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Managing the Production Program of a Small Innovative Chemical Enterprise in the Face of Changing Demand

Author

Listed:
  • I. L. Beilin*

    (Kazan Federal University, Institute of Management, Economics and Finance)

  • V. V. Khomenko

    (Kazan Federal University, Institute of Management, Economics and Finance)

  • N. M. Yakupova

    (Kazan Federal University, Institute of Management, Economics and Finance)

  • E. I. Kadochnikova

    (Kazan Federal University, Institute of Management, Economics and Finance)

  • D. D. Aleeva

    (Kazan Federal University, Institute of Management, Economics and Finance)

Abstract

The article examines the economic problems of innovative enterprises, taking into account the cyclical nature of their development, which are characterized by models of increasing profits with the least losses. As the main method, additive convolution is used with equally important and weighted demand for an innovative product. The estimation of the economically optimal volume of products is presented. The research is complex and can have an impact on social, environmental and other performance indicators of the production sector of the economy. The process of globalization has led to the emergence of a complex network of relationships in the business environment. In a free market economy, this means an increased complexity and uncertainty of factors affecting the financial position of the entities. At present, many features of the finance and economics of innovative enterprises are unclear, which makes it difficult to analyze using traditional methods of economic and mathematical modeling. Forecasting the bankruptcy of business and consumers is also inaccurate and ambiguous, as many internal and external factors influence this process. Even a simple statement that an enterprise or a consumer is at risk of bankruptcy should be considered inaccurate and in fact, rarely in an economic reality there are entities that can be considered bankrupt 100%. It is almost impossible to accurately determine the degree of threat of bankruptcy using traditional statistical methods, such as multidimensional discriminant analysis. When the value of the discriminant function is less than the threshold value, it is considered that the enterprise is exposed to the risk of bankruptcy. Given the cyclical nature of the development of an innovative enterprise, it seems necessary to define ambiguous concepts, such as "high risk of bankruptcy" or "low risk of bankruptcy." We need approaches that can be used not only to predict the level of risk, but also to determine the degree of positive financial position of the analyzed enterprise, for example, "High Solvency" or "Average Solvency", depending on changes in the production program.

Suggested Citation

  • I. L. Beilin* & V. V. Khomenko & N. M. Yakupova & E. I. Kadochnikova & D. D. Aleeva, 2018. "Managing the Production Program of a Small Innovative Chemical Enterprise in the Face of Changing Demand," The Journal of Social Sciences Research, Academic Research Publishing Group, pages 175-180:5.
    • Handle: RePEc:arp:tjssrr:2018:p:175-180
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    References listed on IDEAS

    as
    1. Balcaen, Sofie & Ooghe, Hubert, 2006. "35 years of studies on business failure: an overview of the classic statistical methodologies and their related problems," The British Accounting Review, Elsevier, vol. 38(1), pages 63-93.
    2. Ravi Kumar, P. & Ravi, V., 2007. "Bankruptcy prediction in banks and firms via statistical and intelligent techniques - A review," European Journal of Operational Research, Elsevier, vol. 180(1), pages 1-28, July.
    3. Ji, Tingting, 2004. "Consumer Credit Delinquency And Bankruptcy Forecasting Using Advanced Econometrc Modeling," MPRA Paper 3187, University Library of Munich, Germany.
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