## 1 Introduction  
Modern e-commerce allows companies to compete not only on a local but also on a global scale [1]. From the perspective of the global market, it is not important where the product comes from, but how much time and resources it takes for a shipment delivery to reach a target customer. The costs of material handling and final transportation are often a significant part of the total cost of a product, especially for products of relatively small value [2]. In the 21 st century, a new ideology, Industry 4.0, was formed. With the growth of technology, the large base of data and customer expectations forced the companies to be more effective. Modern companies must pay attention to inciting the processes, self-analysis and self-optimization of the short response time to market changes and the maximum use of related data. It is a way for a company to be competitive on the market. Industry 4.0 is also important for intralogistics in a warehouse. The main process in the warehouse which takes the most time is the products' picking. The effectiveness of this process can be optimized by using new technology, such as robots, voice picking, and augmented reality supported by the better insight of the data from WMS (Warehouse Management System) to reduce the distance of picking route. This distance can be reduced by solving the product allocation problem (PAP). Proper warehouse system planning and PAP in the warehouse are major challenges for industrial companies. The correct assignment of products in warehouses can reduce the cost of energy, the number of forklifts and order-pickers, along with the lead time. For these reasons, it is extremely important to define the optimal storage locations so as to search for better conditions of profitability and service levels in companies. In intralogistics, a warehouse is often a bottleneck, which raises the total costs of a product. Consequently, if product allocation planning is performed randomly, it may prove that higher-demand products will be located most distant from the picking and packing zone. In such cases, there is a risk of unnecessary travel time for the order-picking process. Therefore, the loss of time for the order-picking and the increase in order-pickers and material handling systems may occur. The currents methods for solving PAP are not enough to meet the requirements of Industry 4.0. Currently, these methods enable the grouping of products to different ranks. After classification, they are distributed in the warehouse area. This distribution aims to ensure the shortest time to access products of the highest frequency from the gates/dispatch zone. The typical methods of products classification are methods called ABC, XYZ, EIQ, AHP, and COI Index analyses. Although these methods are still widely used in practice, they do not satisfy the requirements placed on the warehouses nowadays. The above methods are most often performed for one single criterion. However, the mentioned methods can be used to allocate products by different criteria at the same time, but do not allow the analysis of correlations between classified products [3; 4; 5; 6; 7; 8]. Regardless of the industry branch, the modernization of the logistic processes carried out within the warehouse is one of the key components of building a competitive advantage. This is especially important for e-commerce companies, being the major reason for the optimization of intralogistics processes in warehouses where many products are picked per hour. This research gap motivates the authors to propose a new approach based on the collected data in WMS. The proposed model is based on correlated data, such as products parameters, clients' orders and warehouse layout. The proposed model in our paper enables us to select the best product classification methods automatically, so as to find the correlation between the analyzed criteria and results. The best methods (ABC, XYZ, EIQ, AHP) are mixed to achieve even better results. The mixing of the methods is done by an artificial neural network.