Development of fuzzy clustering engine for decision making in manufacturing

This paper proposes a two-stage fuzzy clustering engine by combining fuzzy clustering method and fuzzy inference method. In the first stage, the raw data during manufacturing process can be classified based on its own nature, and with the classification results, operation decision can be made in the second stage by running fuzzy inference engine without relying on highly skilled expert. The objective of the proposed method eliminates the human interference so that the decision can be much more impersonal and reliable. In addition, to improve the performance, fuzzy rules in this engine are mostly derived from the natural feature of the raw data rather than the tradition fuzzy rules formalization. Also, a case study of a company based in Singapore has been presented, and the results are promising and satisfied by the company.