Development of the subsystem of forecasting for the system of marketing information management at an industrial enterprise




marketing information, marketing information management system, types of forecasts, forecasting methods


The theoretical generalization, which is revealed in the development of conceptual and methodological principles and methodical provisions related to formation and functioning of the forecasting subsystem of the marketing information management system at an industrial enterprise, is presented.

The market is a social phenomenon in which the availability of valuable marketing information reduces uncertainty, ensures the promptness of making managerial decisions, makes possible to avoid threats and creates a basis for increase in the efficiency of a production process and competitiveness. Therefore, the control of changes in the marketing environment requires the creation of a marketing information management system at an industrial enterprise, which is based on effective methods of collection and analysis of marketing information. Markets of industrial enterprises make possible to create and test progressive marketing information management systems.

There are trends that cause worsening of prospects for economic growth at the current state of the marketing environment of industrial enterprises. Growth of these risks is facilitated by trends of globalization, informatization, social changes. Such an increase in business risks causes an increase of the role of forecasting. The classical concept of a marketing information management is enhanced and system is restructured and the creation of a subsystem of forecasting is improved. The methodological approach to the functioning of forecasting subsystems of marketing information systems of industrial enterprises based on the model of statistical forecasting of sales volume is offered.

The proposed procedure to overcome a general lack of forecasting methods is related to the failure to take into account an inaccuracy of observations on which the forecast is based, – it is based on the use of fuzzy mathematical methods. It is shown on its basis how traditional forecasting methods can be successfully upgraded for the case when the initial data are given unclearly

Author Biographies

Mykhailo Oklander, Odessa National Polytechnic University Shevchenko blvd., 1, Odessa, Ukraine, 65044

Doctor of Economic Sciences, Professor

Department of Marketing

Tatyana Oklander, Odessa State Academy of Civil Engineering and Architecture Didrihsona str., 4, Odessa, Ukraine, 65029

Doctor of Economic Sciences, Associate Professor

Department of Economy of the enterprise

Irina Pedko, Odessa State Academy of Civil Engineering and Architecture Didrihsona str., 4, Odessa, Ukraine, 65029

Doctor of Economic Sciences, Associate Professor

Department of Economy of the enterprise

Oksana Yashkina, Odessa National Polytechnic University Shevchenko blvd., 1, Odessa, Ukraine, 65044

