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Open Access

ARTICLE

Hybrid Deep Learning Approach for Coupled Demand Forecasting and Supply Chain Optimization

Nusrat Yasmin Nadia1, Md Habibul Arif2, Habibor Rahman Rabby3, Md Iftekhar Monzur Tanvir1, Md Jakir Hossen4,*, M. F. Mridha5
1 Department of Information Technology, Washington University of Science and Technology, Alexandria, VA, USA
2 Department of Information Technology, University of the Potomac, Washington, DC, USA
3 Department of Computer Science, Campbellsville University, Louisville, KY, USA
4 Center for Advanced Analytics (CAA), COE for Artificial Intelligence, Faculty of Engineering & Technology (FET), Multimedia University, Melaka, Malaysia
5 Department of Computer Science and Engineering, American International University-Bangladesh (AIUB), Dhaka, Bangladesh
* Corresponding Author: Md Jakir Hossen. Email: email

Computers, Materials & Continua https://doi.org/10.32604/cmc.2026.074236

Received 06 October 2025; Accepted 26 February 2026; Published online 17 June 2026

Abstract

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%.

Keywords

AI-driven supply chain; demand forecasting; supply chain optimization; deep learning; textile industry; PPE manufacturing; hybrid framework

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