Leveraging Big Data and Business Intelligence for Optimization of Manufacturing Sector Procurement

Procurement within the manufacturing sector has undergone fundamental transformation, driven by the convergence of globalization, digitalization, and competitive pressures. Traditional procurement approaches, while adequate in the past, are increasingly challenged by complexity, volatility, and the need for cost efficiency and agility. Big Data and Business Intelligence (BI) have emerged as pivotal enablers of procurement optimization, offering real-time insights, predictive analytics, and enhanced decision-making capabilities. This paper undertakes a comprehensive review of literature up to 2020, synthesizing how Big Data and BI tools have been conceptualized, applied, and evaluated in manufacturing procurement contexts. The study traces the evolution from conventional, transaction-focused procurement models to data-driven frameworks capable of handling large, diverse, and fast-moving datasets. It highlights how advanced analytics supports supplier evaluation, demand forecasting, risk management, cost control, and sustainability monitoring. By consolidating insights from multiple disciplines including supply chain management, information systems, and operations research this paper provides a structured foundation for understanding the potential and challenges of Big Data and BI in procurement optimization. The analysis underscores key issues of data quality, integration, organizational readiness, and cultural alignment, while identifying opportunities for future research and industrial practice.