Intelligent Warehouse Stocking Using Machine Learning

In today’s era, any small or medium-sized business is required perfect stock management to fulfill the customer’s need. To do so, skilled labour with lots of money needs to be invested. E-commerce giants use Machine Learning techniques to maintain their stock based on demands produced by their buyers at various time intervals. Therefore, to predict the demand for various products and to improve their sales for either small or medium-sized businesses, Warehouse Management can be extended as a service. When we are thinking about demand forecasting, which is a crucial part of all businesses, a question comes to mind that is how much stock of an item should a company or business needs to keep to meet the demands of their customer so that their daily needs can be accomplished. The predictive forecast provides the necessary solution and also provides many benefits such as it provides an advantage of the better customer experience through the reduction of out-of-stock situations, and for lower costs due to better-planned stock. Therefore, this paper focuses on discussing the challenges of building an Intelligent Warehouse Stocking system and discusses the design decisions.