Abstract

Global supply chains increasingly operate under persistent supply shortages driven by geopolitical disruptions, pandemics, and capacity constraints. During such periods, allocation decisions directly influence revenue realization, service continuity, and customer trust. From our research and simulation work, we observed that many organizations still rely on manual or rule-based allocation methods, which often lead to inconsistent decisions and lost revenue during shortages. In this paper, we present a prioritized revenue, AI driven global allocation framework designed to optimally distribute constrained inventory across regions. Based on our direct implementation and simulation experience, the proposed approach integrates demand forecasts, historical revenue contribution, and strategic location priorities into a mathematically grounded optimization model. We used simple weighting and proportional allocation so that the results are easy to understand, explain, and audit. Simulation results across multiple shortage scenarios show that the proposed method consistently improves revenue realization compared to traditional allocation approaches, while maintaining fairness and operational feasibility. Through this research, we provide a practical and implementable allocation model that organizations can directly apply within their existing planning and ERP systems to manage product shortages more effectively. Global supply chains increasingly operate under persistent supply shortages driven by geopolitical disruptions, pandemics, and capacity constraints. During such periods, allocation decisions directly influence revenue realization, service continuity, and customer trust. From our research and simulation work, we observed that many organizations still rely on manual or rule-based allocation methods, which often lead to inconsistent decisions and lost revenue during shortages. In this paper, we present a prioritized revenue, AI driven global allocation framework designed to optimally distribute constrained inventory across regions. Based on our direct implementation and simulation experience, the proposed approach integrates demand forecasts, historical revenue contribution, and strategic location priorities into a mathematically grounded optimization model. We used simple weighting and proportional allocation so that the results are easy to understand, explain, and audit. Simulation results across multiple shortage scenarios show that the proposed method consistently improves revenue realization compared to traditional allocation approaches, while maintaining fairness and operational feasibility. Through this research, we provide a practical and implementable allocation model that organizations can directly apply within their existing planning and ERP systems to manage product shortages more effectively. In this paper, we present a prioritized revenue, AI driven global allocation framework designed to optimally distribute constrained inventory across regions. Based on our direct implementation and simulation experience, the proposed approach integrates demand forecasts, historical revenue contribution, and strategic location priorities into a mathematically grounded optimization model. We used simple weighting and proportional allocation so that the results are easy to understand, explain, and audit. Simulation results across multiple shortage scenarios show that the proposed method consistently improves revenue realization compared to traditional allocation approaches, while maintaining fairness and operational feasibility. Through this research, we provide a practical and implementable allocation model that organizations can directly apply within their existing planning and ERP systems to manage product shortages more effectively.