A systematic review of the Process Mining literature in the Supply Chain

Document Type : Original Article

Authors

1 PhD Student, Department of Industrial Engineering, Faculty of engineering, Qom University of Technology, Qom, Iran. arghiani.m@qut.ac.ir

2 Corresponding Author, Prof, Department of Industrial Engineering, University of Qom, Qom, Iran. j.rezaee@qom.ac.ir

3 PhD Student, Department of Industrial Engineering, Faculty of engineering, Qom University of Technology, Qom, Iran. . rahnama.a@qut.ac.ir

4 Assistance Prof, Department of computer Engineering, National University of Skill, Qom, Iran. zroozbahani@nus.ac.ir

10.22091/jemsc.2026.15287.1347

Abstract

This paper presents a systematic and comprehensive review of PM applications across all critical SC domains. The study identifies key trends, leading countries—specifically highlighting Germany as the leading nation in this research field—widely adopted methods and tools, and the major challenges faced by practitioners. A five-layer analytical framework based on the "Data-to-Value" is introduced to classify PM challenges in SC contexts. The findings reveal a strong emphasis on production and logistics, with discovery as the most frequently applied PM method. Disco and ProM are identified as the most commonly used tools, while data quality and completeness, human factors and expertise, organization and stakeholders, integration of data sources, model complexity and scalability and efficiency remain significant challenges. The study also highlights the growing importance of predictive analytics and the need for domain-specific adaptations of PM techniques. Ultimately, this review identifies research gaps and offers recommendations for advancing the integration of PM into SC, emphasizing the potential for future synergies between data-driven process analysis and SC optimization.

Keywords

Main Subjects

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