Understanding Production Chain Business Process Using Process Mining: A Case Study in the Manufacturing Scenario

  • Alessandro BettacchiEmail author
  • Alberto Polzonetti
  • Barbara Re
Conference paper
Part of the Lecture Notes in Business Information Processing book series (LNBIP, volume 249)


Due to the continuous market change the enterprises need to react fast. To do that a better understanding of the way to work is needed. Indeed this was a real need of a manufacturing enterprise working in the production of coffee machines and selling them all over the world. In this paper, we present the experience made in the application of process mining techniques on a rich set of data that such enterprise collected during the last six years. We compare five mining algorithms, such as: \(\alpha \)-algorithm, Heuristics Miner, Integer Linear Programming Miner, Inductive Miner, Evolutionary Tree Miner. We evaluated algorithms according to specific quality criteria: fitness, precision, generalization and simplicity. Even if comparison studies are already available in the literature we check them according to our working context. We conclude that the Inductive Miner algorithm is especially suited for discovering production chain processes in the context under study. The application of process mining gives the enterprise a comprehensive picture of the internal process organization. Resulting models were used by the company with successful results to motivate the discussion on the need of developing a flexible production chain.


Process mining Process discovery Business process ProM Framework Mining algorithm Production chain 



We thank Nuova Simonelli and e-Lios for the fruitful collaboration in the project. In particular, we are indebted to Nuova Simonelli President Nando Ottavi for its support and Mauro Parrini who helped us in preparing and conducting this research.


  1. 1.
    Buijs, J., van Dongen, B., van der Aalst, W.: A genetic algorithm for discovering process trees. In: 2012 IEEE Congress on Evolutionary Computation (CEC), pp. 1–8. IEEE, June 2012Google Scholar
  2. 2.
    de Medeiros, A., Weijters, A., van der Aalst, W.M.: Genetic process mining: an experimental evaluation. Data Min. Knowl. Discov. 14(2), 245–304 (2007)CrossRefGoogle Scholar
  3. 3.
    De Weerdt, J., De Backer, M., Vanthienen, J., Baesens, B.: A multi-dimensional quality assessment of state-of-the-art process discovery algorithms using real-life event logs. Inf. Syst. 37(7), 654–676 (2012)CrossRefGoogle Scholar
  4. 4.
    Günther, C.W., Verbeek, E.: XES Standard Definition version 2.0 (2014)Google Scholar
  5. 5.
    van der Aalst, W., et al.: Process mining manifesto. In: Daniel, F., Barkaoui, K., Dustdar, S. (eds.) Business Process Management Workshops. LNBIP, vol. 99, pp. 169–194. Springer, Heidelberg (2012)CrossRefGoogle Scholar
  6. 6.
    Leemans, S.J.J., Fahland, D., van der Aalst, W.M.P.: Discovering block-structured process models from event logs - a constructive approach. In: Colom, J.-M., Desel, J. (eds.) PETRI NETS 2013. LNCS, vol. 7927, pp. 311–329. Springer, Heidelberg (2013)CrossRefGoogle Scholar
  7. 7.
    Leemans, S.J., Fahland, D., van der Aalst, W.M.: Discovering block-structured process models from event logs containing infrequent behaviour. In: Lohmann, N., Song, M., Wohed, P. (eds.) Business Process Management Workshops. LNBIP, vol. 171, pp. 66–78. Springer, Heidelberg (2014)CrossRefGoogle Scholar
  8. 8.
    Lorenz, R., Mauser, S., Juhás, G., How to synthesize nets from languages: a survey. In: Proceedings of the Winter Simulation Conference (WSC) 2007, WSC 2007, pp. 637–647, Piscataway, NJ, USA. IEEE Press (2007)Google Scholar
  9. 9.
    Mendling, J.: Metrics for Process Models: Empirical Foundations of Verification, Error Prediction, and Guidelines for Correctness. LNBIP, vol. 6. Springer, Heidelberg (2008)Google Scholar
  10. 10.
    Mendling, J., Reijers, H.A., Cardoso, J.: What makes process models understandable? In: Alonso, G., Dadam, P., Rosemann, M. (eds.) BPM 2007. LNCS, vol. 4714, pp. 48–63. Springer, Heidelberg (2007)CrossRefGoogle Scholar
  11. 11.
    OMG. Business Process Modeling Notation (BPMN)Google Scholar
  12. 12.
    Reijers, H., Mendling, J.: A study into the factors that influence the understandability of business process models. IEEE Trans. Syst. Man Cybern. Part A 41(3), 449–462 (2011)CrossRefGoogle Scholar
  13. 13.
    Rolón, E., Cardoso, J., García, F., Ruiz, F., Piattini, M.: Analysis and validation of control-flow complexity measures with BPMN process models. In: Halpin, T., Krogstie, J., Nurcan, S., Proper, E., Schmidt, R., Soffer, P., Ukor, R. (eds.) Enterprise, Business-Process and Information Systems Modeling. LNBIP, vol. 29, pp. 58–70. Springer, Heidelberg (2009)CrossRefGoogle Scholar
  14. 14.
    van der Aalst, W.M.: Process Mining: Discovery, Conformance and Enhancement of Business Processes. Springer, Heidelberg (2011)CrossRefGoogle Scholar
  15. 15.
    van der Aalst, W.M.: Processes, no knowledge without : process mining as a tool to find out what people and organizations really do. In: Fred, A., Filipe, J., Dietz, J., Aveiro, D., Liu, K. (eds.) Proceedings of the International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management (IC3K 2014), pp. 11–16, Rome (2014)Google Scholar
  16. 16.
    van der Aalst, W.M., van Hee, K.M., ter Hofstede, A.H., Sidorova, N., Verbeek, H., Voorhoeve, M., Wynn, M.: Soundness of workflow nets: classification, decidability, and analysis. Formal Aspects Comput. 23(3), 333–363 (2011)CrossRefGoogle Scholar
  17. 17.
    van der Aalst, W.M., Weijters, T., Maruster, L.: Workflow mining: discovering process models from event logs. IEEE Trans. Knowl. Data Eng. 16(9), 1128–1142 (2004)CrossRefGoogle Scholar
  18. 18.
    van der Werf, J.M.E., van Dongen, B.F., Hurkens, C.A., Serebrenik, A.: Process discovery using integer linear programming. Fundamenta Informaticae 94(3–4), 387–412 (2009)Google Scholar
  19. 19.
    van Dongen, B.F., de Medeiros, A., Verbeek, H.M.W., Weijters, A., van der Aalst, W.M.: The ProM framework: a new era in process mining tool support. In: Ciardo, G., Darondeau, P. (eds.) Application and Theory of Petri Nets 2005. LNCS, vol. 3536, pp. 444–454. Springer, Heidelberg (2005)CrossRefGoogle Scholar
  20. 20.
    Weijters, A., van der Aalst, W.M., de Medeiros, A.: Process Mining with the Heuristics Miner Algorithm (2006)Google Scholar

Copyright information

© Springer International Publishing Switzerland 2016

Authors and Affiliations

  • Alessandro Bettacchi
    • 1
    Email author
  • Alberto Polzonetti
    • 1
  • Barbara Re
    • 1
  1. 1.Computer Science DivisionUniversity of CamerinoCamerinoItaly

Personalised recommendations