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Application of Process Mining in Logistic Processes of Manufacturing Organizations: A Systematic Review

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  • Jasim Alnahas

    (Industrial Engineering Faculty, University of Tabuk, Tabuk 47512, Saudi Arabia)

Abstract

With the continuous development in technology and changes in the logistic systems, organizations should review their logistic processes that evolve over time. To attain a good insight into the conformance of these processes with the designed process model, constant detection and monitoring is required. The main objective of this systematic review is to investigate the state of the art in process mining applications in logistics specifically related to manufacturing organizations. The review aims to analyze and assess the use of process mining techniques and models in the logistics domain, based on the selected studies. In this review, literature was searched between the years 2004 and 2022 using several inclusion and exclusion criteria. Fifteen published studies were selected and analyzed on the use of process mining in the logistics domain based on the process mining techniques and models used. All of the selected studies used models and thirteen of them used real case studies. All of the fifteen studies used one or more algorithms. Only three of these studies did not mention the modeling language used to represent the process. Moreover, seven studies focused on discussing the process discovery alone and five more addressed process discovery in addition to other types of process mining. Eight studies mentioned the process mining tools used that included DISCO and several versions of ProM.

Suggested Citation

  • Jasim Alnahas, 2023. "Application of Process Mining in Logistic Processes of Manufacturing Organizations: A Systematic Review," Sustainability, MDPI, vol. 15(15), pages 1-12, July.
  • Handle: RePEc:gam:jsusta:v:15:y:2023:i:15:p:11783-:d:1207302
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    Cited by:

    1. Bárbara Ferreira & João Reis, 2023. "A Systematic Literature Review on the Application of Automation in Logistics," Logistics, MDPI, vol. 7(4), pages 1-17, November.

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