Skip to main content
SpringerLink
Log in

Towards Data-Driven Business Process Redesign Through the Lens of Process Mining Case Studies

  • Conference paper
  • First Online:
Business Process Management Workshops (BPM 2023)

Part of the book series: Lecture Notes in Business Information Processing ((LNBIP,volume 492))

Included in the following conference series:

  • 399 Accesses

Abstract

Process mining is widely used for business process analysis, but rarely informs Business Process Redesign (BPR) activities. We review process mining literature and BPR framework to create thematic maps of state-of-the-art process mining analyses, techniques, outcomes and BPR best practices. We collect 156 case studies where process mining is applied and use them to validate the proposed themes. We reveal connections between the themes to explore the synergy between process mining and process redesign. Our work contributes to the development of an approach for BPR practitioners to systematically leverage the process mining capabilities, providing a solid starting point for data-driven BPR.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 69.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 89.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Notes

  1. 1.

    https://www.tf-pm.org/competitions-awards/bpi-challenge.

  2. 2.

    https://www.tf-pm.org/resources/casestudy.

References

  1. van der Aalst, W.: Process Mining: Data Science in Action. Springer (2016). https://doi.org/10.1007/978-3-662-49851-4

    Book  Google Scholar 

  2. Alizadeh, M., Lu, X., Fahland, D., Zannone, N., van der Aalst, W.M.: Linking data and process perspectives for conformance analysis. Comput. Secur. 73, 172–193 (2018)

    Article  Google Scholar 

  3. Beerepoot, I., Martin, N., Koorn, J.: From insights to INTEL: evaluating process mining insights with healthcare professionals. In: HICSS (2023)

    Google Scholar 

  4. Braun, V., Clarke, V.: Using thematic analysis in psychology. Qual. Res. Psychol. 3, 77–101 (2006)

    Article  Google Scholar 

  5. vom Brocke, J., Mendling, J.: Business Process Management Cases: Digital Innovation and Business Transformation in Practice. Springer, Heidelberg (2018). https://doi.org/10.1007/978-3-319-58307-5

    Book  Google Scholar 

  6. Ceravolo, P., Tavares, G.M., Junior, S.B., Damiani, E.: Evaluation goals for online process mining: a concept drift perspective. IEEE Trans. Serv. Comput. 15, 2473–2489 (2022)

    Article  Google Scholar 

  7. Cho, M., Song, M., Comuzzi, M., Yoo, S.: Evaluating the effect of best practices for business process redesign: an evidence-based approach based on process mining techniques. DSS 104, 92–103 (2017)

    Google Scholar 

  8. Diaz, O.E., Perez, M.G., Lascano, J.E.: Literature review about intention mining in information systems. J. Comput. Inf. Syst. 61, 295–304 (2021)

    Google Scholar 

  9. Dixit, P.M., Buijs, J.C.A.M., van der Aalst, W.M.P., Hompes, B.F.A., Buurman, J.: Using domain knowledge to enhance process mining results. In: Ceravolo, P., Rinderle-Ma, S. (eds.) SIMPDA 2015. LNBIP, vol. 244, pp. 76–104. Springer, Cham (2017). https://doi.org/10.1007/978-3-319-53435-0_4

    Chapter  Google Scholar 

  10. Dumas, M., La Rosa, M., Mendling, J., Reijers, H.A.: Fundamentals of Business Process Management, 2nd edn. Springer, Heidelberg (2018). https://doi.org/10.1007/978-3-662-56509-4

    Book  Google Scholar 

  11. Dunzer, S., Stierle, M., Matzner, M., Baier, S.: Conformance checking: a state-of-the-art literature review, pp. 1–10. ACM (2019)

    Google Scholar 

  12. van Eck, M.L., Lu, X., Leemans, S.J.J., van der Aalst, W.M.P.: PM\(^2\): a process mining project methodology. In: Zdravkovic, J., Kirikova, M., Johannesson, P. (eds.) CAiSE 2015. LNCS, vol. 9097, pp. 297–313. Springer, Cham (2015). https://doi.org/10.1007/978-3-319-19069-3_19

    Chapter  Google Scholar 

  13. Erasmus, J., Vanderfeesten, I., Traganos, K., Jie-A-Looi, X., Kleingeld, A., Grefen, P.: A method to enable ability-based human resource allocation in business process management systems. In: Buchmann, R.A., Karagiannis, D., Kirikova, M. (eds.) PoEM 2018. LNBIP, vol. 335, pp. 37–52. Springer, Cham (2018). https://doi.org/10.1007/978-3-030-02302-7_3

    Chapter  Google Scholar 

  14. Erdogan, T.G., Tarhan, A.: Systematic mapping of process mining studies in healthcare. IEEE Access 6, 24543–25567 (2018)

    Article  Google Scholar 

  15. Fehrer, T., Fischer, D.A., Leemans, S.J.J., Röglinger, M., Wynn, M.T.: An assisted approach to business process redesign. DSS 156, 113749 (2022)

    Google Scholar 

  16. Garcia, C.D.S., et al.: Process mining techniques and applications - a systematic mapping study. Expert Syst. Appl. 133, 260–295 (2019)

    Article  Google Scholar 

  17. Ghasemi, M.: Towards goal-oriented process mining. In: RE, pp. 484–489 (2018)

    Google Scholar 

  18. Gross, S., Yeshchenko, A., Djurica, D., Mendling, J.: Process mining supported process redesign: matching problems with solutions. In: PoEM, pp. 24–33 (2020)

