The increasing complexity of global supply chains and project-based operations has necessitated the adoption of data-driven decision-making frameworks to enhance efficiency, resilience, and competitiveness. Traditional decision-making approaches, which often rely on intuition and historical practices, are increasingly insufficient in addressing the dynamic and uncertain environments characterizing modern supply chain systems and project scheduling processes. This review article examines the conceptual foundations, methodologies, and practical implications of data-driven decision-making in supply chain management and project scheduling.
The study synthesizes existing literature on advanced analytics, including descriptive, predictive, and prescriptive models, and evaluates their roles in optimizing inventory control, demand forecasting, resource allocation, and scheduling efficiency. Particular attention is given to the integration of big data technologies, machine learning algorithms, and real-time data streams in enhancing operational decision-making. The paper further explores the interplay between supply chain coordination and project scheduling, emphasizing how data-driven insights can improve synchronization across multiple operational layers.
Additionally, the review identifies key challenges associated with data quality, system integration, organizational readiness, and ethical considerations in data utilization. The study proposes a conceptual framework that integrates data analytics capabilities with decision-support systems to improve both strategic and operational outcomes. The findings highlight that organizations adopting data-driven approaches demonstrate improved responsiveness, reduced operational risks, and enhanced performance metrics.
This article contributes to the growing body of knowledge by providing a comprehensive synthesis of current trends and identifying research gaps in the application of data analytics to supply chain and project scheduling domains. It also offers practical recommendations for organizations seeking to transition toward data-centric decision-making models.