Blockchain for Real-Time Supply Chain Tracking with Predictive AI

Abstract

Globalized supply chains face persistent problems of poor visibility, delayed exception handling, counterfeit risk, and brittle planning under uncertainty. Real-time tracking is frequently hampered by siloed information systems and data integrity issues that erode trust among trading partners. This manuscript proposes a reference architecture that integrates permissioned blockchain ledgers with predictive AI to create a shared, tamperevident backbone for event data (e.g., EPCIS-compliant commissioning, aggregation, shipping, receiving, and sensor telemetry) and to enable continuous forecasting of estimated time of arrival (ETA), stockouts, and cold-chain excursions. The blockchain layer establishes data lineage, non-repudiation, and programmable compliance via smart contracts, while the AI layer ingests the same high-quality, time-stamped events to learn patterns and anticipate disruptions. We report results from a controlled simulation study reflecting 12 months of operations across four lanes Date of Publication: 10-06-2026 and two temperature-controlled product families. Relative to a baseline of conventional EDI and siloed databases, the integrated approach reduces ETA mean absolute error by 56%, halves stockout rates, cuts recall resolution time by 75%, and meaningfully lowers cold-chain excursions. The paper details data models, on-chain/off-chain partitioning, privacy controls (channels and selective disclosure), governance, and model lifecycle operations (MLOps) aligned to immutable audit trails. We also present a statistical summary of measured improvements and discuss practical deployment guidance, including standards alignment (GS1 EPCIS), interoperability, and organizational incentives. The findings suggest that combining blockchain’s shared truth with predictive AI’s anticipatory capabilities yields measurable operational gains and risk reduction in complex supply networks.