Application Fields and Research Gaps of Process Mining in Manufacturing Companies

Abstract

To survive in global competition with increasing cost pressure, manufacturing companies must continuously optimize their manufacturing-related processes. Thereby, process mining constitutes an important data-driven approach to gain a profound understanding of the actual processes and to identify optimization potentials by applying data mining and machine learning techniques on event data. However, there is little knowledge about the feasibility and usefulness of process mining specifically in manufacturing companies. Hence, this paper provides an overview of potential applications of process mining for the analysis of manufacturing-related processes. We conduct a systematic literature review, classify relevant articles according to the Supply-Chain-Operations-Reference-Model (SCOR-model), identify research gaps, such as domain-specific challenges regarding unstructured, cascaded and non-linear processes or heterogeneous data sources, and give practitioners inspiration which manufacturing-related processes can be analyzed by process mining techniques.