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Progressing from Process Mining Insights to Process Improvement: Challenges and Recommendations

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Enterprise Design, Operations, and Computing (EDOC 2023)

Abstract

Many organizations have adopted process mining to analyze their business processes, gain insights into their performance, and identify improvement opportunities. Several academic case studies and reports from practice leave no doubt that process mining tools can deliver substantial value to organizations and help them to realize improvements. However, both organizations and academics have also realized that the path from obtaining insights via process mining to realizing the desired improvements is far from trivial. Existing process mining methodologies pay little to no attention to this matter and mainly focus on how to obtain insights through process mining. In this paper, we address this research gap by conducting a qualitative study based on 17 semi-structured interviews. We identify seven challenges pertaining to translating process mining insights into process improvements. Furthermore, we provide five specific recommendations for practitioners and stakeholders that should be considered before starting a new process mining initiative. By doing so, we aim to close the gap between insights and action and help organizations to effectively use process mining to realize process improvements.

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References

  1. van der Aalst, W.M.P., Carmona, J.: Scaling process mining to turn insights into actions. In: van der Aalst, W.M.P., Carmona, J. (eds.) Process Mining Handbook. LNBIP, vol. 448, pp. 495–502. Springer, Cham (2022). https://doi.org/10.1007/978-3-031-08848-3_17

  2. van der Aalst, W., et al.: Business process mining: an industrial application. Inf. Syst. 32, 713–732 (2007)

    Article  Google Scholar 

  3. Aksu, U., Reijers, H.A.: How business process benchmarks enable organizations to improve performance. In: EDOC, IEEE (2020)

    Google Scholar 

  4. Alvarez, C., et al.: Discovering role interaction models in the emergency room using process mining. J. Biomed. Inf. 78, 60–77 (2018)

    Article  Google Scholar 

  5. Augusto, A., et al.: Automated discovery of process models from event logs: review and benchmark. TKDE 31, 686–705 (2018)

    Google Scholar 

  6. Badakhshan, P., Bernhart, G., Geyer-Klingeberg, J., Nakladal, J., Schenk, S., Vogelgesang, T.: The action engine - turning process insights into action. In: Demo Track at ICPM (2019)

    Google Scholar 

  7. Bandara, W., Indulska, M., Chong, S., Sadiq, S.: Major issues in business process management: an expert perspective. In: ECIS, pp. 1240–1251 (2007)

    Google Scholar 

  8. Boyce, C., Neale, P., International, P.: Conducting in-depth interviews: a guide for designing and conducting in-depth interviews for evaluation input. In: PI (2006)

    Google Scholar 

  9. Bozkaya, M., Gabriels, J., van der Werf, J.M.: Process diagnostics: a method based on process mining. In: eKNOW, pp. 22–27 (2009)

    Google Scholar 

  10. Bozorgi, Z.D., Teinemaa, I., Dumas, M., Rosa, M.L., Polyvyanyy, A.: Process mining meets causal machine learning: discovering causal rules from event logs. In: ICPM. IEEE (2020)

    Google Scholar 

  11. Bryman, A.: Social Research Methods. Oxford University Press, Oxford (2016)

    Google Scholar 

  12. Cela, O., Front, A., Rieu, D.: CEFOP: a method for the continual evolution of organisational processes. In: RCIS, IEEE (2017)

    Google Scholar 

  13. 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. Decis. Support Syst. 104, 92–103 (2017)

    Article  Google Scholar 

  14. Dees, M., de Leoni, M., van der Aalst, W., Reijers, H.: What if process predictions are not followed by good recommendations? In: BPM Ind. Forum, pp. 61–72 (2019)

    Google Scholar 

  15. 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 

  16. Emamjome, F., Andrews, R., ter Hofstede, A.H.M.: A case study lens on process mining in practice. In: Panetto, H., Debruyne, C., Hepp, M., Lewis, D., Ardagna, C.A., Meersman, R. (eds.) OTM 2019. LNCS, vol. 11877, pp. 127–145. Springer, Cham (2019). https://doi.org/10.1007/978-3-030-33246-4_8

    Chapter  Google Scholar 

  17. Esiefarienrhe, B.M., Omolewa, I.D.: Application of process mining to medical billing using L\(*\) life cycle model. In: ICECET, IEEE (2021)

    Google Scholar 

  18. Goel, K., Leemans, S.J.J., Wynn, M.T., ter Hofstede, A.H.M., Barnes, J.: Improving PhD student journeys: insights from an Australian higher education institution. In: BPM Ind. Forum, pp. 27–38. CEUR-WS.org (2021)

    Google Scholar 

  19. Grisold, T., Mendling, J., Otto, M., Brocke, J.V.: Adoption, use and management of process mining in practice. Bus. Process Manag. J. (2020)

    Google Scholar 

  20. Hammer, M., Champy, J.: Reengineering the corporation: a manifesto for business revolution. HarperCollins, Collins Bus. Essent. (2009)

    Google Scholar 

  21. Huang, C., Cai, H., Li, Y., Du, J., Bu, F., Jiang, L.: A process mining based service composition approach for mobile information systems. In: MIS, pp. 1–13 (2017)

    Google Scholar 

  22. Jans, M., Hosseinpour, M.: How active learning and process mining can act as continuous auditing catalyst. IJAIS 32, 44–58 (2019)

