傳統流程探勘主要是應用在發現企業流程的真實面貌，利用不同時間的審計軌跡，可以發現企業的流程偏離情形，但傳統流程探勘所發現的流程圖，大多沒有提供有價值的量化資訊，換言之，過去的流程探勘模型並沒有提供機率的資訊。我們在此提出了一個在傳統流程探勘中利用貝氏定理於其上計算出機率資訊的新方法。所得之機率不僅可以用來預期流程走向，也可配合作業成本分析出的成本資訊，計算出流程的預期成本，進而協助企業決策之進行。本文最後也利用一家台灣半導體公司的晶圓封裝測試流程來驗證我們的方法，結果證實所提出之方法可以適用於現行公司。

In modern times, business processes are getting more complex, hence it is difficult to manage them effectively. Process mining is one of the means to find hidden information from the event logs accumulated by information systems that drive business processes. Traditional process mining is mainly used to discover a real picture, usually in the form of some flowchart, of an enterprise’s processes. However, to our knowledge previous process mining techniques lack probability information concerning how frequent different portions of a business have been executed in the past. We propose an approach that increases the value of conventional process mining. This approach combines prior process mining techniques and Bayes’ Theorem to decide probability information for process mining. Probabilistic information can be used not only in making predictions, but also in making decisions. Together with activity-based costing that assigns some cost to each activity in a process, our process mining technique can calculate the expected costs of different stages in the process. This information can then be used to improve the underlying processes. We apply our approach to a chip probing process of a semiconductor firm in Taiwan. Our results confirm that the proposed approach is useful for the company’s decision making in their internal supply chain management.

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