Model for Data Driven Vendor Evaluation and Bid Selection Using Geospatial Intelligence

Abstract

Effective vendor evaluation and bid selection are critical to the operational efficiency, risk management, and cost optimization of infrastructure and energy projects. Traditional procurement methods often rely on subjective assessments, incomplete data, and limited consideration of spatial factors, which can result in suboptimal supplier selection, logistical challenges, and heightened project risks. This presents a data-driven model for vendor evaluation and bid selection leveraging geospatial intelligence (GEOINT) to enhance decision-making in complex and high-risk project environments. The proposed model integrates geospatial data with conventional supplier performance metrics, financial indicators, and compliance records to provide a comprehensive assessment framework. Key components include data acquisition and preprocessing, which consolidate supplier databases and spatial datasets such as location coordinates, transportation networks, and environmental hazards; geospatial risk and accessibility analysis, which evaluates logistical constraints, transport risks, and regional vulnerabilities; and vendor evaluation metrics, which combine performance, compliance, and geospatial indicators into a weighted scoring system. The model further incorporates bid selection and optimization, integrating vendor scores with bid pricing and contract terms to identify suppliers offering the best balance of cost, reliability, and risk exposure. Advanced visualization and scenario analysis tools facilitate decision support, enabling procurement officers to anticipate potential disruptions and optimize supplier networks. By embedding geospatial intelligence into the vendor evaluation process, the framework enhances supply chain transparency, reduces operational and logistical risks, and supports strategic supplier management. The model also provides measurable insights for compliance, regulatory reporting, and project planning, offering a scalable approach adaptable to multiple sectors, including energy, construction, and critical infrastructure. This data-driven, geospatially enabled model represents a significant advancement in procurement practice, providing a systematic, objective, and risk-informed approach to vendor selection and bid evaluation. Its adoption can strengthen operational resilience, optimize costs, and improve decision-making across high-stakes project environments.

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