Advantages of Application of Process Mining and Agent-Based Systems in Business Domain

Conference paper
Part of the Smart Innovation, Systems and Technologies book series (SIST, volume 96)


Businesses and business decisions are getting driven by the information gained from the data more and more nowadays. The number of businesses supporting and managing their processes through the use of information systems and new technologies is growing every day. Even though, there is still a lot of rigidity in the implementation of new technologies. There is a great potential for the use of two of so far not so common disciplines in a business domain, which complement each other. That are process mining and multi-agent systems. Thus, in this paper, we are going to demonstrate the possible utilization of both process mining and multi-agent approaches in business domain. To demonstrate it, we use multi-agent simulator of trading company called MAREA. We analyzed implemented company model with the use of process mining. Process mining was used in two different ways. Firstly, to validate the workflow of the process model. Secondly, to analyze bottlenecks in company’s business processes and the impact of marketing campaigns on these business processes.


Process mining MAREA Multi-agent systems Business Model Business process 



The work was supported by the SGS project of Silesian University in Opava, Czechia.


  1. 1.
    Kollár, I., Král, P., Laco, P. 2016. A few notes on deployment of supervised corporate financial distress prediction models in small enterprises. In: Conference Proceedings of 19th International Scientific Conference “Applications of Mathematics and Statistics in Economics”, pp. 204–213. Univerzita Mateja Bela: Banská Bystrica (2015)Google Scholar
  2. 2.
    van der Aalst, W.M.P., Dumas, M., Ouyang, C., Rozinat, A., Verbeek, E.: Conformance checking of service behavior. ACM Trans. Internet Technol. 8(13), 1–30 (2008)CrossRefGoogle Scholar
  3. 3.
    Medeiros, A.K.A. de, Aalst, W.M.P. van der, Weijters, A.J.M.M.: Workflow mining: current status and future directions. In: On the Move to Meaningful Internet Systems 2003: CoopIS, DOA, and ODBASE. LNCS, vol. 2888, pp. 389–406. Springer, New York (2003)Google Scholar
  4. 4.
    van der Aalst, W.M.P.: Extracting Event Data from Databases to Unleash Process Mining. In: Brocke, J. vom, Schmiedel, T. (eds.) BPM - Driving Innovation in a Digital World, Management for Professionals, pp. 105–128. Springer International Publishing (2015)Google Scholar
  5. 5.
    Runje, B., Krstić Vukelja, E., Stepanić, J.: Agent-based simulation of measuring the quality of services. Tech. Gaz. 22(6), 1561–1566 (2015)Google Scholar
  6. 6.
    Siebers, P., Aickelin, U., Celia, H., Clegg, C.: A multi-agent simulation of retail management practices. Accessed 14 May 2018
  7. 7.
    Terano, T.: Beyond the KISS principle for agent-based social simulation. J. Soc. Inf. 1(1), 175–187 (2008)Google Scholar
  8. 8.
    Sun, J., Tang, J., Fu, W., Wu, B.: Hybrid modeling and empirical analysis of automobile supply chain network. Phys. A 473, 377–389 (2017)CrossRefGoogle Scholar
  9. 9.
    North, M.J., Macal, C.M., Aubin, J.S., Thimmapuram, P., Bragen, M., Hahn, J., Karr, J., Brigham, N., Lacy, M.E., Hampton, D.: Multiscale agent-based consumer market modeling. Complexity 15(5), 37–47 (2010)Google Scholar
  10. 10.
    Li, G., Shi, J.: Agent-based modeling for trading wind power with uncertainty in the day-ahead wholesale electricity markets of single-sided auctions. Appl. Energy 99, 13–22 (2012)CrossRefGoogle Scholar
  11. 11.
    Wall, F.: Agent-based modeling in managerial science: an illustrative survey and study. RMS 10(1), 135–193 (2014)CrossRefGoogle Scholar
  12. 12.
    Twomey, P., Cadman, R.: Agent-based modelling of customer behaviour in the telecoms and media markets. Info 4(1), 56–63 (2002)CrossRefGoogle Scholar
  13. 13.
    Sandita, A.V., Popirlan, C.I.: Developing a multi-agent system in JADE for information management in educational competence domains. Procedia Econ. Financ. 23, 478–486 (2015)CrossRefGoogle Scholar
  14. 14.
    Vymětal, D., Ježek, F.: Demand function and its role in a business simulator. Munich Personal RePEc Archive, 54716 (2014)Google Scholar

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Authors and Affiliations

  1. 1.School of Business Administration in Karvina, Department of Business Economics and ManagementSilesian University in OpavaKarvináCzech Republic

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