    Google Scholar 

  23. Kudo, M., Nogayama, T., Ishida, A., Abe, M.: Business process analysis and real-world application scenarios. In: SITIS, IEEE (2013)

    Google Scholar 

  24. Lauer, T.: Success Factor Person: Right Leadership in Change. In: Change Management, pp. 83–106. Springer, Heidelberg (2021). https://doi.org/10.1007/978-3-662-62187-5_6

    Chapter  Google Scholar 

  25. Lee, C., Ho, G., Choy, K., Pang, G.: A RFID-based recursive process mining system for quality assurance in the garment industry. In: IJPR, pp. 4216–4238 (2013)

    Google Scholar 

  26. Leemans, M., van der Aalst, W.M.P., van den Brand, M.G.J., Schiffelers, R.R.H., Lensink, L.: Software process analysis methodology – a methodology based on lessons learned in embracing legacy software. In: ICSME, IEEE (2018)

    Google Scholar 

  27. Mahendrawathi, E., Zayin, S.O., Pamungkas, F.J.: ERP post implementation review with process mining: a case of procurement process. PCS 124, 216–223 (2017)

    Google Scholar 

  28. Park, G., Aalst, W.: Action-oriented process mining: bridging the gap between insights and actions. Progress Artif. Intell. 1–22 (2022). https://doi.org/10.1007/s13748-022-00281-7

  29. Park, G., van der Aalst, W.M.P.: A general framework for action-oriented process mining. In: Del Río Ortega, A., Leopold, H., Santoro, F.M. (eds.) BPM 2020. LNBIP, vol. 397, pp. 206–218. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-66498-5_16

    Chapter  Google Scholar 

  30. Partington, A., Wynn, M., Suriadi, S., Ouyang, C., Karnon, J.: Process mining for clinical processes. In: TMIS, pp. 1–18 (2015)

    Google Scholar 

  31. Polyvyanyy, A., Pika, A., Wynn, M.T., ter Hofstede, A.H.: A systematic approach for discovering causal dependencies between observations and incidents in the health and safety domain. Saf. Sci. 18, 345–354 (2019)

    Article  Google Scholar 

  32. Ramires, F., Sampaio, P.: Process mining and lean six sigma: a novel approach to analyze the supply chain quality of a hospital. In: IJLSS, pp. 594–621 (2021)

    Google Scholar 

  33. Reinkemeyer, L.: Process mining in action. In: Process mining in action: principles, use Cases and outlook (2020)

    Google Scholar 

  34. Robbins, S., Judge, T.: Organizational Behavior. Pearson Edu. (2016)

    Google Scholar 

  35. Rubin, V.A., Mitsyuk, A.A., Lomazova, I.A., van der Aalst, W.M.P.: Process mining can be applied to software too! In: ESEM, ACM (2014)

    Google Scholar 

  36. Saldana, J.: The Coding Manual for Qualitative Researchers. SAGE, Los Angeles (2015)

    Google Scholar 

  37. dos Santos Garcia, C., et al.: Process mining techniques and applications - a systematic mapping study. In: ESA, pp. 260–295 (2019)

    Google Scholar 

  38. Stuit, M., Wortmann, H.: Discovery and analysis of e-mail-driven business processes. Inf. Syst. 37, 142–168 (2012)

    Article  Google Scholar 

  39. Tawakkal, I., Kurniati, A.P., Wisudiawan, G.A.A.: Implementing heuristic miner for information system audit based on DSS01 COBIT5. In: IC3INA, IEEE (2016)

    Google Scholar 

  40. Trinkenreich, B., Santos, G., Confort, V., Santoro, F.: Toward using business process intelligence to support incident management metrics selection and service improvement. In: SEKE, KSI (2015)

    Google Scholar 

  41. Trkman, P.: The critical success factors of business process management. Int. J. Inf. Manage. 30, 125–134 (2010)

    Article  Google Scholar 

  42. van der Aalst, W.: Process mining: discovery, conformance and enhancement of business processes. Springer (2011)

    Google Scholar 

  43. Weerdt, J.D., Schupp, A., Vanderloock, A., Baesens, B.: Process mining for the multi-faceted analysis of business processes - a case study in a financial services organization. Comput. Ind. 64, 57–67 (2013)

    Article  Google Scholar 

  44. Zerbino, P., Aloini, D., Dulmin, R., Mininno, V.: Towards analytics-enabled efficiency improvements in maritime transportation: a case study in a mediterranean port. Sustainability 11, 4473 (2019)

    Article  Google Scholar 

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Acknowledgements

Part of this research was funded by NWO (Netherlands Organisation for Scientific Research) project number 16672.

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Correspondence to Vinicius Stein Dani .

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Stein Dani, V., Leopold, H., van der Werf, J.M.E.M., Reijers, H.A. (2024). Progressing from Process Mining Insights to Process Improvement: Challenges and Recommendations. In: Proper, H.A., Pufahl, L., Karastoyanova, D., van Sinderen, M., Moreira, J. (eds) Enterprise Design, Operations, and Computing. EDOC 2023. Lecture Notes in Computer Science, vol 14367. Springer, Cham. https://doi.org/10.1007/978-3-031-46587-1_9

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  • DOI: https://doi.org/10.1007/978-3-031-46587-1_9

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