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Dynamic Model of Enterprise Revenue Management Based on the SFA Model

Author

Listed:
  • Aliya Alimhanova

    (Faculty of Control Systems, Tomsk State University of Control Systems and Radioelectronics, 634050 Tomsk, Russia)

  • Andrey Vazhdaev

    (Faculty of Control Systems, Tomsk State University of Control Systems and Radioelectronics, 634050 Tomsk, Russia)

  • Artur Mitsel

    (Faculty of Control Systems, Tomsk State University of Control Systems and Radioelectronics, 634050 Tomsk, Russia
    Department of Experimental Physics, Tomsk Polytechnic University, 634050 Tomsk, Russia)

  • Anatoly Sidorov

    (Faculty of Control Systems, Tomsk State University of Control Systems and Radioelectronics, 634050 Tomsk, Russia)

Abstract

The actual problem of enterprise revenue management that requires an effective solution is considered. Revenue is the main source of cash proceeds specifically from the main enterprise activities, as well as one of the main factors affecting enterprise functioning. As a result, the amount of revenue is extremely important for the company—it must be sufficient to ensure the repayment of all expenses of the company and the formation of the required profit amount. However, the amount of revenue itself is not the only important characteristic of revenue; the revenue stability over time and the revenue receipt regularity are no less important. The purpose of this work is to develop a dynamic model of enterprise revenue management, which differs from the model known in the literature by considering the parameter of enterprise performance efficiency. The parametric method of Stochastic Frontier Analysis (SFA) is used as a method to evaluate the efficiency of an enterprise. Financial indicators are used as input and output data. The model was tested on six small business sectors of a single-industry town for the period from 2007 to 2021. Data collection was carried out using the SPARK system, which allows selecting enterprises for research by the status of the enterprise (bankrupt/operating), by the size of the enterprise (large/medium/small/micro), etc. The above calculations based on the constructed modified model have demonstrated the possibility of using the enterprise’s revenue management with the desired rate of change and with the work efficiency parameter.

Suggested Citation

  • Aliya Alimhanova & Andrey Vazhdaev & Artur Mitsel & Anatoly Sidorov, 2022. "Dynamic Model of Enterprise Revenue Management Based on the SFA Model," Mathematics, MDPI, vol. 11(1), pages 1-13, December.
    • Handle: RePEc:gam:jmathe:v:11:y:2022:i:1:p:211-:d:1021419
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    References listed on IDEAS

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    1. Wheelock, David C & Wilson, Paul W, 1995. "Explaining Bank Failures: Deposit Insurance, Regulation, and Efficiency," The Review of Economics and Statistics, MIT Press, vol. 77(4), pages 689-700, November.
    2. Mogilat , Anastasia & Ipatova, Irina, 2016. "Technical efficiency as a factor of Russian industrial companies’ risks of financial distress," Applied Econometrics, Russian Presidential Academy of National Economy and Public Administration (RANEPA), vol. 42, pages 05-29.
    3. Ruey-Ching Hwang & Jhao-Siang Siao & Huimin Chung & C. Chu, 2011. "Assessing bankruptcy prediction models via information content of technical inefficiency," Journal of Productivity Analysis, Springer, vol. 36(3), pages 263-273, December.
    4. Jondrow, James & Knox Lovell, C. A. & Materov, Ivan S. & Schmidt, Peter, 1982. "On the estimation of technical inefficiency in the stochastic frontier production function model," Journal of Econometrics, Elsevier, vol. 19(2-3), pages 233-238, August.
    5. Edward I. Altman, 1968. "Financial Ratios, Discriminant Analysis And The Prediction Of Corporate Bankruptcy," Journal of Finance, American Finance Association, vol. 23(4), pages 589-609, September.
    6. Merton, Robert C, 1974. "On the Pricing of Corporate Debt: The Risk Structure of Interest Rates," Journal of Finance, American Finance Association, vol. 29(2), pages 449-470, May.
    7. Dimara, Efthalia & Skuras, Dimitris & Tsekouras, Kostas & Tzelepis, Dimitris, 2008. "Productive efficiency and firm exit in the food sector," Food Policy, Elsevier, vol. 33(2), pages 185-196, April.
    8. Battese, George E. & Coelli, Tim J., 1988. "Prediction of firm-level technical efficiencies with a generalized frontier production function and panel data," Journal of Econometrics, Elsevier, vol. 38(3), pages 387-399, July.
    9. Quang Nguyen & Sean Pascoe & Louisa Coglan & Son Nghiem, 2021. "The sensitivity of efficiency scores to input and other choices in stochastic frontier analysis: an empirical investigation," Journal of Productivity Analysis, Springer, vol. 55(1), pages 31-40, February.
    10. Kai Sun & Ruhul Salim, 2020. "A semiparametric stochastic input distance frontier model with application to the Indonesian banking industry," Journal of Productivity Analysis, Springer, vol. 54(2), pages 139-156, December.
    11. Alecos Papadopoulos & Christopher F. Parmeter, 2022. "Quantile Methods for Stochastic Frontier Analysis," Foundations and Trends(R) in Econometrics, now publishers, vol. 12(1), pages 1-120, November.
    12. Shumway, Tyler, 2001. "Forecasting Bankruptcy More Accurately: A Simple Hazard Model," The Journal of Business, University of Chicago Press, vol. 74(1), pages 101-124, January.
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