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Process Mining Contributions to Discrete-event Simulation Modelling

Author

Listed:
  • Jadrić Mario

    (University of Split, Faculty of Economics, Business and Tourism, Split, Croatia)

  • Pašalić Ivana Ninčević

    (University of Split, Faculty of Economics, Business and Tourism, Split, Croatia)

  • Ćukušić Maja

    (University of Split, Faculty of Economics, Business and Tourism, Split, Croatia)

Abstract

Background: Over the last 20 years, process mining has become a vibrant research area due to the advances in data management technologies and techniques and the advent of new process mining tools. Recently, the links between process mining and simulation modelling have become an area of interest.Objectives: The objective of the paper was to demonstrate and assess the role of process mining results as an input for discrete-event simulation modelling, using two different datasets, one of which is considered data-poor while the other one data-rich.Methods/Approach: Statistical calculations and process maps were prepared and presented based on the event log data from two case studies (smart mobility and higher education) using a process mining tool. Then, the implications of the results across the building blocks (entities, activities, control-flows, and resources) of simulation modelling are discussed.Results: Apart from providing a rationale and the framework for simulation that is more efficient modelling based on process mining results, the paper provides contributions in the two case studies by deliberating and identifying potential research topics that could be tackled and supported by the new combined approach.Conclusions: Event logs and process mining provide valuable information and techniques that could be a useful input for simulation modelling, especially in the first steps of building discreteevent models, but also for validation purposes.

Suggested Citation

  • Jadrić Mario & Pašalić Ivana Ninčević & Ćukušić Maja, 2020. "Process Mining Contributions to Discrete-event Simulation Modelling," Business Systems Research, Sciendo, vol. 11(2), pages 51-72, October.
    • Handle: RePEc:bit:bsrysr:v:11:y:2020:i:2:p:51-72:n:5
    DOI: 10.2478/bsrj-2020-0015
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    References listed on IDEAS

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    1. Cristina Pronello & Davide Longhi & Jean-Baptiste Gaborieau, 2018. "Smart Card Data Mining to Analyze Mobility Patterns in Suburban Areas," Sustainability, MDPI, vol. 10(10), pages 1-21, September.
    2. Chen Zhong & Michael Batty & Ed Manley & Jiaqiu Wang & Zijia Wang & Feng Chen & Gerhard Schmitt, 2016. "Variability in Regularity: Mining Temporal Mobility Patterns in London, Singapore and Beijing Using Smart-Card Data," PLOS ONE, Public Library of Science, vol. 11(2), pages 1-17, February.
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    More about this item

    Keywords

    process mining; event log; simulation model; smart mobility;
    All these keywords.

    JEL classification:

    • M15 - Business Administration and Business Economics; Marketing; Accounting; Personnel Economics - - Business Administration - - - IT Management

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