Artificial intelligence-driven risk management for enhancing supply chain agility: A deep-learning-based dual-stage PLS-SEM-ANN analysis

This study posits that the use of artificial intelligence (AI) enables supply chains (SCs) to dynamically react volatile environments, and alleviate potentially costly decision-makings for small-medium enterprises (SMEs). Building on a resource-based view, this work examines impact AI SC risk management SMEs. A structural model comprising AI-risk capabilities, re-engineering capabilities chain agility (SCA) was developed tested based data collected from executives, managers senior SMEs The main methodological approach used in is partial least squares-based equation modelling (PLS-SEM) neural network (ANN). results identified influences agility. Re-engineering further affect mediate PLS-SEM ANN were compared revealed consistency models B. Current levels demand uncertainties challenges making complex trade-off decisions require huge resources very limited time. With AI, it possible various scenarios answer crucial questions archaic infrastructures are not able to. combines multi-construct concept non-linear relationships model.