CONCEPTUAL MODELS OF DECISION-MAKING IN LOGISTICS USING PROCESS MINING
Abstract
The purpose of the study is to develop conceptual models for management decision‑making in logistics systems based on the application of Process Mining methodology. The relevance of the topic is driven by the need to improve the efficiency of logistics operations in the context of increasing supply chain complexity and digital transformation of business processes. Traditional approaches to analyzing logistics processes often rely on theoretical models that do not always reflect the actual state of affairs, whereas process mining allows identifying actual processes based on event logs from information systems. The research methodology is based on a systematic analysis of scientific publications from Scopus and Web of Science databases for the period 2020–2025, synthesis of conceptual models of process analysis and their adaptation to the specifics of logistics activities. The study employs methods of comparative analysis, systematization, and generalization to examine existing approaches to applying Process Mining in logistics. The research results include a developed integrated conceptual model for decision‑making that combines three key components: process discovery, conformance checking, and process enhancement. The proposed model provides a structured approach to analyzing logistics operations based on event log data from ERP systems, warehouse management systems, and transportation systems. The main types of deviations in logistics processes have been identified, and a classification of process mining methods has been developed according to the specifics of logistics tasks. The practical value of the study lies in the possibility of applying the developed conceptual models by logistics companies to optimize warehousing, transportation, and distribution processes, reduce order fulfillment cycle time, increase customer service levels, and reduce operational costs based on analysis of actual data on logistics operations execution. Additionally, the paper outlines an implementation blueprint—from data‑model design and data‑quality policy to tool selection and metric governance—that ensures reproducibility, scalability, and transparency of managerial decisions across multi‑tier logistics networks.
References
Agostinelli S., Maggi F. M., Marrella A., Mecella M. Automated generation of executable RPA scripts from user interface logs. У: Business Process Management: BPM 2021 (Lecture Notes in Computer Science, Vol. 12875). Springer. 2021. С. 116–131. DOI: https://doi.org/10.1007/978-3-030-85469-0_10
van der Aalst W. M. P. Process mining: A 360-degree overview. У: Process Mining Handbook (Lecture Notes in Business Information Processing, Vol. 448). Springer. 2020. С. 3–34. DOI: https://doi.org/10.1007/978-3-031-08848-3_1
Baier T., Mendling J., Weske M. Bridging abstraction layers in process mining. Information Systems. 2021. Вип. 46. С. 123–139. DOI: https://doi.org/10.1016/j.is.2014.04.004
Berti A., van Zelst S. J., van der Aalst W. M. P. Process mining for Python (PM4Py): Bridging the gap between process and data science. У: ICPM Demo Track. 2020. С. 13–16. DOI: https://doi.org/10.48550/arXiv.1905.06169
dos Santos Garcia C., Meincheim A., Faria Junior E. R., Dallagassa M. R., Sato D. M. V., Carvalho D. R., Santos E. A. P., Scalabrin E. E. Process mining techniques and applications: A systematic mapping study. Expert Systems with Applications. 2020. Вип. 133. С. 260–295. DOI: https://doi.org/10.1016/j.eswa.2019.05.003
Dees M., de Leoni M., van der Aalst W. M. P. What if process predictions are not followed? У: Business Process Management: BPM 2021 (Lecture Notes in Computer Science, Vol. 12875). Springer. 2021. С. 61–77. DOI: https://doi.org/10.1007/978-3-030-85469-0_7
Dumas M., La Rosa M., Mendling J., Reijers H. A. Fundamentals of Business Process Management (2nd ed.). Springer. 2020. DOI: https://doi.org/10.1007/978-3-662-56509-4
Fani Sani M., van Zelst S. J., van der Aalst W. M. P. Conformance checking approximation using subset selection and edit distance. У: Advanced Information Systems Engineering: CAiSE 2020 (Lecture Notes in Computer Science, Vol. 12127). Springer. 2020. С. 234–251. DOI: https://doi.org/10.1007/978-3-030-49435-3_15
García Bañuelos L., Dumas M., La Rosa M., De Weerdt J., Ekanayake C. C. Controlled automated discovery of collections of business process models. Information Systems. 2021. Вип. 46. С. 85–101. DOI: https://doi.org/10.1016/j.is.2013.10.001
