IMPROVING BUSINESS PROCESS IN SEMICONDUCTOR MANUFACTURING BY DISCOVERING BUSINESS RULES

Abstract

Process and data are equally important for business process management. Data is especially relevant in the context of automated business processes, and process controlling. In the context of knowledge-intensive domain, data modified by engineers and used by a fully automatic manufacturing system can lead to unpredictable errors. In this paper, a method permitting the discovery of truly enforced business rules used by the process is proposed. These rules will be highlighted using data from the model of the studied process. The proposed methodology is mainly based on a data mining approach. The proposed method has been tested on data from semiconductor industry, in which processes are known to be complex, and the number of business rules is known to be important. The results show that the method is efficient in assisting engineers in process errors detection and practical process improvement.

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