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This paper introduces HAF-DS, a hybrid AI framework that couples LSTM-based demand forecasting with a mixed integer linear programming (MILP) optimization layer for supply chain management. The LSTM module learns temporal demand dependencies, while the MILP layer optimizes replenishment and allocation decisions to minimize costs. Experiments on textile and PPE datasets demonstrate that HAF-DS outperforms statistical and deep learning baselines, achieving a 14.7% reduction in MAE and a 5.4% decrease in inventory costs.
Integrating deep learning forecasting with MILP optimization slashes inventory costs by 5.4% and stockouts by 27.5% in textile and PPE supply chains.
Supply chain resilience and efficiency are vital in industries characterized by volatile demand and uncertain supply, such as textiles and personal protective equipment (PPE). Traditional forecasting and optimization approaches often operate in isolation, limiting their real-world effectiveness. This paper proposes a Hybrid AI Framework for Demand-Supply Forecasting and Optimization (HAF-DS), which integrates a Long Short-Term Memory (LSTM)-based demand forecasting module with a mixed integer linear programming (MILP) optimization layer. The LSTM captures temporal and contextual demand dependencies, while the optimization layer prescribes cost-efficient replenishment and allocation decisions. The framework jointly minimizes forecasting error and operational cost through embedding-based feature representation and recurrent neural architectures. Experiments on textile sales and supply chain datasets show significant performance gains over statistical and deep learning baselines. On the combined dataset, HAF-DS reduced Mean Absolute Error (MAE) from 15.04 to 12.83 (14.7%), Root Mean Squared Error (RMSE) from 19.53 to 17.11 (12.4%), and Mean Absolute Percentage Error (MAPE) from 9.5% to 8.1%. Inventory cost decreased by 5.4%, stockouts by 27.5%, and service level rose from 95.5% to 97.8%. These results confirm that coupling predictive forecasting with prescriptive optimization enhances both accuracy and efficiency, providing a scalable and adaptable solution for modern textile and PPE supply chains.
