Data-driven analysis of product routings in discrete manufacturing using process mining and machine learning

Abstract This paper proposes a method for automatically analysing routings using production data in discrete manufacturing environments. By integrating process mining and machine learning techniques, the method analyses process flows and their homogeneity to structure production into product families and visualise the associated models. Traditionally, Group Technology has supported the identification of product families and their associated processes by enabling the organisation of production around homogeneous objects and resources. This task is essential for current state analysis, serves as a key prerequisite for optimising production processes, and forms the foundation for Lean techniques such as Value Stream Mapping, Pull Production, and Line Balancing. However, as production systems grow more complex, traditional Group Technology faces limitations. Concurrently, Industry 4.0 has introduced advanced digitalisation, increasing the availability of production data. Leveraging this data to generate actionable insights remains a challenge for many manufacturers. This research addresses this challenge by proposing a data-driven method that overcomes the constraints of traditional Group Technology. The method is empirically validated in a discrete manufacturing setting. The study contributes to the field of production planning and control by demonstrating a novel application of process mining and machine learning for automated routing analysis in modern industrial environments.