A Decision-Centric IoT and Blockchain-Enabled Supply Chain Information System for Demand-Aware Routing: Architecture and Multi-Seed Evaluation

Supply chain information systems increasingly need to support decisions, yet Internet of Things (IoT) and blockchain approaches remain largely data-centric. We propose a decision-centric system in which IoT data acquisition, an artificial intelligence decision engine and a blockchain layer form one closed loop: each daily routing plan is optimized jointly for cost, lateness and delivered demand, validated against shared partner constraints and recorded before execution. We evaluated it by simulation on the supply chain logistics problem dataset (41 real customers) over 30 paired seeds. A learned deep Q-network engine with decision-time rollout reduced cost per 1000 units delivered by 24.3% relative to a demand-aware heuristic and 28.0% relative to OR-Tools, deciding in 43 ms; seeding adaptive large neighborhood search (ALNS) with its plan gave the lowest cost, 28.0% below the heuristic and below ALNS alone in 29 of 30 seeds. Joint routing–demand optimization lowered cost per unit by 33–45% relative to a cost-only objective. Under hard time windows, a heterogeneous fleet and eleven other scenarios, rollout stayed 12–32% cheaper than the heuristic in all 130 paired runs, and seeded ALNS was cheapest in every setting. Evidence-based validation blocked every invalid plan with up to four of five validators faulty.