Doctor of Economic Sciences, Associate Professor

Department of Marketing


  1. Malhotra, N. K., Birks, D. F. (2007). Marketing Research: An Applied Approach. Prentice Hall/Financial Times, 835.
  2. Churchill, G. A., Iacobucci, D. (2005). Marketing Research: Methodological Foundations. Thomson/South-Western, 697.
  3. Evans, J. R., Berman, B. (2005). Marketing in the 21st Century. Cincinnati, Ohio: Atomic Dog Publishing, 628.
  4. Gates, B. (1999). Business @ the Speed of Thought: Using a Digital Nervous Sistem. New York: Warner Books, 166.
  5. Kotler, P. T., Keller, K. L. (2011). Marketing Management. Hardcover, 668.
  6. Kourentzes, N., Rostami-Tabar, B., Barrow, D. K. (2017). Demand forecasting by temporal aggregation: Using optimal or multiple aggregation levels? Journal of Business Research, 78, 1–9. doi: 10.1016/j.jbusres.2017.04.016
  7. Athanasopoulos, G., Hyndman, R. J., Kourentzes, N., Petropoulos, F. (2017). Forecasting with temporal hierarchies. European Journal of Operational Research, 262 (1), 60–74. doi: 10.1016/j.ejor.2017.02.046
  8. Guo, F., Diao, J., Zhao, Q., Wang, D., Sun, Q. (2017). A double-level combination approach for demand forecasting of repairable airplane spare parts based on turnover data. Computers & Industrial Engineering, 110, 92–108. doi: 10.1016/j.cie.2017.05.002
  9. Lessmann, S., Voß, S. (2017). Car resale price forecasting: The impact of regression method, private information, and heterogeneity on forecast accuracy. International Journal of Forecasting, 33 (4), 864–877. doi: 10.1016/j.ijforecast.2017.04.003
  10. Garcia, M. G. P., Medeiros, M. C., Vasconcelos, G. F. R. (2017). Real-time inflation forecasting with high-dimensional models: The case of Brazil. International Journal of Forecasting, 33 (3), 679–693. doi: 10.1016/j.ijforecast.2017.02.002
  11. Bergman, J. J., Noble, J. S., McGarvey, R. G., Bradley, R. L. (2017). A Bayesian approach to demand forecasting for new equipment programs. Robotics and Computer-Integrated Manufacturing, 47, 17–21. doi: 10.1016/j.rcim.2016.12.010
  12. Zelenkov, Y., Fedorova, E., Chekrizov, D. (2017). Two-step classification method based on genetic algorithm for bankruptcy forecasting. Expert Systems with Applications, 88, 393–401. doi: 10.1016/j.eswa.2017.07.025
  13. Jiang, S., Chin, K.-S., Wang, L., Qu, G., Tsui, K. L. (2017). Modified genetic algorithm-based feature selection combined with pre-trained deep neural network for demand forecasting in outpatient department. Expert Systems with Applications, 82, 216–230. doi: 10.1016/j.eswa.2017.04.017
  14. Mirakyan, A., Meyer-Renschhausen, M., Koch, A. (2017). Composite forecasting approach, application for next-day electricity price forecasting. Energy Economics, 66, 228–237. doi: 10.1016/j.eneco.2017.06.020
  15. Bui, L. T., Truong Vu, V., Huong Dinh, T. T. (2017). A novel evolutionary multi-objective ensemble learning approach for forecasting currency exchange rates. Data & Knowledge Engineering. doi: 10.1016/j.datak.2017.07.001
  16. Kuo, R. J., Chen, C. H., Hwang, Y. C. (2001). An intelligent stock trading decision support system through integration of genetic algorithm based fuzzy neural network and artificial neural network. Fuzzy Sets and Systems, 118 (1), 21–45. doi: 10.1016/s0165-0114(98)00399-6
  17. Yang, M.-S., Lin, T.-S. (2002). Fuzzy least-squares linear regression analysis for fuzzy input–output data. Fuzzy Sets and Systems, 126 (3), 389–399. doi: 10.1016/s0165-0114(01)00066-5
  18. Seraya, O. V., Demin, D. A. (2012). Linear Regression Analysis of a Small Sample of Fuzzy Input Data. Journal of Automation and Information Sciences, 44 (7), 34–48. doi: 10.1615/jautomatinfscien.v44.i7.40
  19. Cebeci, U. (2009). Fuzzy AHP-based decision support system for selecting ERP systems in textile industry by using balanced scorecard. Expert Systems with Applications, 36 (5), 8900–8909. doi: 10.1016/j.eswa.2008.11.046
  20. Shavranskyi, V. M. (2012). Using fuzzy logic in support systems decision complications during drilling. Technology audit and production reserves, 4 (1 (6)), 35–36. doi: 10.15587/2312-8372.2012.4782
  21. Li, D.-F. (2005). Multiattribute decision making models and methods using intuitionistic fuzzy sets. Journal of Computer and System Sciences, 70 (1), 73–85. doi: 10.1016/j.jcss.2004.06.002
  22. Holt, C. C. (2004). Forecasting seasonals and trends by exponentially weighted moving averages. International Journal of Forecasting, 20 (1), 5–10. doi: 10.1016/j.ijforecast.2003.09.015
  23. Winters, P. R. (1960). Forecasting Sales by Exponentially Weighted Moving Averages. Management Science, 6 (3), 324–342. doi: 10.1287/mnsc.6.3.324
  24. Box, G. E., Jenkins, G. M., Reinsel, G. C. (1994). Time Series Analysis, Forecasting and Control. NJ.: Prentice Hall, Englewood Clifs, 379.
  25. Mandelbrot, B. (1972). Statistical Methodology for Non-Periodic Cycles: From the Covariance to R/S Analysis. Annals of Economic and Social Measurement, 1 (3), 259–290.
  26. Mandelbrot, B. B., Hudson, R. L. (2004). The (mis)behavior of markets: a fractal view of risk, ruin and reward. N.Y.: Basic Books, 352.
  27. Peters, E. E. (1994). Fractal market analysis: applying chaos theory to investment and economics. John Wiley &Sons, Inc, 336.
  28. Yankovyi, O. H., Yashkina, O. I. (2006). Prohnozuvannia vzaiemopoviazanykh pokaznykiv sotsialno-ekonomichnoho rozvytku Ukrainy. Statystyka Ukrainy, 3, 61–66.
  29. Zadeh, L. (1965). Fuzzy Sets. Information and Control, 8 (3), 338–353.
  30. Raskin, L. G., Seraya, O. V. (2008). Nechetkaya matematika. Kharkiv: Parus, 352.
  31. Dyubua, D., Prad, A. (1990). Teoriya vozmozhnostey. Prilozhenie k predstavleniyu znaniy v informatike. Moscow: Radio i svyaz', 286.
  32. Raskin, L., Sira, O. (2016). Method of solving fuzzy problems of mathematical programming. Eastern-European Journal of Enterprise Technologies, 5 (4 (83)), 23–28. doi: 10.15587/1729-4061.2016.81292
  33. Pawlak, Z. (1982). Rough sets. International Journal of Computer & Information Sciences, 11 (5), 341–356. doi: 10.1007/bf01001956
  34. Raskin, L., Sira, O. (2016). Fuzzy models of rough mathematics. Eastern-European Journal of Enterprise Technologies, 6 (4 (84)), 53–60. doi: 10.15587/1729-4061.2016.86739




How to Cite

Oklander, M., Oklander, T., Pedko, I., & Yashkina, O. (2017). Development of the subsystem of forecasting for the system of marketing information management at an industrial enterprise. Eastern-European Journal of Enterprise Technologies, 5(3 (89), 39–51.



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