AI-Powered Supply Chain Risk Intelligence for Consumer Protection in CPG Distribution Networks

Samuel Oladapo Taiwo and Obianuju O. Okosieme

International Journal of Scientific Research in Computer Science, Engineering and Information Technology, 2023, vol. 9, issue 6, 1008-1029

Abstract: The complexity of modern Consumer Packaged Goods (CPG) distribution networks requires risk intelligence that can anticipate disruptions and protect consumers through timely detection, traceability, and targeted intervention. This paper contributes an original integrative artifact the AI-Powered Supply Chain Risk Intelligence (SCRI) framework for consumer protection derived through a systematic literature review and thematic analysis of research from 2010 to 2023. The framework formalizes how multi-source risk signals (operational, IoT/quality telemetry, and external indicators) are transformed by an AI risk-intelligence layer comprising anomaly detection and risk sensing, predictive analytics, digital twin simulation, and real-time monitoring/control. It further provides a structured taxonomy linking AI capabilities to consumer-protection mechanisms, including recall precision, provenance transparency, product integrity assurance, and resilience-driven availability. In addition to synthesizing evidence on AI-enabled disruption detection and adaptive response, the paper reframes implementation barriers (data quality, organizational readiness, model interpretability) and ethical risks (privacy, bias, accountability) as design constraints for trustworthy deployment. The resulting contribution is a consumer-centric blueprint and research agenda for building explainable, governance-aligned AI systems that improve safety outcomes and strengthen trust across CPG distribution ecosystems.

Keywords: Supply Chain Risk Intelligence; AI-Enabled Consumer Protection; CPG Distribution Networks; Digital Twin & Predictive Analytics; Traceability and Product Integrity (search for similar items in EconPapers)
Date: 2023
Note: Article URL: https://ijsrcseit.com/CSEIT23906782
References: Add references at CitEc
Citations:

Downloads: (external link)
https://ijsrcseit.com/CSEIT23906782 Article URL (text/html)
https://ijsrcseit.com/paper/CSEIT23906782.pdf Full text (application/pdf)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:jbh:ijsrcs:v9:y2023:i6:id:hcseit23906782

DOI: 10.32628/CSEIT23906782

Access Statistics for this article

More articles in International Journal of Scientific Research in Computer Science, Engineering and Information Technology from International Journal of Scientific Research in Computer Science, Engineering and Information Technology
Bibliographic data for series maintained by Pankaj Sharma (USA) ().