Multi dataset LSTM analysis for accurate and sustainable supply chain forecasting

Accurate demand forecasting is a critical enabler of sustainable Supply Chain Management, as it directly influences production planning, inventory control, resource utilization, and environmental impact. In alignment with the United Nations Sustainable Development Goals, particularly SDG 9 (Industry, Innovation and Infrastructure), SDG 12 (Responsible Consumption and Production), and SDG 13 (Climate Action), this study proposes a layered AI-based forecasting framework aimed at enhancing supply chain efficiency while supporting sustainable development objectives. The proposed framework integrates three progressively advanced models: a standard Long Short-Term Memory (LSTM) network, an LSTM with an Attention Mechanism, and a Quantum-inspired Attention LSTM, designed to capture long-term temporal dependencies and complex feature interactions inherent in real-world supply chain data. The framework is experimentally validated using three real-world supply chain datasets representing e-commerce, grocery retail, and customer analytics domains. Experimental results demonstrate that the Quantum-inspired Attention LSTM consistently outperforms classical LSTM and attention-based LSTM models, achieving forecasting accuracies of 92.85%, 92.56%, and 90.42%, respectively. Improved forecasting accuracy directly contributes to sustainability by reducing overproduction, minimizing excess inventory, lowering avoidable transportation and storage operations, and mitigating carbon emissions across supply chain networks. An ablation study further confirms that the incorporation of attention and quantum-inspired representations significantly enhances model robustness and predictive reliability. The findings illustrate that advanced AI-driven forecasting, when explicitly aligned with SDG-oriented objectives, can serve as a practical and scalable mechanism for enabling sustainable supply chain operations, supporting data-driven decision-making, and advancing responsible production and consumption practices.