AI-Driven Supply Chain Risk Management in the Manufacturing Sector: Tackling Data Bias, Ensuring Algorithmic Transparency, and Enhancing Human-AI Collaboration

• Author(s): Achu Obinna ; Elijah Kayode Adejumo ; Samuel Yaw Larbi
• Paper ID: 1708194
• Page: 77-94
• Published Date: 03-05-2025
• Published In: Iconic Research And Engineering Journals
• Publisher: IRE Journals
• e-ISSN: 2456-8880
• Volume/Issue: Volume 8 Issue 11 May-2025

Abstract

This paper stems from the revolutionary changes that are currently observed regarding the integration of artificial intelligence (AI) into manufacturing supply chains as measures to enhance efficiency, ures to optimizing logistics, and mitigate risks. This study throws light on AI-driven risk management, especially its applications in predictive analytics, real-time risk detection, and logistics automation. It examines the challenges associated with AI adoption, including data bias, lack of transparency, and resistance to automation. Case studies of successful AI implementations, such as predictive maintenance and warehouse automation, are presented alongside instances of AI failures, while lessons learned are also presented and analysed. Strategies for mitigating bias, improving explainability, and fostering human-AI collaboration are discussed, with a focus on regulatory and ethical considerations. The study also identifies emerging AI technologies that will shape the future of supply chain management. Findings suggest that while AI enhances resilience and efficiency, addressing bias, ensuring transparency, and fostering human oversight are critical for sustainable AI adoption. The study recommends policy interventions, workforce training, and further research on AI fairness and explainability to maximize AI’s potential in supply chain management.

Keywords

Algorithmic transparency, AI-driven supply chains, human-AI collaboration, predictive analytics, risk management

Citations

IRE Journals:
Achu Obinna , Elijah Kayode Adejumo , Samuel Yaw Larbi "AI-Driven Supply Chain Risk Management in the Manufacturing Sector: Tackling Data Bias, Ensuring Algorithmic Transparency, and Enhancing Human-AI Collaboration" Iconic Research And Engineering Journals Volume 8 Issue 11 2025 Page 77-94

IEEE:
Achu Obinna , Elijah Kayode Adejumo , Samuel Yaw Larbi "AI-Driven Supply Chain Risk Management in the Manufacturing Sector: Tackling Data Bias, Ensuring Algorithmic Transparency, and Enhancing Human-AI Collaboration" Iconic Research And Engineering Journals, 8(11)