Combining Process Mining with Trace Clustering: Manufacturing Shop Floor Process - An Applied Case

Abstract

Process mining allows observing process execution based on real event data and proposes methods and tools to provide diagnostics, reducing the gaps between practice and conceptual models. When process mining discovery techniques are applied in flexible processes with many decisions at runtime, the results are often semi-structured or unstructured process models that are difficult to understand. In this context, trace clustering is an approach for reducing the complexity of process models and improving the accuracy and comprehensibility. This paper presents an applied case in industrial manufacturing production with an unstructured process and issues in production performance indicators. A set of techniques were used to understand how the process occurs in practice, how many trace clusters should be identified as homogeneous process variants, and what causes production inefficiency. Finally, the results identify bottlenecks caused by erroneous decisions at runtime and serve to support process improvement.

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