The Functionalities of Cognitive Technology in Management Control System

  • Andrzej Bytniewski
  • Kamal Matouk
  • Anna Chojnacka-Komorowska
  • Marcin HernesEmail author
  • Adam Zawadzki
  • Agata Kozina
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 12034)


Cognitive technologies are an important factor accelerating the development of business and society. They are an artificial intelligence tool with cognitive skills enabling learning through empirical experience acquired through direct interaction with the environment. The application of these technologies in the management control system enriches it with the functions of automatic analysis of real data mapping business processes that are implemented at all levels of management. The aim of this paper is to analyze the functionalities of cognitive technology for the intelligent knowledge processing process by the management control system. To achieve this goal, the following research methods were used: literature analysis, observation of phenomena, deduction, and induction. The main results of researches are conclusions that cognitive technology should be used, among other, for realize following functions: automatic creation of a new strategic and operating budget plan, automatic real-time conversion of budgets in case of a change in the organizational structure of the enterprise, automatic deviations control, interpretation and identification the causes of deviations.


Management control system Artificial intelligence Cognitive technology Integrated management information system Enterprise resource planning Industry 4.0 



“The project is financed by the Ministry of Science and Higher Education in Poland under the programme “Regional Initiative of Excellence” 2019–2022 project number 015/RID/2018/19 total funding amount 10 721 040,00 PLN”.


