PREDICTIVE ANALYTICS IN SUPPLY CHAIN MANAGEMENT A REVIEW OF BUSINESS ANALYST-LED OPTIMIZATION TOOLS

Abstract

This quantitative study investigates the transformative role of predictive analytics in optimizing supply chain management (SCM) processes through business analyst-led decision frameworks. Predictive analytics, which integrates statistical modeling, machine learning, and data mining techniques, enables organizations to forecast demand, anticipate disruptions, and enhance operational efficiency across global logistics networks. The research examined 150 operational sites across manufacturing, retail, and logistics sectors using a quasi-experimental design to quantify the impact of predictive analytics adoption and business analyst mediation on performance indicators such as forecast accuracy, inventory turnover, fill rate, lead-time variability, and cost efficiency. Descriptive and inferential analyses revealed that predictive-adopting organizations achieved a 40% improvement in forecasting precision, a 47% reduction in lead-time variability, and over a 30% decrease in cost-to-serve compared with non-adopting firms. Regression and mediation analyses confirmed that predictive maturity significantly enhanced key performance indicators, while the Analyst Mediation Index (AMI) partially mediated the relationship between predictive analytics sophistication and operational outcomes, validating the critical interpretive role of analysts in translating algorithmic insights into strategic actions. The results demonstrated that predictive analytics maturity, when supported by digital connectivity and cloud-based integration, yields substantial gains in decision intelligence, service reliability, and financial performance. The study concludes that predictive analytics functions not only as a technological instrument but also as a managerial paradigm in which human analytical competence and data-driven foresight converge to produce measurable competitive advantage. Recommendations emphasize the institutionalization of analytical governance, development of business analyst competency frameworks, and continuous model validation to sustain predictive performance and organizational adaptability within dynamic global supply chains.