ARTIFICIAL INTELLIGENCE-ENABLED SUPPLY CHAIN RESILIENCE FOR DISRUPTION MANAGEMENT

Supply-chain disruptions caused by cyber attacks, natural disasters, geopolitical events, labor interruptions, factory incidents, and transportation constraints can affect operational continuity, financial performance, and recovery. Artificial intelligence (AI) provides opportunities to analyze complex disruption patterns and support more responsive supply-chain resilience strategies. This study aimed to examine relationships between disruption characteristics, supplier conditions, operational consequences, and recovery outcomes and to develop an AI-enabled analytical framework for supporting supply-chain disruption management. A structured dataset containing disruption records was analyzed using descriptive and comparative approaches to identify patterns relevant to AI-enabled disruption management. The analysis examined disruption type, severity, production impact, revenue loss, response time, recovery duration, supplier tier, backup-supplier availability, and permanent supplier changes. Comparative tables and graphical analyses were used to identify patterns in disruption consequences and resilience outcomes. Cyber attacks demonstrated the greatest overall operational and recovery consequences among the examined disruption categories. Increasing disruption severity was associated with greater production impact, financial losses, longer response requirements, and extended recovery periods. Organizations with backup suppliers generally demonstrated shorter recovery periods across supplier tiers, indicating the importance of supplier redundancy in disruption preparedness and recovery. The findings demonstrate that disruption characteristics and supplier resilience mechanisms are important considerations for intelligent supply-chain management. An AI-oriented analytical framework can support disruption classification, risk prioritization, contingency planning, response coordination, and recovery decision-making within complex supply networks.