AI-Driven Self-Healing Supply Chains and the Future of Logistics Automation

Abstract

This study examines how artificial intelligence (AI) enables self-healing capabilities in global supply chains and reshapes logistics automation. Employing a qualitative multi-case study design, data were collected through 42 semi-structured interviews across six multinational organisations in manufacturing, retail, and third-party logistics. Thematic analysis yielded four themes: AI-enabled disruption sensing; autonomous reconfiguration mechanisms; human- AI collaboration and workforce transformation; and ethical and governance considerations. Findings demonstrate that self-healing capability emerges from the dynamic interplay between AI infrastructure, adaptive organisational routines, and human agency - not from technology alone. Organisations embedding AI within a dynamic capabilities’ framework achieve measurable reductions in disruption recovery time and operational costs. A grounded theoretical model of AI-driven supply chain self-healing is proposed, integrating dynamic capabilities theory and complex adaptive systems theory.
Practical implications for supply chain leaders and directions for future mixed-methods research are discussed.