Automation becomes crucial when it comes to optimal replenishment dozens of points of sale with an assortment of thousands of items on a daily basis, based on the latest data. Most retailers engage their employees in the process of replenishment at an operational level. However optimization by increasing automation is more effective.

Statistical models are trained daily on the basis of current sales data. As a result, sales forecasts are generated for each product and store individually. Based on the forecasts, orders are created to replenish stock in each store. The entire process, from collecting receipts from cash registers, through model teaching, to generating orders for warehousemen, runs automatically, limiting people’s involvement to an absolute minimum.

The implementation of the Data-Driven Retail industry involves analyzing large amount of receipt data, information about thousands of products and dozens of retail-specific factors in order to predict future business events. Thanks to the use of advanced statistical methods on a large scale, it is highly probable that:

• trends can be identified,
• demand for individual products can be predicted,
• marketing activities can be optimized,
• sales can be increased by adjusting the offer to individual customer needs “just in time”.

Machine learning models are able to “learn” a new reality within two weeks, i.e. react to changes in an automatic way. In-depth statistical analysis supported by expert knowledge gives the business a new perspective on the current market situation.