Leveraging Data-Driven Insights to Enhance Supplier Performance and Supply Chain Resilience
DOI:
https://doi.org/10.53469/wjimt.2024.07(05).15Keywords:
Artificial Intelligence, Supply Chain Performance, Supply Chain Resilience, Adaptive Capabilities, Predictive Analytics in Supply Chains, Supply Chain Volatility ManagementAbstract
The study examines how artificial intelligence (AI) enhances supply chain performance (SCP) and resilience (SCRes), with adaptive capabilities (AC) and supply chain collaboration (SCC) as mediators. Data from X firms were analyzed using structural equation modeling (SEM) to explore the relationships between AI, AC, SCC, supply chain dynamism (SCD), and supply chain outcomes. The results show that AI significantly improves SCP (β = 0.49, p < 0.01), leading to a 49% boost in performance, particularly in metrics like SLAs and on-time delivery. Moreover, AI’s effect on SCRes is amplified through AC (β = 0.71, p < 0.01), resulting in a 66% increase in resilience, while SCC strengthens resilience further (β = 0.68, p < 0.01) by 71%. AI also helps firms manage dynamic environments more effectively (β = 0.38, p < 0.01). These findings underscore AI's role in improving operational efficiency and building resilient supply chains. The study offers insights for firms to leverage AI, develop adaptive capabilities, and enhance collaboration for better performance and resilience.
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