The Role of MuleSoft in AI-Enhanced Predictive Demand Forecasting for Supply Chain Optimization

Gopigari, Vinay Sai Kumar Goud (2025) The Role of MuleSoft in AI-Enhanced Predictive Demand Forecasting for Supply Chain Optimization. World Journal of Advanced Research and Reviews, 26 (2). 061-080. ISSN 2581-9615

This article examines the transformative role of MuleSoft in enabling AI-enhanced predictive demand forecasting for supply chain optimization. Beginning with an overview of the evolution from traditional forecasting methods to sophisticated AI-powered approaches, the discussion progresses through MuleSoft's API-led connectivity framework and its critical function in integrating diverse data sources. The integration architecture facilitates seamless connections between enterprise systems and external variables while enabling real-time data synchronization. The implementation of AI models through MuleSoft creates pathways for processing historical sales data and deploying predictive capabilities within various supply chain contexts. These integrated systems drive automated inventory optimization and support cross-functional decision-making with measurable performance metrics. Industry-specific implementations across retail, consumer packaged goods, industrial manufacturing, and pharmaceutical sectors demonstrate the adaptability of this article, while emerging technologies like federated machine learning, digital twins, and knowledge graphs point toward future opportunities. Addressing current technical and organizational challenges will further advance the integration of predictive forecasting into resilient supply chain operations.