The Role of AI in Supply Chain Optimization: Enhancing Efficiency through Predictive Analytics

Abstract

Aim: Recent innovations in artificial intelligence (AI) transform the supply chain management system and allow organizations to predict demand, regulate the stock, reduce risk, and optimize the logistics process, becoming more specific and quicker. The purpose of this study is to examine how end-to-end supply chain optimization through artificial intelligence (AI)-based predictive analytics can improve the accuracy of the forecast, inventory management, and logistics effectiveness.

Methods: Based on modern-day case evidence and measurements, the analysis will be directed towards quantifiable goals: 10 30% reductions in MAPE; 5-15% reductions in days-in-inventory; 10-20% reductions in logistics cost per order; and 4-8% reduction percentage payback in on-time-in-very-full and ROI of over 20%. The study adopted a comparative analytical design using secondary data from multiple industries to assess the effectiveness of AI-driven predictive models (gradient boosting, random forests, and LSTM) against classical time-series forecasting approaches.

Results: The findings show that companies with unified AI platforms, feature stores, MLOps pipelines, and balanced data models are binding significantly more adoption and return compared to the competitors, which use individual instruments, since the mutual data resource, lineage, and governance decelerate friction and aid learning speed.

Conclusion: The study concludes that predictive analytics, when integrated into unified AI platforms, enhances supply chain resilience and sustainability by converting real-time data into actionable insights for cost and service optimization.

Recommendation: The study proposes that organizations should embrace unified AI structures that have controlled data models to achieve optimal predictive analytics performance and scale across supply chain operations.