Paper Title
AUTONOMOUS RETAIL INVENTORY OPTIMIZATION USING DECENTRALIZED MULTI-AGENT ARCHITECTURE

Abstract
The cost of loss of inventory in the retail industry is a major financial burden every year. This paper introduces a completely independent, decentralized multi-agent system that is aimed at retail inventory optimization. The system eliminates the habitual human sanctioning procedure on all the phases of the decision pipeline and still retains human control on the governance level. There are four specialized agents that deal with Demand Forecasting, Replenishment, Dashboard Monitoring, Supplier Coordination. The two of them create a closed feedback loop in which demand forecasts are used to make restocking decisions, restocking decisions are used to make supplier orders, and real-time keeps a track of the stock level. The distributed architecture eliminates failure points, removes delays that are introduced by humans, and allows the continuous adaptation to be responsive. Agents communicate by clearly defined API contracts, as opposed to having a global controller, maintaining the independence of each component. It is based on the recent studies on predictive autonomous supply chain and architecture gives a practical roadmap of lights-out inventory management with artificial intelligence in control of demand, end to end, supplier, coordination and sensing. Human stakeholders still have the responsibility of parameter setup, vendor setup, and audit setup. Keywords - Multi-Agent Systems, Retail Inventory Optimization, Autonomous Supply Chain, Demand Prediction, Re-ordering Decision, Supplier Co-ordination, Decentralized Architecture, Minimal Human Intervention