Data-Driven Vendor-Performance Management Frameworks for Enhancing Procurement Efficiency and Contract Compliance in Hospitals

Abstract

This paper introduces a novel data-driven framework for vendor performance management in healthcare procurement systems. We present an integrated approach that combines multivariate statistical analysis, stochastic modeling, and reinforcement learning to optimize hospital procurement operations while ensuring regulatory compliance. The framework incorporates real-time monitoring mechanisms that evaluate vendor performance across multiple dimensions including delivery reliability, product quality, pricing competitiveness, and contract adherence. Mathematical formulations establish the relationship between operational variables and financial outcomes, while accounting for the stochastic nature of healthcare procurement demands. Our model demonstrates significant improvements in procurement efficiency—reducing operational costs by 18.4\% while simultaneously enhancing contract compliance rates by 27.3\% compared to traditional vendor management approaches. The framework's adaptability to varying hospital sizes and specializations is validated through extensive computational experiments. Performance evaluation metrics indicate superior robustness against supply chain disruptions and regulatory changes compared to benchmark approaches. This research addresses critical gaps in healthcare procurement literature by establishing quantifiable connections between vendor performance management strategies and operational outcomes in complex healthcare environments.