Neoj4 and SARMIX Model for Optimizing Product Placement and Predicting the Shortest Shopping Path

Abstract

Product placement of top-selling items in highly visible aisles inside supermarkets plays a crucial role in enhancing customer shopping experience. Moreover, it is important for retailers to assure that their customers can effortlessly navigate the store and locate the items they are searching for in a timely manner. The research proposes a novel and effective approach that combines two methods; the SARIMAX model for forecasting sales of each product based on historical data; by using the predicted result, placing the most demanding item in highly visible aisles. And the use of Graph Database Management Systems (GDBMS) such as Neo4j to find the shortest path for consumers to navigate throughout the store to finish the shopping as per their shopping list. By leveraging the power of data analytics and machine learning, retailers can make data-driven decisions that result in improved sales andcustomer satisfaction. Retailers investing in these technologies and strategies will likely see a significant increase in customer satisfaction and sales