Simulation based Forecast of Supermarket Sales

Analysis and forecasting of the sales of products is extraordinary important for managers of supermarkets. In special, the forecasting of special bargain offers is of interest. This paper approaches this task by low level simulation of customers and products of a supermarket. The simulation is parameterized by results of analysis of real sales receipt data to achieve the results close to reality. The analysis includes building of probabilistic models based on clustering and time series analysis, as well as simulation, which is used to forecast time series of product sales. Along with results of analysis, the proposed simulation approach also incorporates results of empiric analysis of customer psychology. Further, the user can set other parameters like composition of customer types, prices of products, assortment of bargain offers, configuration of shelves and product locations, which allows investigation of different influences and specialized simulation of selectable products of interest.