ABSTRACT:The convergence of multi-vendor e-commerce architecture and artificial intelligence has given rise to a new paradigm in grocery retail management. This paper presents a comprehensive research study on the design, architecture, and implementation of a Multi-Vendor Grocery Management System (MVGMS) integrated with AI-powered future prediction capabilities. Against a backdrop of a global online grocery market projected to grow from $655.51 billion in 2025 to $1.72 trillion by 2030 at a CAGR of 21.3%, this system addresses critical operational challenges such as demand forecasting, inventory optimization, vendor performance analysis, food waste minimization, and personalized consumer experiences. The research examines state-of-the-art machine learning models including Long Short-Term Memory (LSTM) networks, XGBoost, and ensemble hybrid approaches, which have demonstrated forecast error reductions of up to 42.87% compared to traditional statistical methods. The paper further evaluates system architecture, data pipelines, ethical considerations, and implementation roadmaps for deploying AI forecasting in a multi-vendor grocery context. Findings indicate that AI-powered demand forecasting can reduce inventory errors by 30-50%, cut food waste by up to 49%, lower lost sales due to stockouts by 65%, and reduce warehousing costs by 10-40%. The research concludes with a proposed system model, future research directions, and a discussion of the broader impact on supply chains, sustainability, and digital commerce.
KEYBOARD: Multi-Vendor Marketplace, Grocery Management System, AI-Powered Forecasting, Demand Prediction, LSTM, XGBoost, Inventory Optimization, Food Waste Reduction, Supply Chain Intelligence, Digital Commerce
MULTI-VENDOR GROCERY MANAGEMENT SYSTEM WITH AI-POWERED FUTURE PREDICTION
AMOL JADHAV