  1. 1.
    Wildavsky, A.: Budgeting and Governing. Routledge, Abingdon (2017)CrossRefGoogle Scholar
  2. 2.
    Stilley, K.M., Inman, J.J., Wakefield, K.L.: Planning to make unplanned purchases? The role of in-store slack in budget deviation. J. Consum. Res. 37(2), 264–278 (2010)CrossRefGoogle Scholar
  3. 3.
    Hernes, M.: A cognitive integrated management support system for enterprises. In: Hwang, D., Jung, J.J., Nguyen, N.-T. (eds.) ICCCI 2014. LNCS (LNAI), vol. 8733, pp. 252–261. Springer, Cham (2014). Scholar
  4. 4.
  5. 5.
    Hernes, M., Bytniewski, A.: Knowledge representation of cognitive agents processing the economy events. In: Nguyen, N.T., Hoang, D.H., Hong, T.-P., Pham, H., Trawiński, B. (eds.) ACIIDS 2018. LNCS (LNAI), vol. 10751, pp. 392–401. Springer, Cham (2018). Scholar
  6. 6.
    Hernes, M., Bytniewski, A.: Knowledge integration in a manufacturing planning module of a cognitive integrated management information system. In: Nguyen, N.T., Papadopoulos, G.A., Jędrzejowicz, P., Trawiński, B., Vossen, G. (eds.) ICCCI 2017. LNCS (LNAI), vol. 10448, pp. 34–43. Springer, Cham (2017). Scholar
  7. 7.
    Wu, D.D., Chen, S.H., Olson, D.: Business intelligence in risk management: some recent progresses. Inf. Sci. 256, 1–7 (2014)CrossRefGoogle Scholar
  8. 8.
    Velagapalli, V., Bommareddy, S.S.R., Premalatha, V.: Application of lean techniques, enterprise, resource planning and artificial intelligence in construction project management. In: International Conference on Advances in Civil Engineering (ICACE 2019), 21–23 March 2019. University Vijayawada, A.P., India, vol. 7 (2019). International Journal of Recent Technology and Engineering (IJRTE). ISSN 2277-3878Google Scholar
  9. 9.
    Ryan, J., Snyder, Ch.: Intelligent agents and information resource management. In: Proceedings of the Tenth Americas Conference on Information Systems, New York, pp. 4627–4630 (2004)Google Scholar
  10. 10.
    Gupta, J.N.D., Forgione, G.A., Mora T., M. (eds.): Intelligent Decision-Making Support Systems. Foundations, Applications and Challenges. Springer, London (2006). Scholar
  11. 11.
    Ryfors, D., Wallin, M., Truve, T.: Swedish manufacturing SMEs readiness for Industry 4.0. What factors influence an implementation of Artificial Intelligence and how ready are manufacturing SMEs in Sweden? Jonkoping University (2019)Google Scholar
  12. 12.
    Martinez-Lopez, F.J., Casillas, J.: Artifical intelligence-based systems applied in industrial marketing: An historical overview, current and future insights. Ind. Mark. Manage. 42(4), 489–495 (2018)CrossRefGoogle Scholar
  13. 13.
    Rikhardson, P., Yigitbasioglu, O.: Business intelligence & analytics in management accounting research: status and future focus. Int. J. Acc. Inf. Syst. 29, 37–58 (2018)CrossRefGoogle Scholar
  14. 14.
    Zebec, A.: Cognitive BPM: Business Process Automation nad Innovation with Artificial Intelligence. Accessed 10 Nov 2019
  15. 15.
    Gandon, F.: Distributed Artificial Intelligence and Knowledge Management: Ontologies and Multi Agent Systems for a Corporate Semantic Web, Universite Nice Sophia Antipolis, 2002 (tel-00378201). Accessed 10 Nov 2019
  16. 16.
    Lee, M.C., Cheng, J.F.: Development multi-enterprise collaborative enterprise intelligent decision support system. J. Converg. Inf. Technol. 2(2), 64 (2007)Google Scholar
  17. 17.
    Kishore, R., Zhang, H., Ramesh, R.: Enterprise integration using the agent paradigm: foundations of multi-agent-based integrative business information systems. Decis. Support Syst. 42(1), 48–78 (2006)CrossRefGoogle Scholar
  18. 18.
    Roland Berger Trend Compedium 2030, Megatrend 5 Dynamic technology & innovation (2017)Google Scholar
  19. 19.
    Bytniewski, A. (ed.): An Architecture of Integrated Management System. Wydawnictwo Uniwersytetu Ekonomicznego we Wrocławiu, Wrocław (2015). (in Polish)Google Scholar
  20. 20.
    Chojnacka-Komorowska, A., Hernes, M.: Knowledge representation in controlling sub-system. In: Ganzha, M., Maciaszek, L., Paprzycki, M. (eds.) Position Papers of the 2015 Federated Conference on Computer Science and Information Systems, vol. 6, pp. 187–193. Polskie Towarzystwo Informatyczne (2015)Google Scholar
  21. 21.
    Goertzel, B.: OpenCogBot – achieving generally intelligent virtual agent control and humanoid robotics via cognitive synergy. In: Proceedings of ICAI 2010, Beijing (2010)Google Scholar
  22. 22.
    Kollmann, S., Siafara, L.C., Schaat, S., Wendt, A.: Towards a cognitive multi-agent system for building control. Procedia Comput. Sci. 88, 191–197 (2016)CrossRefGoogle Scholar
  23. 23.
    Snaider, J., McCall, R., Franklin, S.: The LIDA framework as a general tool for AGI. In: The Fourth Conference on Artificial General Intelligence (2011)Google Scholar
  24. 24.
    Lasota, T., Telec, Z., Trawiński, B., Trawiński, K.: a multi-agent system to assist with real estate appraisals using bagging ensembles. In: Nguyen, N.T., Kowalczyk, R., Chen, S.-M. (eds.) ICCCI 2009. LNCS (LNAI), vol. 5796, pp. 813–824. Springer, Heidelberg (2009). Scholar
  25. 25.
  26. 26.
  27. 27.
    Hernes, M., Bytniewski, A.: Towards big management. In: Król, D., Nguyen, N.T., Shirai, K. (eds.) ACIIDS 2017. SCI, vol. 710, pp. 197–209. Springer, Cham (2017). Scholar
  28. 28.
    Park, N.: Secure UHF/HF dual-band RFID: strategic framework approaches and application solutions. In: Jędrzejowicz, P., Nguyen, N.T., Hoang, K. (eds.) ICCCI 2011. LNCS (LNAI), vol. 6922, pp. 488–496. Springer, Heidelberg (2011). Scholar
  29. 29.
    Hernes, M.: Performance evaluation of the customer relationship management agent’s in a cognitive integrated management support system. In: Nguyen, N.T. (ed.) Transactions on Computational Collective Intelligence XVIII. LNCS, vol. 9240, pp. 86–104. Springer, Heidelberg (2015). Scholar
  30. 30.
    Nowosielski, K.: Performance improvement of controlling processes: results of theoretical and empirical researches. Przegląd Organizacji 5, 50–58 (2014)CrossRefGoogle Scholar

Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  1. 1.Wroclaw University of Economics and BusinessWroclawPoland

Personalised recommendations