Goel K., Wuest T., de Lange K., van der Veen E. Process mining in supply chain management: A review. International Journal of Production Research. 2021. Вип. 59, № 16. С. 5184–5207. DOI: https://doi.org/10.1080/00207543.2020.1785896
Greco G., Guzzo A., Pontieri L., Saccà D. Discovering expressive process models by clustering log traces. IEEE Transactions on Knowledge and Data Engineering. 2021. Вип. 18, № 8. С. 1010–1027. DOI: https://doi.org/10.1109/TKDE.2006.123
Hassani M., Siccha S., Richter F., Seidl T. Efficient process discovery from event streams using sequential pattern mining. У: IEEE SSCI—CIDM. 2015. С. 1366–1373. DOI: https://doi.org/10.1109/SSCI.2015.195
Henrique S., De Weerdt J., Coomans T. Process mining for robotic process automation: A case study in customs. У: Business Process Management: BPM 2021 (Lecture Notes in Computer Science, Vol. 12875). Springer. 2021. С. 34–49. DOI: https://doi.org/10.1007/978-3-030-85469-0_5
Hompes B., Buijs J. C. A. M., van der Aalst W. M. P., Dixit P. M., Buurman H. Discovering deviating cases and process variants using trace clustering. У: Benelux Conference on Artificial Intelligence. 2015. С. 5–6. DOI: https://doi.org/10.48550/arXiv.1507.06516
Leemans S. J. J., Fahland D., van der Aalst W. M. P. Scalable process discovery and conformance checking. Software & Systems Modeling. 2020. Вип. 17, № 2. С. 599–631. DOI: https://doi.org/10.1007/s10270-016-0545-x
Liberati A., Altman D. G., Tetzlaff J., Mulrow C., Gøtzsche P. C., Ioannidis J. P. A., Clarke M., Devereaux P. J., Kleijnen J., Moher D. The PRISMA statement for reporting systematic reviews and meta-analyses of studies. BMJ. 2021. Вип. 339. P. b2535. DOI: https://doi.org/10.1136/bmj.b2535
Leno V., Polyvyanyy A., Dumas M., La Rosa M., Maggi F. M. Robotic process mining: Vision and challenges. Business & Information Systems Engineering. 2021. Вип. 63, № 3. С. 301–314. DOI: https://doi.org/10.1007/s12599-020-00641-4
Martin N., Depaire B., Caris A. The use of process mining in business process simulation model construction. Business & Information Systems Engineering. 2020. Вип. 58, № 1. С. 73–87. DOI: https://doi.org/10.1007/s12599-015-0410-4
Munoz Gama J., Martin N., Fernandez Llatas C., Johnson O. A., Sepúlveda M., Helm E., Galvez Yanjari V., Rojas E., Martinez Millana A., Aloini D., Amantea I. A., Andrews R., Arias M., Beerepoot I., Burattin A., Capurro D., Carmona J., Castellanos M., та ін. Process mining for healthcare: Characteristics and challenges. Journal of Biomedical Informatics. 2021. Вип. 127. P. 103994. DOI: https://doi.org/10.1016/j.jbi.2022.103994
Nguyen H., Dumas M., La Rosa M., Maggi F. M., Suriadi S. Mining business process stages from event logs. IEEE Transactions on Services Computing. 2020. Вип. 13, № 6. С. 1036–1049. DOI: https://doi.org/10.1109/TSC.2018.2808288
Nolle T., Seeliger A., Mühlhäuser M. BINet: Multivariate business process anomaly detection using deep learning. У: Business Process Management: BPM 2021 (Lecture Notes in Computer Science, Vol. 11675). Springer. 2021. С. 271–287. DOI: https://doi.org/10.1007/978-3-030-26619-6_18
Polato M., Sperduti A., Burattin A., de Leoni M. Time and activity sequence prediction of business process instances. Computing. 2021. Вип. 100, № 9. С. 1005–1031. DOI: https://doi.org/10.1007/s00607-018-0593-x
Schönig S., Rogge Solti A., Cabanillas C., Jablonski S., Mendling J. Efficient and customisable declarative process mining with SQL. У: CAiSE 2016 (Lecture Notes in Computer Science, Vol. 9694). Springer. 2016. С. 290–305. DOI: https://doi.org/10.1007/978-3-319-39696-5_18
Agostinelli S., Maggi F. M., Marrella A., Mecella M. Automated generation of executable RPA scripts from user interface logs. У: Business Process Management: BPM 2021 (Lecture Notes in Computer Science, Vol. 12875). Springer. 2021. PP. 116–131. DOI: https://doi.org/10.1007/978-3-030-85469-0_10
van der Aalst W. M. P. Process mining: A 360-degree overview. У: Process Mining Handbook (Lecture Notes in Business Information Processing, Vol. 448). Springer. 2020. PP. 3–34. DOI: https://doi.org/10.1007/978-3-031-08848-3_1
Baier T., Mendling J., Weske M. Bridging abstraction layers in process mining. Information Systems. 2021. Is. 46. PP. 123–139. DOI: https://doi.org/10.1016/j.is.2014.04.004
Berti A., van Zelst S. J., van der Aalst W. M. P. Process mining for Python (PM4Py): Bridging the gap between process and data science. У: ICPM Demo Track. 2020. PP. 13–16. DOI: https://doi.org/10.48550/arXiv.1905.06169
dos Santos Garcia C., Meincheim A., Faria Junior E. R., Dallagassa M. R., Sato D. M. V., Carvalho D. R., Santos E. A. P., Scalabrin E. E. Process mining techniques and applications: A systematic mapping study. Expert Systems with Applications. 2020. Is. 133. PP. 260–295. DOI: https://doi.org/10.1016/j.eswa.2019.05.003