    Google Scholar 

  19. Koorn, J.J., et al.: Bringing rigor to the qualitative evaluation of process mining findings: an analysis and a proposal. In: ICPM, pp. 120–127 (2021)

    Google Scholar 

  20. Kubrak, K., Milani, F., Nolte, A., Dumas, M.: Prescriptive process monitoring: quo vadis? PeerJ Comput. Sci. 8, e1097 (2022)

    Article  Google Scholar 

  21. Leewis, S., Smit, K., Zoet, M.: Putting decision mining into context: a literature study. In: Agrifoglio, R., Lamboglia, R., Mancini, D., Ricciardi, F. (eds.) Digital Business Transformation. LNISO, vol. 38, pp. 31–46. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-47355-6_3

    Chapter  Google Scholar 

  22. Maita, A.R.C., et al.: A systematic mapping study of process mining. Enterp. Inf. Syst. 12, 505–549 (2018)

    Article  Google Scholar 

  23. Mendling, J., Rosemann, M., vom Brocke, J.: Business Process Management Cases. Volume 2: Digital Transformation - Strategy, Processes and Execution. Springer, Berlin (2021). https://doi.org/10.1007/978-3-662-63047-1

  24. Milani, F., Lashkevich, K., Maggi, F.M., Di Francescomarino, C.: Process mining: a guide for practitioners. In: Guizzardi, R., Ralyté, J., Franch, X. (eds.) RCIS 2022. LNBIP, vol. 446, pp. 265–282. Springer, Cham (2022). https://doi.org/10.1007/978-3-031-05760-1_16

    Chapter  Google Scholar 

  25. Márquez-Chamorro, A.E., Resinas, M., Ruiz-Cortés, A.: Predictive monitoring of business processes: a survey. IEEE Trans. Serv. Comput. 11, 962–977 (2018)

    Article  Google Scholar 

  26. Nakatumba, J., van der Aalst, W.M.P.: Analyzing resource behavior using process mining. In: Rinderle-Ma, S., Sadiq, S., Leymann, F. (eds.) BPM 2009. LNBIP, vol. 43, pp. 69–80. Springer, Heidelberg (2010). https://doi.org/10.1007/978-3-642-12186-9_8

    Chapter  Google Scholar 

  27. Netjes, M., Mansar, S.L., Reijers, H.A., van der Aalst, W.M.P.: Performing business process redesign with best practices: an evolutionary approach. In: Filipe, J., Cordeiro, J., Cardoso, J. (eds.) ICEIS 2007. LNBIP, vol. 12, pp. 199–211. Springer, Heidelberg (2008). https://doi.org/10.1007/978-3-540-88710-2_16

    Chapter  Google Scholar 

  28. Nickerson, R.C., Varshney, U., Muntermann, J.: A method for taxonomy development and its application in information systems. Eur. J. Inf. Syst. 22, 336–359 (2013)

    Article  Google Scholar 

  29. Park, G., van der Aalst, W.M.P.: Action-oriented process mining: bridging the gap between insights and actions. Prog. Artif. Intell. 1–22 (2022)

    Google Scholar 

  30. Pika, A., Wynn, M.T., Fidge, C.J., ter Hofstede, A.H.M., Leyer, M., van der Aalst, W.M.P.: An extensible framework for analysing resource behaviour using event logs. In: Jarke, M., et al. (eds.) CAiSE 2014. LNCS, vol. 8484, pp. 564–579. Springer, Cham (2014). https://doi.org/10.1007/978-3-319-07881-6_38

    Chapter  Google Scholar 

  31. Reijers, H.A., Liman Mansar, S.: Best practices in business process redesign: an overview and qualitative evaluation of successful redesign heuristics. Omega 33, 283–306 (2005)

    Article  Google Scholar 

  32. Sato, D.M.V., Freitas, S.C.D., Barddal, J.P., Scalabrin, E.E.: A survey on concept drift in process mining. ACM Comput. Surv. Article 189 (2021)

    Google Scholar 

  33. Taymouri, F., Rosa, M.L., Dumas, M., Maggi, F.M.: Business process variant analysis: survey and classification. Knowl. Based Syst. 211, 106557 (2021)

    Article  Google Scholar 

  34. Zerbino, P., Stefanini, A., Aloini, D.: Process science in action: a literature review on process mining in business management. Technol. Forecast. Soc. Change 172, 121021 (2021)

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. School of Information Systems, Queensland University of Technology, Brisbane, Australia

    Zeping Wang, Rehan Syed & Chun Ouyang

  2. Centre for Data Science, Queensland University of Technology, Brisbane, Australia

    Zeping Wang & Chun Ouyang

Authors
  1. Zeping Wang
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Rehan Syed
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Chun Ouyang
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Zeping Wang .

Editor information

Editors and Affiliations

  1. KU Leuven, Leuven, Belgium

    Jochen De Weerdt

  2. TUM School of Computation, Information and Technology, Heilbronn, Germany

    Luise Pufahl

Rights and permissions

Reprints and permissions

Copyright information

© 2024 The Author(s), under exclusive license to Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Wang, Z., Syed, R., Ouyang, C. (2024). Towards Data-Driven Business Process Redesign Through the Lens of Process Mining Case Studies. In: De Weerdt, J., Pufahl, L. (eds) Business Process Management Workshops. BPM 2023. Lecture Notes in Business Information Processing, vol 492. Springer, Cham. https://doi.org/10.1007/978-3-031-50974-2_20

Download citation

  • DOI: https://doi.org/10.1007/978-3-031-50974-2_20

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-50973-5

  • Online ISBN: 978-3-031-50974-2

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation