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Considering Random Factors in Modeling Complex Microeconomic Systems

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  • Oksana Hoshovska

    (Institute of Administration and Postgraduate Education, Department of Theoretical and Applied Economics, Lviv Polytechnic National University, 79013 Lviv, Ukraine)

  • Zhanna Poplavska

    (Institute of Administration and Postgraduate Education, Department of Theoretical and Applied Economics, Lviv Polytechnic National University, 79013 Lviv, Ukraine)

  • Natalia Kryvinska

    (Department of e-Business, Faculty of Business, Economics and Statistics, University of Vienna, 1010 Wien, Austria
    Department of Information Systems, Faculty of Management, Comenius University in Bratislava, 81499 Bratislava, Slovakia)

  • Natalia Horbal

    (Institute of Economics and Management, Department of Foreign Trade and Customs, Lviv Polytechnic National University, 79013 Lviv, Ukraine)

Abstract

Within the framework of a model describing real-functioning association of three enterprises, numerical calculations of economic dynamics parameters considering fluctuating market demand for the goods were performed. A methodology was suggested for approximated consideration of both seasonal and random demand fluctuations at the market of textile garments; the main steps of the suggested methodology were described. The main exogenous random factors within this model include, as stated above, the volume of market demand for the goods produced by the enterprises of the group. The basic volume of market demand is considered at the average actual level according to the results of the enterprises’ analysis, and additionally we take into account the influence of non-price factors, such as random changes in the consumers’ tastes, consumers’ income, and other random factors on the market demand. By volume of market demand, we consider the total amount of goods produced by the enterprises of the group that all consumers are willing and able to purchase at a specific price in a marketplace. The calculations were made based on actual values of external economic parameters, such as labor cost, product prices, etc. Influence of the market demand fluctuations on the companies’ activity has been illustrated both numerically and graphically, allowing the analysis of the impact of exogenous parameters on the companies output and profits. The suggested approach creates a basis for further analysis of the impact of random factors of a similar nature, i.e., stochastic shocks related to the level of interest rates, shifts and turnabouts in the social environment, as well as the market transformations due to annual/seasonal epidemics.

Suggested Citation

  • Oksana Hoshovska & Zhanna Poplavska & Natalia Kryvinska & Natalia Horbal, 2020. "Considering Random Factors in Modeling Complex Microeconomic Systems," Mathematics, MDPI, vol. 8(8), pages 1-18, July.
  • Handle: RePEc:gam:jmathe:v:8:y:2020:i:8:p:1206-:d:387895
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    References listed on IDEAS

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    1. Thomassey, Sebastien & Happiette, Michel & Castelain, Jean-Marie, 2005. "A global forecasting support system adapted to textile distribution," International Journal of Production Economics, Elsevier, vol. 96(1), pages 81-95, April.
    2. Kilian, Lutz & Zhou, Xiaoqing, 2018. "Modeling fluctuations in the global demand for commodities," Journal of International Money and Finance, Elsevier, vol. 88(C), pages 54-78.
    3. Jin, Ming & DeHoratius, Nicole & Schmidt, Glen, 2017. "In search of intra-industry bullwhips," International Journal of Production Economics, Elsevier, vol. 191(C), pages 51-65.
    4. Xun Wang & Fotios Petropoulos, 2016. "To select or to combine? The inventory performance of model and expert forecasts," International Journal of Production Research, Taylor & Francis Journals, vol. 54(17), pages 5271-5282, September.
    5. Rina Tanaka & Aya Ishigaki & Tomomichi Suzuki & Masato Hamada & Wataru Kawai, 2019. "Data Analysis of Shipment for Textiles and Apparel from Logistics Warehouse to Store Considering Disposal Risk," Sustainability, MDPI, vol. 11(1), pages 1-14, January.
    6. Shuyun Ren & Hau-Ling Chan & Pratibha Ram, 2017. "A Comparative Study on Fashion Demand Forecasting Models with Multiple Sources of Uncertainty," Annals of Operations Research, Springer, vol. 257(1), pages 335-355, October.
    7. Gorman, Michael F. & Brannon, James I., 2000. "Seasonality and the production-smoothing model," International Journal of Production Economics, Elsevier, vol. 65(2), pages 173-178, April.
    8. Thomassey, Sebastien & Happiette, Michel & Castelain, Jean Marie, 2005. "A short and mean-term automatic forecasting system--application to textile logistics," European Journal of Operational Research, Elsevier, vol. 161(1), pages 275-284, February.
    9. ZELLNER, Arnold & KMENTA, Jan & DREZE, Jacques H., 1966. "Specification and estimation of Cobb-Douglas production function models," LIDAM Reprints CORE 12, Université catholique de Louvain, Center for Operations Research and Econometrics (CORE).
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    3. Jin-Biao Lu & Zhi-Jiang Liu & Dmitry Tulenty & Liudmila Tsvetkova & Sebastian Kot, 2021. "RETRACTED: Implementation of Stochastic Analysis in Corporate Decision-Making Models," Mathematics, MDPI, vol. 9(9), pages 1-16, May.

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