Design of an Algorithm for Forecasting Purchase Prices for Building Materials in Retail Trade Networks

The graduate work is devoted to the study of the dynamic pricing processes at DIY-companies and the development of the purchase prices forecasting algorithm for selected groups of items. The relevance of the research is explained as integrated approach to price forecasting, which includes using of historical data such as purchase and sale prices, competitor prices and econometric coefficients, as well as the ability to set user parameters for applying the model in the context of a specific company's strategy. The work includes: research of existing forecasting purchase prices methods for DIY-items, collecting and analyzing data of the DIY-market, suppliers, logistic conditions and other cost components. The algorithm for forecasting purchase prices based on the collected data has been developed, tested and optimized. The efficiency of the developed algorithm was evaluated on real data.