Expiry Classification and Improved Efficient Delivery Mechanism of Packaged Food Product Using XGBoost Algorithm and Haversine Formula

Abstract

Supply chain management and typical small scale independent retailer's poor infrastructure, poor management approach, makes Supply chain management at state-of-the-art levels is challenging in the present situation. There are mainly unorganized retailers dominating the Indian retail market. Retailers who are haphazard homogeneous class present in today's markets. There has been a significant growth in organized retailers entering the retail industry in recent years. There are several drawbacks associated with the existing supply chain that serves mainly independent retailers. Among the current supply chain's major disadvantages are numerous intermediaries, wastage and quality degradation, poor infrastructure, and high costs. These observations explore ways of improving transportation and supply chain management within the food processing industry. Several factors contribute to the inefficiency of transportation and handling systems. This results in a lack of consistency in the supply of quality raw materials to processing, packaging, and distribution companies. In today's competitive market, it is extremely difficult to successfully maintain high quality, low cost products without any additional costs from suppliers. As a network process, supply chain management involves people, organizations, information, activities, and resources. Uncertainty and variation in supply affect supply chain accomplishment, so SCM must be able to accommodate them. A Streamlining supply chains in an effective way is through information sharing systems. In our proposed system the quality classification of food products with expiry dates using machine learning technique improves the SCM. The system uses XGBOOST algorithm which makes the classification with perception and clarification than K-Means algorithm.