Skip to main content
SpringerLink
Log in

Using Artificial Intelligence in Company Management

  • Conference paper
  • First Online:
Digital Age: Chances, Challenges and Future (ISCDTE 2019)

Part of the book series: Lecture Notes in Networks and Systems ((LNNS,volume 84))

Abstract

Thanks to technological progress, artificial intelligence is currently used in different areas of our lives. The use of artificial intelligence in business and finance has a promising future. Artificial intelligence is inspired by the behavior of biological patterns, having also the ability to learn and then capture these strongly non-linear dependencies. The advantage of artificial neural networks consists in their capability of working with big data, in the precision of their results or easier use of the obtained neural network. The objective of this contribution is to carry out systematic literary research of the most renowned scientific resources and find out whether it is possible to use artificial intelligence in practice, in company management. After a clearly defined process of selecting the appropriate scientific outcomes, these studies are explored and conclusions are made. A total of 31 publications fulfilled the criteria. The publications more or less agree on the practical applicability of artificial intelligence in company management.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 169.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 219.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Similar content being viewed by others

References

  1. Antonescu, M.: Are business leaders prepared to handle the upcoming revolution in business artificial intelligence? Qual. Access Success 19(53), 15–19 (2018)

    Google Scholar 

  2. Arputhamalar, A., Kannan, S.P.: Written correspondence – the foremost channel of information transfer in organisations. Qual. Access Success 17(151), 111–114 (2016)

    Google Scholar 

  3. Bai, S.A.: Artificial intelligence technologies in business and engineering. In: IET Conference Publications, International Conference on Sustainable Energy and Intelligent Systems, pp. 856–859 (2011)

    Google Scholar 

  4. Baryannis, G., Validi, S., Dani, S., Antoniou, G.: Supply chain risk management and artificial intelligence: state of the art and future research directions. Int. J. Prod. Res. 57(7), 2179–2202 (2018)

    Article  Google Scholar 

  5. Beiranvand, V., Abu Bakar, A., Othman, Z.: A comparative survey of three AI techniques (NN, PSO, and GA) in financial domain. In: IEE Proceedings of the 7th International Conference on Computing and Convergence Technology, pp. 332–337 (2012)

    Google Scholar 

  6. Choy, K.L., Lee, W.B., Lo, V.: An intelligent supplier relationship management system for selecting and benchmarking suppliers. Int. J. Technol. Manag. 26(7), 717–742 (2003)

    Article  Google Scholar 

  7. Ferràs-Hernández, X.: The future of management in a world of electronic brains. J. Manag. Inq. 27(2), 260–263 (2017)

    Article  Google Scholar 

  8. Fink, A.: Conducting Research Literature Reviews, 3rd edn. Sage, Los Angeles (2010)

    Google Scholar 

  9. Gallo, P., Gallo, P.J., Timková, V., Šenková, A., Karahuta, M.: Use of dashboards in predicting the development of the company using neural networks in hotel management. Geojournal Tourism Geosites 22(2), 307–316 (2018)

    Google Scholar 

  10. Gao, W., Qi, Q., Dong, L., Liu, C.: Application of artificial intelligence in innovation experiment management system engineering. In: Jing, W., Ning, X., Huiyu, Z. (eds.) Proceedings of the 8th International Conference on Management and Computer Science, pp. 171–175. Atlantis Press, Paris (2018)

    Google Scholar 

  11. Gressley, S., Horák, J., Kováčová, M., Valašková, K., Poliak, M.: Consumer attitudes and behaviors in the technology-driven sharing economy: motivations for participating in collaborative consumption. J. Self-Gov. Manag. Econ. 7(1), 25–30 (2019)

    Article  Google Scholar 

  12. Horák, J.: Using artificial intelligence to analyse businesses in agriculture industry. In: Horák, J. (ed.) SHS Web of Conferences: Innovative Economic Symposium 2018 – Milestones and Trends of World Economy, p. 01005. EDP Sciences, France (2019)

    Article  Google Scholar 

  13. Horák, J., Krulický, T.: Comparison of exponential time series alignment and time series alignment using artificial neural networks by example of prediction of future development of stock prices of a specific company. In: Horák, J. (ed.) SHS Web of Conferences: Innovative Economic Symposium 2018 – Milestones and Trends of World Economy, p. 01006. EDP Sciences, France (2019)

    Google Scholar 

  14. Jia, Q., Guo, Y., Li, R., Li, Y., Chen, Y.: A conceptual artificial intelligence application framework in human resource management. In: Chang, F.K., Li, E.Y., Li, E.Y. (eds.) Proceedings of the International Conference on Electronic Business, pp. 106–114. International Consortium for Electronic Business (2018)

    Google Scholar 

  15. Jones, M.V., Coviello, N., Tang, Y.K.: International entrepreneurship research (1989–2009): a domain ontology and thematic analysis. J. Bus. Ventur. 26(6), 632–659 (2011)

    Article  Google Scholar 

  16. Khalyasmaa, A.I., Dmitriev, S.A., Valiev, R.T.: Grid company risk management system based on adaptive neuro-fuzzy inference. In: IEEE Proceedings of 2017 XX International Conference on Soft Computing and Measurements, pp. 892–895. IEEE, New York (2017)

    Google Scholar 

  17. Klieštik, T.: Models of autoregression conditional heteroskedasticity garch and arch as a tool for modeling the volatility of financial time series. Ekonomicko-manažerské spectrum 7(1), 2–10 (2013)

