Managing business processes using soft computing techniques – a literature review

Authors

  • Nikola D. Vojtek PhD student at FON

DOI:

https://doi.org/10.7595/management.fon.2017.0023

Keywords:

Soft Computing, Fuzzy logic, Neural Networks, Business Process management

Abstract

Non-ideal and turbulent environment is affecting the business operations of today's companies. The influence and behavior of external factors is not possible or very difficult to predict, but necessary to consider in the decision making process at all organizational levels. Development of computer technologies allowed companies processing large amounts of data in order to make decisions which take into account both internal and external factors. Due to their tolerance of imprecision and uncertainty, Soft computing techniques (SCT) are increasingly used in solving those problems. They are characterized by the ability to adapt quickly to the changes in the environment, stability when processing large amounts of inaccurate data and real time responsiveness. The basic and most commonly used Soft Computing techniques are Fuzzy logic (FL) and neural networks (NN). This paper gives a review of 78 papers referring to the development of theory and practical application of soft computing techniques in business process management. Papers were selected based on the journals impact factor and year of publication (with the focus on recent papers). Approximately, equal number of both Fuzzy logic and Neural Network techniques was tried to be captured. Although this review is not final, it can be considered as a valid guide to explore the opportunities and possibilities which fuzzy logic and neural networks offer process owners in managing their business processes. In conclusion of this paper, fields for further research have been identified.

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Published

2017-10-31

How to Cite

Vojtek, N. D. (2017). Managing business processes using soft computing techniques – a literature review. Management:Journal of Sustainable Business and Management Solutions in Emerging Economies, 23(1), 63–71. https://doi.org/10.7595/management.fon.2017.0023

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