STRESS SITUATIONS AND LOGISTICS FORECASTS FOR BUSINESS

Abstract

Forecasting methods are widely used in logistics. The values of predictive estimates of the development of the analyzed processes or phenomena are the basis for decision-making in operational, tactical, and strategic planning (from assessing the likelihood of a shortage of products in a warehouse to choosing a company development strategy). The accuracy and reliability of the forecast guarantee the effectiveness of the task’s implementation. Our work is a study of the impact of stressful situations on the building logistics forecasts for business, focusing on the issue of safety stock optimization. This paper seems to the authors to be very relevant in the context of the current economic situation in the Russian Federation. The authors have reviewed and tested a widely used linear regression model on real data of a big Russian company. Its extreme inefficiency for use in a stressful situation is shown. Accordingly, the authors have developed and introduced their own forecasting model (also tested). Further development in this direction implies the use of time series analysis to improve/correct the results obtained.