Revolutionizing Procurement: How AI & Data Analytics Driving Cost Effective Solutions in EPC

This paper, therefore, discusses the revolution brought by AI and Data Analytics in the procurement processes of EPC (Engineering, Procurement and Construction) and how each of the challenges, including cost control, enhanced efficiency and procurement downtime, could be solved using the two technologies. When the use of the procurement models is applied, the following benefits will be realized: supplier selection is facilitated using artificial intelligence, thereby automating the entire process; there is increased accuracy of costs compared to manual estimation, therefore reducing bias. The use of AI will allow better historical and real-time data analysis for procurement needs forecast, supplier evaluation and material cost prediction. Furthermore, machine learning algorithms can automate adjustments depending on real-time market variability, such for instance, a change in the price of the materials or a disruption in the supply chain, as well as execution flexibility regarding the project needs. Data Analytics also adds more weightage to procurement efficiency because it gives detailed analysis of the past and present procurement data. By utilizing state-of-the-art analytical frameworks, top managements are allowed to make powerful supplier analyses in relation to reliability, lead time, and cost of contracts prior to negotiations on contract supplier contracts and before contracting risks. What this study aims to do is to highlight real-life implementations of these technologies and how EPC firms can capitalize on these technologies to drive procurement efficiency, cost reduction, project delivery, and supplier relationship all for efficient project delivery. This paper thus provides a detailed discussion on the MI and TI of implementing AI and DA within procurement in the EPC sector via case studies and examples.