Demand forecast based on the cluster model to improve the performance of system

In the World Championships in modern times, have different products to suit the customer s request, was available to him. It is important that we assess and track the performance of the supply chain, especially because several organizations are involved in this chain. A variety of criteria can be used for this purpose. A model approach to supply chain operations reference, which represents an effort to standardize assess supply chain performance is considered. Metrics include on-time delivery, order fulfillment time delivery, fill rate (the deficit of the balance of demand that is met); full order fulfillment, supply chain response time, production flexibility, cost management, supply chain, total days in the supply; cycle time fund to fund, net asset turnover.Goal: The purpose of this paper is to develop a forecasting model for retailers based on customer segmentation, to improve performance of inventory.Method: The research makes an attempt to capture the knowledge of segmenting the customers based on various attributes as an input to the demand forecasting in a store. The paper suggests a data mining model which has been used for forecasting of demand. The proposed model has been applied for demands of grocery items in a store. Based on the proposed forecasting model, the inventory performance has been studied with simulation.Findings: The proposed forecasting model with the inventory replenishment system results in the reduction of inventory level and increase in customer service level. Hence, the proposed model in the paper results in improved performance of inventory.Originality/value- With the advent of data mining systems which have given rise to the use of business intelligence in various domains, the current paper addresses one of the most pressing issues in retail management, as demand forecasting with minimum error is the key to success in inventory and supply chain management. The proposed forecasting model with the inventory replenishment system results in the reduction of inventory level and increase in customer service level. The proposed model outperforms other widely used existing models.

Keywords:

store , Inventory management , Demand forecasting , Supply chain management , Data mining , Artificial intelligence , Logistics.