Dees M., de Leoni M., van der Aalst W. M. P. What if process predictions are not followed? У: Business Process Management: BPM 2021 (Lecture Notes in Computer Science, Vol. 12875). Springer. 2021. PP. 61–77. DOI: https://doi.org/10.1007/978-3-030-85469-0_7
Dumas M., La Rosa M., Mendling J., Reijers H. A. Fundamentals of Business Process Management (2nd ed.). Springer. 2020. DOI: https://doi.org/10.1007/978-3-662-56509-4
Fani Sani M., van Zelst S. J., van der Aalst W. M. P. Conformance checking approximation using subset selection and edit distance. У: Advanced Information Systems Engineering: CAiSE 2020 (Lecture Notes in Computer Science, Vol. 12127). Springer. 2020. PP. 234–251. DOI: https://doi.org/10.1007/978-3-030-49435-3_15
García Bañuelos L., Dumas M., La Rosa M., De Weerdt J., Ekanayake C. C. Controlled automated discovery of collections of business process models. Information Systems. 2021. Is. 46. PP. 85–101. DOI: https://doi.org/10.1016/j.is.2013.10.001
Goel K., Wuest T., de Lange K., van der Veen E. Process mining in supply chain management: A review. International Journal of Production Research. 2021. Is. 59, № 16. PP. 5184–5207. DOI: https://doi.org/10.1080/00207543.2020.1785896
Greco G., Guzzo A., Pontieri L., Saccà D. Discovering expressive process models by clustering log traces. IEEE Transactions on Knowledge and Data Engineering. 2021. Is. 18, № 8. PP. 1010–1027. DOI: https://doi.org/10.1109/TKDE.2006.123
Hassani M., Siccha S., Richter F., Seidl T. Efficient process discovery from event streams using sequential pattern mining. У: IEEE SSCI—CIDM. 2015. PP. 1366–1373. DOI: https://doi.org/10.1109/SSCI.2015.195
Henrique S., De Weerdt J., Coomans T. Process mining for robotic process automation: A case study in customs. У: Business Process Management: BPM 2021 (Lecture Notes in Computer Science, Vol. 12875). Springer. 2021. PP. 34–49. DOI: https://doi.org/10.1007/978-3-030-85469-0_5
Hompes B., Buijs J. C. A. M., van der Aalst W. M. P., Dixit P. M., Buurman H. Discovering deviating cases and process variants using trace clustering. У: Benelux Conference on Artificial Intelligence. 2015. PP. 5–6. DOI: https://doi.org/10.48550/arXiv.1507.06516
Leemans S. J. J., Fahland D., van der Aalst W. M. P. Scalable process discovery and conformance checking. Software & Systems Modeling. 2020. Is. 17, № 2. PP. 599–631. DOI: https://doi.org/10.1007/s10270-016-0545-x
Liberati A., Altman D. G., Tetzlaff J., Mulrow C., Gøtzsche P. C., Ioannidis J. P. A., Clarke M., Devereaux P. J., Kleijnen J., Moher D. The PRISMA statement for reporting systematic reviews and meta-analyses of studies. BMJ. 2021. Is. 339. P. b2535. DOI: https://doi.org/10.1136/bmj.b2535
Leno V., Polyvyanyy A., Dumas M., La Rosa M., Maggi F. M. Robotic process mining: Vision and challenges. Business & Information Systems Engineering. 2021. Is. 63, № 3. PP. 301–314. DOI: https://doi.org/10.1007/s12599-020-00641-4
Martin N., Depaire B., Caris A. The use of process mining in business process simulation model construction. Business & Information Systems Engineering. 2020. Is. 58, № 1. PP. 73–87. DOI: https://doi.org/10.1007/s12599-015-0410-4
Munoz Gama J., Martin N., Fernandez Llatas C., Johnson O. A., Sepúlveda M., Helm E., Galvez Yanjari V., Rojas E., Martinez Millana A., Aloini D., Amantea I. A., Andrews R., Arias M., Beerepoot I., Burattin A., Capurro D., Carmona J., Castellanos M., та ін. Process mining for healthcare: Characteristics and challenges. Journal of Biomedical Informatics. 2021. Is. 127. P. 103994. DOI: https://doi.org/10.1016/j.jbi.2022.103994
Nguyen H., Dumas M., La Rosa M., Maggi F. M., Suriadi S. Mining business process stages from event logs. IEEE Transactions on Services Computing. 2020. Is. 13, № 6. PP. 1036–1049. DOI: https://doi.org/10.1109/TSC.2018.2808288
Nolle T., Seeliger A., Mühlhäuser M. BINet: Multivariate business process anomaly detection using deep learning. У: Business Process Management: BPM 2021 (Lecture Notes in Computer Science, Vol. 11675). Springer. 2021. PP. 271–287. DOI: https://doi.org/10.1007/978-3-030-26619-6_18
Polato M., Sperduti A., Burattin A., de Leoni M. Time and activity sequence prediction of business process instances. Computing. 2021. Is. 100, № 9. PP. 1005–1031. DOI: https://doi.org/10.1007/s00607-018-0593-x
Schönig S., Rogge Solti A., Cabanillas C., Jablonski S., Mendling J. Efficient and customisable declarative process mining with SQL. У: CAiSE 2016 (Lecture Notes in Computer Science, Vol. 9694). Springer. 2016. PP. 290–305. DOI: https://doi.org/10.1007/978-3-319-39696-5_18
