The Use of Forecasting Models to Identify Trends of Logistics Development in Business Management
The Use of Forecasting Models to Identify Trends of Logistics Development in Business Management

Summary/Abstract: Background. Knowledge about the changing market trends and the ways to identify them is the key to effective business management. The basis of mistaken decisions is a wrong interpretation of information coming from the company. With regard to logistic processes, information on the amount of incurred expenses exhibits some dependencies between them. Therefore, one way of making the right decisions is the use of forecasting models based on time series, which can be used to determine future values of a studied phenomenon. Verification of the obtained results through the determination of average forecast errors can be implemented.Research aims. The research was conducted to determine the degree of relationship of macroeconomic indicators with the economic parameters emerging in the enterprise. The use of information obtained in this way can be applied to reduce the risk associated with entrepreneurial activity. The utilitarian purpose of this research is defining the correlation coefficient used to determine the extent to which the investigated factors are interdependent and making predictions about the future value of inventory costs.Methodology. To implement its objectives, critical analysis of literature was applied, analysis of statistical data, a method of determining dependencies between those variables based on the correlation coefficient and the ARIMA method of forecasting based on time series, using advanced autoregression and moving average models. On the basis of the average of relative forecast errors the accuracy of the forecast for future periods can be determined.Key findings. The main result of the analysis is to obtain information about the influence of macroeconomic factors on changes of the value of the field of logistic enterprises as well as the use of autoregression and moving average models to determine the future size of the variables in the time series. Using the ARIMA model, made on the basis of real plant data forecasts relating to the cost of inventory, showed the suitability of the method for pre-planning of the future logistics trends.

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