B‑A‑G‑S: A Research Architecture for Counterfeit Prevention in Consumer Device Supply Chains

Abstract

The proliferation of counterfeit and unsafe consumer-electronics components poses escalating risks to public safety, industrial reliability, and national security. Traditional document-based audits, post-hoc recalls, and isolated certification programs have proven insufficient for the speed, opacity, and geographic dispersion of modern supply chains. This study introduces B-A-G-S, an integrated and privacy-preserving architecture that combines Blockchain, Artificial Intelligence, Geographic Information Systems, and Smart Contracts to create a continuous, verifiable, and adaptive system for detecting and mitigating counterfeit activities. Using simulation modeling informed by CPSC recall records, EU RAPEX alerts, CBP seizure statistics, and synthetic Shenzhen–Los Angeles trade flows, the framework was evaluated across three high-risk domains—unsafe power adapters, defective lithium-ion batteries, and counterfeit integrated circuits. Quantitative results demonstrate substantial improvements over conventional processes: Counterfeit Penetration Rate decreased from 35–45% to 5–7%, Time-to-Detection dropped from 60–90 days to 10–15 days, and the Recall Severity Index declined from 0.78 to 0.23, while maintaining acceptable operational overhead (+6%). Economic analysis shows a Cost-Benefit Ratio of 3.8:1, yielding positive returns within two years of deployment. These findings confirm that the synergistic combination of blockchain integrity, AI anomaly scoring, geospatial risk weighting, and adaptive smart-contract enforcement can transform counterfeit prevention from a reactive activity into a proactive, intelligence-driven infrastructure of trust. The work provides a scalable blueprint for regulators, industry consortia, and manufacturers seeking evidence-based, machine-verifiable compliance mechanisms aligned with emerging U.S. and international supply-chain security mandates.