Abstract
Artificial Intelligence (AI) is now accelerated by generative capabilities; it is at the forefront of discussion in nearly every part of the global economy. However, a consolidated view of its scholarly evolution and real-world hurdles remains sparse. Addressing this, this study positions two research questions: (1) What is the current state of AI adoption in manufacturing supply chains? and (2) What issues and concerns dominate the discourse? A two-phase systematic Literature Network Analysis (SLNA) has been employed to answer these questions. This study reviewed 680 relevant articles; eight thematic clusters emerged, ranging from AI-powered planning and smart logistics to sustainability, security, blockchain integration, and AI-enhanced decision systems. This reveals the transversal role of AI across strategic, tactical, and operational layers. While literature confirms performance gains in demand forecasting, inventory optimization, and risk mitigation, persistent barriers emerge from fragmented data infrastructures, change-management deficits, high entry costs for SMEs, and ethical‐algorithmic bias.
