A Framework Supporting Supply Chain Complexity and Confidentiality Using Process Mining and Auto Identification Technology

  • Zineb LamghariEmail author
  • Maryam Radgui
  • Rajaa Saidi
  • Moulay Driss Rahmani
Conference paper
Part of the Learning and Analytics in Intelligent Systems book series (LAIS, volume 7)


Supply chain is a network between a company and its suppliers to produce and distribute a product to the final customer. This network includes different activities, people, entities, etc. The interaction between these elements provides a cross-organization Business Process. It has mainly treated with process mining techniques, to handle the resulted process instances as event logs. These events are obtained by implementing the auto identification technology within the supply chain related to materials or personal. By doing so, the provided events are not simply presented, they emerged more challenges like complexity and data confidentiality. Therefore, in this work we develop a descriptive framework, based on a literature study, to answer supply chain challenges related to the process mining field. This is done, by implementing process mining within the supply chain, using auto identification technology and taking into consideration recent challenges related to cross-organization Business Process.


Process mining Cross-organizational Supply chain Complexity Confidentiality Auto Identification Technology Alpha-T Encryption Cross-organization Business Process 



This work was supported by the National Center for Scientific and Technical Research (CNRST) in Rabat, Morocco.


  1. 1.
    Hugos, M.H.: Essentials of Supply Chain Management. Wiley, Hoboken (2018)CrossRefGoogle Scholar
  2. 2.
    Jamaludin, Z., Huong, C.Y., Abdullah, L., Nordin, M.H., Abdullah, M.F., Haron, R., Jalal, K.B.A.: Automated tracking system using RFID for sustainable management of material handling in an automobile parts manufacturer. J. Telecommun. Electron. Comput. Eng. (JTEC) 10(1–7), 35-40 (2018)Google Scholar
  3. 3.
    Van der Aalst, W.: Process Mining: Data Science in Action. 2nd edn. Springer (2016)Google Scholar
  4. 4.
    Van der Aalst, W., Weijters, T., Maruster, L.: Workflow mining: discovering process models from event logs. IEEE Trans. Knowl. Data Eng. 16(9), 1128–1142 (2004)CrossRefGoogle Scholar
  5. 5.
    Niederman, F., Mathieu, R.G., Morley, R., Kwon, I.W.: Examining RFID applications in supply chain management. Commun. ACM 50(7), 92–101 (2007)CrossRefGoogle Scholar
  6. 6.
    Van der Aalst, W., Reijers, H.A., Weijters, A.J., Van Dongen, B.F., De Medeiros, A.A., Song, M., Verbeek, H.M.W.: Business process mining: An industrial application. Inf. Syst. 32(5), 713–732 (2007)CrossRefGoogle Scholar
  7. 7.
    Van der Aalst, W., Bichler, M., Heinzl, A.: Responsible data science. Bus. Inf. Syst. Eng. 59(5), 311–313 (2017)CrossRefGoogle Scholar
  8. 8.
    Kang, Y.S., Lee, K., Lee, Y.H., Chung, K.Y.: RFID-based supply chain process mining for imported beef. Korean J. Food Sci. Anim. Resour. 33(4), 463–473 (2013)CrossRefGoogle Scholar
  9. 9.
    Glaschke, C., Gronau, N., Bender, B.: Cross-system process mining using RFID technology, pp. 179–186. (2016).
  10. 10.
    Gerke, K., Claus, A., Mendling, J.: Process mining of RFID-based supply chains. In: 2009 IEEE Conference on Commerce and Enterprise Computing. IEEE, pp. 285–292 (2009)Google Scholar
  11. 11.
    Van der Aalst, W., et al.: Process mining manifesto. In: Daniel, F., Barkaoui, K., Dustdar, S. (eds.) Business Process Management Workshops. BPM 2011. Lecture Notes in Business Information Processing, vol 99. Springer, Heidelberg (2011)Google Scholar
  12. 12.
    Kalenkova, A., Lomazova, I.A., Van der Aalst, W.: Process model discovery: a method based on transition system decomposition. In: International Conference on Applications and Theory of Petri Nets and Concurrency, LNCS, pp. 71–90. Springer, Heidelberg (2014)CrossRefGoogle Scholar
  13. 13.
    Van der Aalst, W.: A general divide and conquer approach for process mining, Ganzha. In: Federated Conference on Computer Science and Information Systems, pp. 1–10 (2013)Google Scholar
  14. 14.
    Munoz-Gama, J., Carmona, J., Van der Aalst, W.: Single-entry single-exit decomposed conformance checking. Inf. Syst. 46, 102–122 (2014)CrossRefGoogle Scholar
  15. 15.
    Van der Aalst, W.: Process cubes: slicing, dicing, rolling up and drilling down event data for process mining. In: Asia Pacific Conference on Business Process Management, Lecture Notes in Business Information Processing, vol. 3159, pp. 1–22. Springer (2013)Google Scholar
  16. 16.
    Vogelgesang, T., Appelrath, H.J.: Multidimensional process mining with PMCube explore. In: Proceedings of the BPM Demo Session 2015 Co-located with the 13th International Conference on Business Process Management, Innsbruck, Austria, pp. 90–94 (2015)Google Scholar
