Abstract

The complexity of Request for Quotation (RFQ) processes in power distribution and data center infrastructure projects poses significant challenges, including inefficiencies, human errors, and prolonged timelines. This paper introduces a data-driven model designed to automate RFQ workflows, leveraging automation, digital tools, and structured playbooks. The model streamlines procurement processes, reduces errors, and enhances efficiency by integrating advanced technologies such as machine learning and natural language processing. Real-world applications, pilot projects, and simulations demonstrate substantial improvements, including a 40% reduction in processing time and significant error minimization. This study also highlights the model's scalability and adaptability to varying project sizes and complexities. Limitations, such as initial costs and data dependency, are discussed alongside stakeholder recommendations to ensure successful implementation. Future research directions are proposed, focusing on sustainability metrics and emerging technologies like blockchain to enhance transparency and efficiency. The findings underscore the transformative potential of automated RFQ models in modernizing procurement practices for critical infrastructure projects.