Revolutionizing Supply Chains with Artificial Intelligence and Machine Learning

This chapter presents a conceptual model for embedding artificial intelligence (AI) and machine learning (ML) tools across each supply chain function, with the specific goals of boosting visibility, sharpening forecasts, improving choices and strengthening risk defences. Drawing on resource-based view and dynamic capabilities theory, the model maps the resources, routines and performance gains that accompany smart-technology adoption. In addition to advancing scholarly discourse, the chapter seeks to direct researchers and managers toward practical research directions by putting forth testable hypotheses and suggesting proof-of-concept locations in digitally mature supply chains. The conceptual model is organized in three distinct yet interconnected layers. They are AI/ML capabilities/input layer, supply-chain management functional integration/process layer/mediators, and performance outcomes/output layer. Surveys, combined with structural-equation modeling, are well suited to examine the model's predictive power and to reveal how context alters its structures.