Integrated Vendor Performance Evaluation Model for Strengthening Procurement Accuracy in University Operations

Universities depend on complex procurement systems that span academic departments, administrative units, campus services, and capital projects, yet vendor performance evaluation practices often remain fragmented, inconsistent, and weakly aligned with institutional goals. This paper proposes an Integrated Vendor Performance Evaluation Model designed to strengthen procurement accuracy, reduce operational risks, and improve value-for-money outcomes in university operations. The model integrates quantitative scoring, qualitative assessments, and predictive analytics into a unified architecture that captures the multidimensional nature of vendor contributions across cost, quality, timeliness, compliance, sustainability, and service responsiveness. Drawing from procurement analytics research, supplier governance theory, and higher-education operational requirements, the framework aligns data from purchase orders, delivery logs, service-level agreements, digital workflows, user feedback, and audit trails to produce a holistic and traceable evaluation record. A multi-criteria decision analysis layer is incorporated to ensure transparent weighting of indicators, while anomaly detection and exception-flagging algorithms enhance the early identification of delivery discrepancies, underperformance trends, and compliance breaches. The model also embeds a continuous improvement loop that links vendor performance insights to contract renewal decisions, category strategies, and procurement planning cycles. Pilot simulations demonstrate that integrating structured scoring with workflow-derived evidence significantly improves prediction accuracy for vendor reliability and reduces inconsistencies caused by subjective or siloed evaluations. The model supports scenario analysis for strategic sourcing, enabling procurement officers to compare vendors across varying risk and performance thresholds. It further incorporates ESG indicators relevant to modern universities, including environmental footprint, ethical sourcing, diversity commitments, and alignment with campus sustainability targets. By consolidating procurement data streams, instituting standardized evaluation protocols, and embedding analytics-driven quality assurance checks, the proposed model reinforces internal controls, enhances accountability, and ensures that procurement outcomes more accurately reflect vendor capabilities. The study concludes that universities seeking operational resilience and resource optimization can benefit significantly from adopting an integrated vendor performance evaluation system. Future work should extend predictive components, explore machine-learning–based contract risk scoring, and test interoperability with enterprise resource planning (ERP) platforms to enable real-time evaluation at scale.