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A Process Mining Success Factors Model

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Business Process Management (BPM 2022)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 13420))

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Abstract

Process mining – a suite of techniques for extracting insights from event logs of Information Systems (IS) – is increasingly being used by a wide range of organisations to improve operational efficiency. However, despite extensive studies of Critical Success Factors (CSF) in related domains, CSF studies of process mining are limited. Moreover, these studies merely identify factors, and do not provide essential details such as a clear conceptual understanding of success factors and their interrelationships. Using a process mining success model published in 2013 as a conceptual foundation, we derive an empirically supported, enhanced process mining critical success factors model. Applying a hybrid approach, we qualitatively analyse 62 process mining case reports covering diverse perspectives. We identify nine process mining critical success factors, explain how these factors relate to the process mining context and analyse their interrelationships with regard to process mining success. Our findings will guide organisations to invest in the right mix of critical success factors for value realisation in process mining practice.

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Notes

  1. 1.

    https://www.processexcellencenetwork.com/process-mining/articles/why-the-real-value-of-process-mining-lies-in-simulation. Accessed 10th June 2021.

  2. 2.

    https://www.gartner.com/en/documents/3991229. Accessed 5th June 2021.

  3. 3.

    https://www2.deloitte.com/de/de/pages/finance/articles/global-process-mining-survey-2021.html. Accessed 15th June 2021.

  4. 4.

    Retrieved from: https://www.tf-pm.org/resources/casestudy. Date: 5th June 2021.

  5. 5.

    Relationship nodes are special types of nodes that define the connection between two project items.

  6. 6.

    Matrix intersection is a 2-dimensional table that displays coded content from rows and columns.

  7. 7.

    Memos allow researchers to capture thoughts and reflections during coding to justify coding choices.

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Author information

Authors and Affiliations

  1. Queensland University of Technology, Brisbane, Australia

    Azumah Mamudu, Wasana Bandara, Moe T. Wynn & Sander J. J. Leemans

Authors
  1. Azumah Mamudu
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  2. Wasana Bandara
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  3. Moe T. Wynn
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  4. Sander J. J. Leemans
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Corresponding author

Correspondence to Azumah Mamudu .

Editor information

Editors and Affiliations

  1. Sapienza University of Rome, Rome, Italy

    Claudio Di Ciccio

  2. Eindhoven University of Technology, Eindhoven, The Netherlands

    Remco Dijkman

  3. Universidad de Sevilla, Seville, Spain

    Adela del Río Ortega

  4. Technical University of Munich, Garching, Germany

    Stefanie Rinderle-Ma

Appendix: Supplementary Material

Appendix: Supplementary Material

Supplementary material for this article is available online at https://bit.ly/3qrtrOE. It contains three parts: Part A provides an overview of 62 published case reports, Part B provides example quotes that support success factor explanations, and Part C presents case evidence supporting the identified CSF relationships.

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Mamudu, A., Bandara, W., Wynn, M.T., Leemans, S.J.J. (2022). A Process Mining Success Factors Model. In: Di Ciccio, C., Dijkman, R., del Río Ortega, A., Rinderle-Ma, S. (eds) Business Process Management. BPM 2022. Lecture Notes in Computer Science, vol 13420. Springer, Cham. https://doi.org/10.1007/978-3-031-16103-2_12

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  • DOI: https://doi.org/10.1007/978-3-031-16103-2_12

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  • Print ISBN: 978-3-031-16102-5

  • Online ISBN: 978-3-031-16103-2

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