    Google Scholar 

  18. Kopia, J., Kompalla, A., Ceausu, I.: Theory and practice of integrating management systems with high level structure. Qual.-Access Success 17(155), 52-29 (2016)

    Google Scholar 

  19. Lawrynowicz, A.: Production planning and control with outsourcing using artificial intelligence. Int. J. Serv. Oper. Manag. 3(2), 193–209 (2018)

    Google Scholar 

  20. Marrella, A.: What automated planning can do for business process management. In: Teniente, E., Weidlich, M. (eds.) Business Process Management Workshops, pp. 7–19. Springer, Berlin (2018)

    Chapter  Google Scholar 

  21. Mičieta, B., Staszewska, J., Biňasová, V., Herčko, J.: Adaptive logistics management and optimization through artificial intelligence. Commun. - Sci. Lett. Univ. Žilina 19(2A), 10–14 (2017)

    Google Scholar 

  22. Min, H.: Artificial intelligence in supply chain management: theory and applications. Int. J. Logistics Res. Appl. 13(1), 13–39 (2010)

    Article  Google Scholar 

  23. Nenortaite, J., Butleris, R.: Business rules management improvement through the application of particle swarm optimization algorithm and artificial neural networks. In: Targamadze, A., Butleris, R., Rutkiene, R. (eds.) Information Technologies’ 2008, Proceedings, pp. 84–90. Kaunas University of Technology Press, Kaunas (2008)

    Google Scholar 

  24. Paschek, D., Luminosu, C.T., Draghici, A.: Automated business process management – in times of digital transformation using machine learning or artificial intelligence. In: Bondrea, I., Inta, M., Simion, C. (eds.) MATEC Web of Conferences, vol. 121, p. 04007. EDP Science, France (2017)

    Article  Google Scholar 

  25. Santin, D.: On the approximation of production functions: a comparison of artificial neural networks frontiers and efficiency techniques. Appl. Econ. Lett. 15(8), 597–600 (2008)

    Article  Google Scholar 

  26. Šuleř, P.: Using Kohonen´s neural networks to identify the bankruptcy of enterprises: Case study based on construction companies in South Bohemian region. In: Dvouletý, O., Lukeš, M., Mísař, J. (eds.) Proceedings of the 5th International Conference Innovation Management, Entrepreneurship and Sustainability, pp. 985–995. Oeconomica Publishing House, Prague (2017)

    Google Scholar 

  27. Šustrová, T.: An artificial neural network model for a wholesale company’s order-cycle management. Int. J. Eng. Bus. Manag. 8, 1–6 (2016)

    Article  Google Scholar 

  28. Vella, V., Ng, W.L.: A dynamic fuzzy money management approach for controlling the intraday risk-adjusted performance of AI trading algorithms. Intell. Syst. Acc. Financ. Manag. 22(2), 153–178 (2015)

    Article  Google Scholar 

  29. Vochozka, M., Horák, J.: Comparison of neural networks and regression time series when estimating the copper price development. In: Ashmarina, S., Vochozka, M. (eds.) Contributions to Economics, pp. 169–181. Springer, Heidelberg (2019)

    Google Scholar 

  30. Vochozka, M., Machová, V.: Determination of value drivers for transport companies in the Czech Republic. Nase More 65(4), 197–201 (2018)

    Article  Google Scholar 

  31. Walczak, S.: Artificial neural networks and other AI applications for business management decision support. In: I. Management Association (ed.) Intelligent Systems: Concepts, Methodologies, Tools, and Applications, pp. 2047–2071. IGI Global, Hershey (2018). https://doi.org/10.4018/978-1-5225-5643-5.ch091

  32. Wirtz, B.W., Müller, W.M.: An integrated artificial intelligence framework for public management. Public Manag. Rev. 21(7), 1076–1100 (2018)

    Article  Google Scholar 

  33. Zhang, X., Chen, Y.: An artificial intelligence application in portfolio management. In: Zheng, X. (ed.) Proceedings of the International Conference on Transformations and Innovations in Management, pp. 37, 86–104. Atlantis Press, Paris (2017)

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Institute of Technology and Business, School of Expertness and Valuation, Okružní 517/10, 37001, České Budějovice, Czech Republic

    J. Vrbka & Z. Rowland

Authors
  1. J. Vrbka
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Z. Rowland
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to J. Vrbka .

Editor information

Editors and Affiliations

  1. Applied Management Department, Samara State University of Economics, Samara, Russia

    Svetlana Igorevna Ashmarina

  2. Institute of Technology and Business, Ceske Budejovice, Czech Republic

    Marek Vochozka

  3. Department of Applied Management, Samara State University of Economics, Samara, Russia

    Valentina Vyacheslavovna Mantulenko

Rights and permissions

Reprints and permissions

Copyright information

© 2020 Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Vrbka, J., Rowland, Z. (2020). Using Artificial Intelligence in Company Management. In: Ashmarina, S., Vochozka, M., Mantulenko, V. (eds) Digital Age: Chances, Challenges and Future. ISCDTE 2019. Lecture Notes in Networks and Systems, vol 84. Springer, Cham. https://doi.org/10.1007/978-3-030-27015-5_51

Download citation

  • DOI: https://doi.org/10.1007/978-3-030-27015-5_51

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-27014-8

  • Online ISBN: 978-3-030-27015-5

  • eBook Packages: EngineeringEngineering (R0)

Publish with us

Policies and ethics

Search

Navigation