The growing environmental and economic pressures on manufacturing industries demand smarter systems that close material loops before waste leaves the factory gate. This study presents a Hybrid AI-IoT Integrated Framework for optimizing the reverse supply chain of pre-consumer waste within manufacturing environments, emphasizing the Ready-Made Garment (RMG) sector as an application domain. The framework unites artificial intelligence, internet-of-things sensing, and blockchain-based traceability to build an adaptive, data-driven ecosystem for sustainable internal logistics.

Methodologically, the framework couples AI-driven predictive analytics with multi-objective optimization algorithms that minimize waste-handling cost, transport distance, and energy consumption while quantifying carbon-equivalent impacts. Real-time IoT data streams—sourced from production lines, material bins, and internal transfer vehicles—feed a reinforcement-learning loop that continuously refines recovery routes and material allocation. A blockchain ledger secures traceability across every reverse-flow event, enabling transparent ESG reporting and auditable circular-economy metrics.

Validation through a pilot-ready simulation in an RMG facility indicates measurable improvements: up to 22 % reduction in internal logistics cost, 18 % lower energy use, and 15 % decrease in CO₂ emissions, without compromising throughput. The findings confirm that AI-IoT-enabled reverse logistics can transform pre-consumer waste into a profitable sustainability lever, positioning data-intelligent circularity as a new competitive advantage in manufacturing supply chains.

Md. Tanvirul Islam

Affiliation

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Hybrid AI-IoT Integrated Framework for Sustainable Reverse Supply Chain Optimization: Advancing Pre-Consumer Circularity and Traceability in Manufacturing