Agricultural Product Supply Chain Inventory Control and Allocation Decision Support System Integrating Artificial Intelligence Technology

Among important factors in the agricultural sector is the control of inventory. This ensures that a business maintains the right amount of stock. Inventory control plays an important role in waste management, resource use optimization, and timely deliveries. This helps to meet demand fluctuations of goods. Here, we present a decision support system (DSS) for inventory control and allocation, integrating artificial intelligence (AI) technologies such as machine learning algorithms, predictive analytics, and intelligent optimization techniques. Our proposed system uses real-time data from multiple sources, including weather forecasts, market demand predictions, and logistics data, to optimize inventory levels and dynamically allocate products across the supply chain. AI-driven models, including neural networks and support vector machines, are used to predict demand and adjust supply chain strategies accordingly. Also, heuristic algorithms such as the genetic algorithm (GA) and particle swarm optimization (PSO) are used to resolve complex allocation problems, minimizing costs and reducing spoilage of perishable goods. The DSS also incorporates feedback loops that enable continuous learning and system improvements based on evolving data patterns. The model is tested using real-world agricultural data, demonstrating its effectiveness in enhancing operational efficiency, reducing waste, and improving responsiveness to market demand. Our results focus on the potential of AI-integrated systems in transforming the agricultural supply chain, offering a robust framework for practitioners and policymakers aiming to adopt sustainable, data-driven supply chain solutions.