Citation

Nadia, Nusrat Yasmin and Arif, Md Habibul and Rabby, Habibor Rahman and Monzur Tanvir, Md Iftekhar and Hossen, Md Jakir and Mridha, M. F. (2026) Hybrid Deep Learning Approach for Coupled Demand Forecasting and Supply Chain Optimization. Computers, Materials & Continua, 88 (3). pp. 1-10. ISSN 1546-2226

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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 costefficient 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%.

Item Type:	Article
Uncontrolled Keywords:	Deep learning, textile industry, PPE manufacturing, hybrid framework
Subjects:	Q Science > Q Science (General) > Q300-390 Cybernetics
Divisions:	Faculty of Engineering and Technology (FET)
Depositing User:	Ms Rosnani Abd Wahab
Date Deposited:	04 Sep 2026 03:16
Last Modified:	04 Sep 2026 03:16
URII:	http://shdl.mmu.edu.my/id/eprint/16695

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