  17. 17.
    Verbeek, H.M.W., Van der Aalst, W.: Merging alignments for decomposed replay. In: Application and Theory of Petri Nets and Concurrency, PETRI NETS, Lecture Notes in Computer Science, vol. 9698. Springer, Cham (2016)CrossRefGoogle Scholar
  18. 18.
    Munoz-Gama, J., Carmona, J., Van der Aalst, W.: Conformance checking in the large: partitioning and topology. In: International Conference on Business Process Management. Lecture Notes in Computer Science, vol. 8094, pp. 130–145 (2013)Google Scholar
  19. 19.
    Hompes, B., Verbeek, E., Van der Aalst, W.M.P.: Finding suitable activity clusters for decomposed process discovery. In: Proceedings of the 4th International Symposium on Data-driven Process Discovery and Analysis, LN in Business Information Processing, pp. 32–57. Springer (2014)Google Scholar
  20. 20.
    Buijs, J.C.A.M., Dongen, B.F., Van der Aalst, W.: Towards cross-organisational process mining in collections of process models and their executions. In: Business Process Management Workshops, pp. 2–13. Springer, Heidelberg (2012)CrossRefGoogle Scholar
  21. 21.
    Irshad, H., Shafiq, B., Vaidya, J., Bashir, M.A., Shamail, S., Adam, N.: Preserving privacy in collaborative business process composition. In: 2015 12th International Joint Conference on e-Business and Telecommunications, vol. 4, pp. 112–123. IEEE (2015)Google Scholar
  22. 22.
    Burattin, A., Conti, M., Turato, D.: Toward an anonymous process mining. In: Proceedings of the 3rd International Conference on Future Internet of Things and Cloud (FiCloud), pp. 58–63 (2015)Google Scholar
  23. 23.
    Liu, C., Duan, H., Zeng, Q., Zhou, M., Lu, F., Cheng, J.: Towards comprehensive support for privacy preservation cross-organization business process mining. IEEE Trans. Serv. Comput. 12, 639–653 (2016)CrossRefGoogle Scholar
  24. 24.
    Van der Aalst, W., Van Dongen, B.F., Christian, G.W.: ProM: the process mining toolkit. In: BPM Demos, vol. 489, no. 31, p. 2 (2009)Google Scholar
  25. 25.
    Li, J., Liu, D., Yang, B.: Process mining: extending α-algorithm to mine duplicate tasks in process logs. In: Advances in Web and Network Technologies, and Information Management, vol. 4537, pp. 396–407 (2007)Google Scholar
  26. 26.
    Van der Aalst, W., Medeiros, A., Weijters, A.: Genetic process mining. In: Applications and Theory of Petri Nets, Lecture Notes in Computer Science, vol. 3536 (2005)Google Scholar
  27. 27.
    Goedertier, S., Martens, D., Vanthienen, J., Baesens, B.: Robust process discovery with artificial negative events. J. Mach. Learn. Res. 10, 1305–1340 (2009)MathSciNetzbMATHGoogle Scholar
  28. 28.
    He, Z., Gu, F., Zhao, C., Liu, X., Wu, J., Wang, J.: Conditional discriminative pattern mining: concepts and algorithms. Inf. Sci. 375, 1–15 (2017)CrossRefGoogle Scholar
  29. 29.
    Sarno, R., Dewandono, R.D., Ahmad, T., Naufal, M.F., Sinaga, F.: Hybrid association rule learning and process mining for fraud detection. IAENG Int. J. Comput. Sci. 42(2) (2015)Google Scholar
  30. 30.
    Wang, J., Wong, R.K., Ding, J., Guo, Q., Wen, L.: Efficient selection of process mining algorithms. IEEE Trans. Serv. Comput. 6(4), 484–496 (2012)CrossRefGoogle Scholar
  31. 31.
    Van der Aalst, W.: Responsible data science: using event data in a “people friendly” manner. In: International Conference on Enterprise Information Systems, pp. 3–28. Springer (2016)Google Scholar
  32. 32.
    Hermawan, S.R.: A more efficient deterministic algorithm in process model discovery. Int. J. Innov. Comput. Inf. Control 14(3), 971–995 (2018)Google Scholar
  33. 33.
    Yan, Z., Sun, B., Chen, Y., Wen, L., Hu, L., Wang, J., Wang, L.: Decomposed and parallel process discovery: a framework and application. Future Gener. Comput. Syst. 98, 392–405 (2019). Scholar
  34. 34.
    Rafiei, M., von Waldthausen, L., Aalst, W.: Ensuring confidentiality in process mining (2018)Google Scholar
  35. 35.
    Bae, H., Seo, Y.: BPM-based integration of supply chain process modeling, executing and monitoring. Int. J. Prod. Res. 45(11), 2545–2566 (2007)CrossRefGoogle Scholar
  36. 36.
    Gold, S., Seuring, S., Beske, P.: Sustainable supply chain management and inter-organizational resources: a literature review. Corp. Soc. Responsib. Environ. Manag. 17(4), 230–245 (2010)Google Scholar

Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  • Zineb Lamghari
    • 1
    Email author
  • Maryam Radgui
    • 1
    • 2
  • Rajaa Saidi
    • 1
    • 2
  • Moulay Driss Rahmani
    • 1
  1. 1.LRIT Associated Unit to CNRST (URAC 29), Rabat IT Center, Faculty of SciencesMohammed V UniversityRabatMorocco
  2. 2.SI2M LaboratoryNational Institute of Statistics and Applied EconomicsRabatMorocco

Personalised recommendations