helpdesk is the only service contact window of CIM system and the agent members in helpdesk may not increase as CIM system becomes more and more complex. How to help the helpdesk agent to find out system problem quickly and dispatch contact window to solve it correctly becomes a very important and critical issue.
Currently, we found that most documents described of system problems are using words, we can dispatch contact window of each subsystem to solve it according to these words. If techniques of case-based reasoning (CBR) Model are facilitated, the system problem would be classified quickly. CBR model not only saves human effort but also maintains the knowledge and keeps the precision of the assignment.
Furthermore, considering performance and efficiency of the system, the Cosine similarity is used to calculate the similarity of each document vector by term frequency-inverse document frequency (TF-IDF) as term weights.
In experiment results, the CBR model system was verified by 2000 documents in the domain knowledge testing, and the average precision of our method could reach 80%.

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