A SUPPLY CHAIN MANAGEMENT BASED CUSTOMER PREDICTION MODEL FOR GROCERY STORE

Management is now on the verge of a breakthrough in figuring out precisely how the interactions ultimately decide performance in the service sector between knowledge flows, resources, energy, human resources, and capital equipment. How these five concepts of flow interlock to be able to reinforce one another and to bring about switching and fluctuation should provide the grounds for predicting the emergence of actions, strategies, organizational types and investment choices. A principle of distribution management that recognized the interconnected complexities of organizational relationships as organizations are so intertwined. The expert argued that technological capabilities would affect the overall functionality of skills such as science, engineering, sales, and promotion. Illustrated phenomena use a laptop computer or perhaps computer simulation of order information flow along with the impact of its effect on development in addition to the general distribution functionality for each supply chain member, in addition to the entire supply chain application. A proposed customer forecasting model based on Machine Learning will be implemented to show the customer forecasting research work for the grocery store located in Lucknow, Uttar Pradesh, India. This research paper outlines the use, application and implementation of the Kernel-based Support Vector Machine for regression analysis. Also, it selects the best Kernel for the predicted model to be implemented.