Optimizing Supply Chain with Artificial Intelligence in Business

This literature review states the theoretical foundations, current advancements, practical applications, and technological tools of Artificial Intelligence (AI) in supply chain management (SCM). The review also describes the outcomes from recent studies that shows Artificial intelligence’s evolution from a theoretical part to a transformative enabler of adaptive, data-driven supply chains. The theory illustrates AI’s roots in decision sciences, systems theory, and hybrid analytical models, which effect strategic planning under uncertainty. Besides that, current developments describe AI’s effects in predictive analytics, risk management, and real-time decision-making. It is very much crucial for this study. On the other hand, case studies from various sectors such as food logistics, production, and retail demonstrate tangible improvements in efficiency, resilience, and consumer satisfaction. Some excellent AI technology such as machine learning, NLP, IoT, and cloud-based platforms, noting their effects on operational excellence. Besides that, persistent challenges such as cost, infrastructure gaps, data silos, and limited sustainability focus continue to constrain extensive use. This review proclaims the demand for broad base AI strategies that clarify technical and organizational obstacles, clearing the way for more excellent and permanent supply chain management. The integration of advanced technologies in supply chain management has led improvements significantly across key performance indicators. Some peer reviewed articles showed that, forecast accuracy increased from 67–70% to 89–92% (Alomar, 2022; Wong et al., 2024; Abaku et al., 2024; Khoa et al., 2024), according to Fosso Wamba et al., 2022; Helo & Hao, 2022; Grover, 2025 inventory turnover rose from 4–5 to 5–6 times per year. Cost reduction ranged from 10–20%, while delivery time decreased by 15–25% (Hasan et al., 2024; Shamsuddoha et al., 2025; Eyo-Udo, 2024; Khan & Jalal, 2023). and on-time delivery improved by 10–18% (Thuraka, 2021; Vaka, 2024; Attah et al., 2024; Fatorachian, 2024). So, stock-out incidents were reduced by 15–30%, and consumer satisfaction increased by 18–22%.