The Role of Data Analytics in Procurement and Supply Chain Optimization: A Case Study Approach

Abstract

Data analytics has emerged as a transformative force in procurement and supply chain management, enabling organizations to make data-driven decisions that enhance efficiency, reduce costs, and mitigate risks. This case study approach explores how predictive analytics and AI technologies are reshaping procurement processes by providing insights into supplier performance, demand forecasting, and market trends. Companies leveraging these tools can identify inefficiencies, uncover hidden costs, and respond swiftly to supply chain disruptions. The integration of real-time data analytics facilitates agile decision-making, allowing organizations to adapt to changing market conditions effectively. Moreover, data analytics plays a crucial role in supplier risk assessment by continuously monitoring key metrics such as financial health and compliance. This proactive approach empowers procurement teams to mitigate potential disruptions before they escalate. As organizations increasingly rely on data analytics, they gain a competitive edge by optimizing their procurement strategies and enhancing collaboration across the supply chain. This study highlights the critical importance of adopting advanced analytical tools to drive innovation and improve overall supply chain performance