Improving Supply Chain Resilience through Predictive Analytics and Real-Time Data Integration - Prem Narayan Ahirwar (International Journal of Industrial and Product Design Engineering, 2025)
Demand forecasting has under gone major changes because to the incorporation of automated analytics into supply chain management (SCM), which has improved company productivity, accuracy, and responsiveness. Central to this transformation is the application of machine learning (ML), which enables the analysis of large and complex datasets to identify patterns, detect trends, and generate precise forecasts. Conventional methods for predicting frequently rely on linear models and historical sales data, which can be difficult to change to dynamic and quickly shifting market conditions. Inefficiencies including overstocking, stockouts, and interrupted supply chain procedures are often caused by this restriction. AI-driven prediction models, on the other hand, provide far more precise and flexible projections by combining historical data with current market signals and external factors like consumer behavior, changes in the season, and economic indicators. In the framework of contemporary supply chain systems, this research investigates the breakthrough significance of machine learning in demand anticipating. It specifically looks to how machine learning (ML) improves inventory optimization, lowers uncertainty, and enhances data-driven strategic decision-making. The paper also looks at critical implementation issues such data quality, system integration difficulties, and ethical concerns like algorithmic openness and data sovereignty. Despite the obvious positives, ML adoption necessitates thorough preparation, a strong computer system, and a trained employees. By reviewing current academic research, real-world industrial applications, and case studies, this paper highlights best practices for leveraging ML in demand forecasting. Ultimately, the paper underscores how predictive analytics can foster resilience and sustainability within supply chains, offering a competitive advantage in an increasingly complex and volatile global market.
Keywords: Inventory management; Machine learning (ML); demand forecasting; predictive analytics; supply chain management (SCM)