LEVERAGING DATA SCIENCE FOR PROCUREMENT COST FORECASTING AND SUPPLIER FINANCIAL HEALTH ASSESSMENT

We examine how analytical capability, supplier intelligence, and integrated data flows shape procurement decision effectiveness across global organizations. We use a multiyear dataset covering predictive analytics, machine learning for supplier assessment, real time integration, data readiness, and procurement outcomes drawn from the Global Top 250 Digital Procurement Leaders Database. We apply structured modelling to test how forecasting accuracy, supplier risk signals, and synchronized system updates improve cost stability, supplier continuity, and sourcing efficiency. We find that predictive analytics strengthens cost planning, machine learning improves early detection of supplier instability, and real time integration accelerates cycle performance. These effects grow stronger under high data quality and weaken when data environments contain gaps or inconsistencies. The work introduces an integrated pathway showing how analytical tools reinforce one another to form a coherent performance mechanism relevant to firms operating in volatile global markets. The results offer guidance for managers seeking stable decision systems, for policymakers advancing data governance, and for global practitioners aiming to build resilient procurement structures.