Published December 20, 2022 | Version v1
Journal article Open

Development of new mathematical methods and algorithms for verifying the adequacy of mathematical models of objects based on data from a natural experiment to determine the functional stability area

Creators

  • 1. European Institute for Innovation Development (Ostrava)
  • 2. Admiral Makarov State University of Maritime and Inland Shipping (St. Petersburg)

Description

The relevance of the development of applied mathematical modelling, which includes numerical methods and software packages in its problem area, its importance for the entire economic activity of the country as a whole, is due to the intensive digitalisation and computerisation of all technological chains of production processes. The integration of production support and various databases, as well as all parts of production and their effective management, require the development of comprehensive research of mathematical methods for modelling production processes. To date, mathematical modelling is applied to calculations of the financial stability point function, which does not fully reflect the variability of the predicted consequences, and consequently, the set of measures to preserve this stability. Due to the complication of production and economic relations, the need for modeling and calculating the area of financial stability, i.e., a set of marginal and non-marginal indicators, under which the economic condition of the enterprise will be considered to be acceptably stable, is actualised. The scientific problem is that mathematical modelling of production and economic processes does not provide for a wide variability (set) of indicators of financial stability as an area, which prevents flexivity in the economic activity of the enterprise. The scientific novelty of the work consists in the development of a method and algorithm for determining the financial stability area of an economic entity. The purpose of the study was to create a mathematical apparatus for calculating the financial stability of an enterprise. In the course of the study, the works of leading scientists and researchers in mathematical modelling and business processing, as well as the works of the authors of the article in this field were used. The authors presented a methodology for the development of quantitative indicators and, based on it, a methodology for mathematical modelling of calculating the financial stability area as a mathematical system that includes all eight main coefficients accepted as parameters of financial stability, and considers the limits that correspond to the economic indicators of the stability of the enterprise.

Files

inn2022-12-01.pdf

Files (708.8 kB)

Name Size Download all
md5:36b191336dc219f5baedaa9517083cc9
708.8 kB Preview Download

Additional details

References

  • Business Process Model and Notation (BPMN). Version 2.0. https://www.omg.org/spec/BPMN/2.0/PDF
  • Buychik, A. (2021a). Basic principles application of the ABC methodology in human resource management at the enterprise. Actual Issues of Management Development. European Scientific e-Journal, 12(6), 24-32.
  • Buychik, A. (2021b). Updating the parameters of the development of effective economic thought to motivate society to finance innovative activities. Economy at the Crossroads of Time. European Scientific e-Journal, 10(4), 7-16.
  • Chow, G. C. (1997). Dynamic economics: Optimization by the Lagrange method. New York: Oxford University Press.
  • Churchman, C. W. (1963). Thinking for Decisions: Deductive Quantitative Methods. Chicago, Illinois: Science Research Associates.
  • Gujarati, D. N. (1992). Essentials of econometrics. New York: McGraw-Hill.
  • Gujarati, D. N. (1995). Basic Econometrics. New York: McGraw-Hill.
  • Harzallah, M. (2007). Incorporating IDEF3 into the Unified Enterprise Modelling Language. Proceedings of the 2007 Eleventh International IEEE EDOC Conference Workshop, 133-140.
  • Hofacker, I., & Vetschera, R. (2001). Algorithmical approaches to business process design. Computer & Operations Research, 28, 1253-1275.
  • Jeong, K.-Y. (2008). Integration of queuing network and IDEF3 for business process analysis. Business Process Management Journal, 14(4), 471-482.
  • Komissarov, P. V. (2021a). Comprehensive assessment of the base of mathematical modelling of production business processes. Actual Issues of Management Development. European Scientific e-Journal, 14(8), 7-23.
  • Komissarov, P. V. (2021b). Determination of the centric rate of the economic stability domain for manufacturing enterprises. Economy at the Crossroads of Time. European Scientific e-Journal, 10(4), 27-36.
  • Koubarakis, M. (2002). A formal framework for business process modelling and design. Information Systems, 27, 299-319.
  • Kurpayanidi, K. I. (2019). Theoretical basis of management of innovative activity of industrial corporation. ISJ Theoretical & Applied Science, 69(1), 7-14. (In Russian)
  • Powell, S. G., Schwaninger, M., & Trimble, C. (2001). Measurement and control of business processes. System Dynamics Review, 17(1), 63-91.
  • Teshabaev, A. E. (2018). The methodological approaches to management improving for modern companies. Scientific Technical Journal, 22, 108-115. (In Russian)
  • Vergidis, K., & Tiwari, A. (2008). Business process analysis and optimization: beyond reengineering. IEEE Transactions on Systems, Man, Cybernatics. Part C: Application and Reviews, 1-14.
  • Whitman, L., & Huff, B. (1997). Structured models and dynamic systems analysis: The integration of the IDEF0/IDEF3 modeling methods and discrete event simulation. Proceedings of Simulation Conference, 518-524.
  • Zadeh, L. (1965). Fuzzy Sets. Information & Control, 8, 338-353.