Procurement Process Automatization of Retail Stores with Using of Sales Forecasting

This research paper is aimed at optimizing the procurement processes of the retail stores "Dachnik" through its automation, namely, creating the required report using functional of the 1C:Enterprise system and sales forecasting tool as an additional source of information during determining the purchase volumes. Such automation should reduce the costs of the purchasing department business processes, as the subprocess of forming the orders for suppliers was simplified. What is more, combining two methods of determining the purchasing volume (calculation based on the formula specified by the management and forecasting of sales) will provide more accurate determination of the required goods volumes. The main goal of this work is the development of a software module for purchasing activities automation and sales forecasting. To achieve the above aim, it is necessary to perform some tasks: · To conduct a detailed analysis and modeling of the current procurement process of the retail stores "Dachnik" · To create the report «Forming the order to suppliers» in the 1C:Enterprise system, which automates the subprocess «Sales analysis» of the procurement process · To design a model for seasonal sales forecasting for key product groups in Microsoft SSAS · To optimize the procurement business process and modeling its future state after automation. The outcomes of the research contain the model of procurement processes of retail stores “Dachnik” up to the second level of decomposition in IDEF0 notation and analysis of company’s IT-infrastructure. This allowed to identify a number of limitations in the procurement process and to find ways of its optimization. The report «Forming the order to suppliers» and the model for seasonal sales forecasting for key products groups can be emphasized as the key results of this research. Due to these developments, the procurement process is now carried out 6 times faster, with greater accuracy and with money costs 79% less than before.