Citation

Hossain, Kazi Md Shahadat and Ahad, Md Abdul and Akhtar, Nahin and Nabil, Ashrafur Rahman and Amin, Md Ruhul and Sarker, Md Tanjil and Abdul Karim, Hezerul (2025) AI-Driven Supply Chain Optimization: A Hybrid CNN-BiLSTM and Financial Strategy Framework. In: 2025 Multimedia University Engineering Conference, MECON 2025, 21 July 2025 - 23 July 2025, Cyberjaya, Malaysia.

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Abstract

The complexity of U.S. supply chains, driven by globalization, ESG compliance demands, and market volatility, necessitates a transition to more intelligent and resilient operations. This study introduces an integrated framework that synergizes data-driven infrastructures, hybrid AI models (CNN-BiLSTM), and advanced financial strategies to optimize supply chain performance. Leveraging over 120 empirical studies and extensive case simulations, the framework achieves a demand forecast accuracy of 96.60%, reduces inventory and transportation costs, enhances routing efficiency, and lowers carbon emissions. Financially, AI integration yields a 154% ROI within six months and reduces disruption cost exposure by 37.5%. Methodologies include BiLSTM for demand forecasting, CNN for routing optimization, and Deep Q-Learning for inventory management, all integrated into a unified supply chain management platform. Results confirm significant operational improvements: transportation costs and inventory holding costs were cut by 12% and 14% respectively, while service levels increased by over 7%. This research presents a scalable, AI-driven model for building next-generation supply chains that are predictive, cost-efficient, and aligned with sustainability goals.

Item Type:	Conference or Workshop Item (Paper)
Uncontrolled Keywords:	Supply chain optimization, artificial intelligence (AI)
Subjects:	Q Science > QA Mathematics > QA71-90 Instruments and machines Q Science > QH Natural history
Divisions:	Faculty of Artificial Intelligence & Engineering (FAIE)
Depositing User:	Ms Rosnani Abd Wahab
Date Deposited:	17 Mar 2026 06:35
Last Modified:	06 Apr 2026 04:04
URII:	http://shdl.mmu.edu.my/id/eprint/15487

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