A Linear Regression Model for Deploying a Cognitive Web for an Inventory Prediction System

Abstract

Inventory management plays a crucial role in the sales system as it indirectly impacts customer satisfaction. Inaccurate determination of the quantity of goods to be purchased often leads to unstable stock circulation in the warehouse. The numerous factors influencing procurement decisions pose challenges for managers. Several initiatives have been undertaken to maintain optimal stock levels to ensure availability when required. In this study, we developed a linear regression model to estimate the inventory for the upcoming one-month period. The selection of linear regression was motivated by its ability to forecast future trends. The research involved creating a web application that utilized sales data from the previous six months, focusing on examples of products sold in a store. The objective of the application is to assist store owners in making informed decisions regarding stock replenishment for the next period. By doing so, they can fulfill customer demands without excessive inventory accumulation, while considering the limitations of storage capacity.