In today’s increasingly complex and globalized supply chains, procurement responsiveness is a critical determinant of operational efficiency, cost management, and competitive advantage. Traditional procurement processes, often reliant on manual workflows and fragmented data systems, face challenges such as extended cycle times, delays in supplier response, and limited visibility into supply chain dynamics. These inefficiencies hinder timely decision-making, increase operational costs, and compromise overall supply chain performance. Big data approaches offer a transformative solution by enabling organizations to leverage large, diverse, and high-velocity datasets to enhance procurement responsiveness and optimize cycle times. Big data analytics integrates information from multiple sources, including enterprise resource planning (ERP) systems, supplier databases, transactional records, IoT-enabled logistics devices, market intelligence platforms, and social media feeds. Descriptive analytics provides insights into historical procurement performance, predictive analytics forecasts supplier lead times and demand fluctuations, and prescriptive analytics identifies optimal sourcing strategies and resource allocations. By analyzing these data streams, organizations can identify bottlenecks, anticipate potential disruptions, and streamline approval, ordering, and delivery processes, thereby significantly reducing procurement cycle durations. The applications of big data in procurement span multiple industries, including manufacturing, retail, logistics, and healthcare. In manufacturing, it ensures timely raw material availability for uninterrupted production. Retail and e-commerce benefit from responsive replenishment and inventory turnover. Logistics operations leverage predictive routing and supplier evaluation, while healthcare and pharmaceutical procurement relies on real-time monitoring for critical supplies and compliance adherence. Despite its transformative potential, implementing big data analytics in procurement presents challenges, such as data integration complexity, high implementation costs, workforce skill gaps, cybersecurity risks, and the potential for overreliance on automated insights. This examines how big data approaches can enhance procurement responsiveness through optimized cycle time reduction, emphasizing the strategic importance of data-driven decision-making. It highlights the need for organizations to invest in robust analytics infrastructure, workforce capability, and data governance frameworks to fully realize operational efficiency, supply chain agility, and sustained competitive advantage in a dynamic global market.
Akinlade et al. (Wed,) studied this question.