Özet:

Optimization of the stock management process is associated with the search for a forecasting model, a method for generation of a forecasting time series, a model of logistic operation, determining a reasonable level of reserve stocks and establishing the optimization criterion. Successful solution to the optimization problem in general can be achieved only if the whole complex of local management problems is successfully solved. In this case, the method of generation of a cointegrated time series of demand forecasting is the central element of the technology of optimal stocks management. This relates to the fact that probabilistic nature of demand is the main factor reducing efficiency of management in systems of this class. It was shown that the proposed method for improving management efficiency can be used in any economic system due to the possibility of construction of a single logistic operation model. The proposed approach is based on formation of a time series specifically designed to solve the problem of forecasting the demand in stocks buffering systems. Such a series contains both information on sales volumes and data related to consumer demand. Since consumer activity is ahead of the process of physical consumption of products, it becomes possible to use anticipatory markers in forecasting problems. The study of operational processes using a verified indicator of efficiency has confirmed the hypothesis of presence of anticipatory markers within the framework of the formed forecast time series.It has been established that the maximum management efficiency can be achieved in the case of a lower construction accuracy of the forecast model. This is due to the fact that the logistic operation model takes into account the costs of movement of products and their valuation at the operation input and output. Author Biographies Igor Lutsenko, Kremenchuk Mykhailo Ostrohradskyi National University Pershotravneva str., 20, Kremenchuk, Ukraine, 39600 Doctor of Technical Sciences, Professor Department of Information and Control Systems

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