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<p>The increasing complexity and volatility of global supply chains have accelerated the adoption of Big Data technologies in logistics, enabling the transition from reactive operations toward proactive and data-driven decision-making systems. In this context, this study presents a systematic literature review (SLR) based on a PRISMA inspired methodology to analyze the evolution, applications, and operational impact of Big Data in logistics and supply chain management between 2010 and 2024. The review synthesizes research across thirteen strategic logistics domains, examining the role of advanced analytical technologies such as machine learning, Internet of Things (IoT) sensor integration, predictive analytics, and blockchain in applications including route optimization, predictive maintenance, demand forecasting, warehouse automation, and supply chain resilience. To complement the literature review, text mining techniques were applied to the abstracts of the selected studies to identify thematic patterns and emerging research trends within the field. The findings indicate that Big Data technologies contribute significantly to improvements in operational efficiency, logistics visibility, forecasting accuracy, agility, and customer-oriented decision-making. However, the analysis also reveals persistent challenges associated with data governance, cybersecurity, algorithmic transparency, infrastructure requirements, and the limited digital maturity of small and medium enterprises (SMEs). Based on the reviewed literature, the study proposes an integrated conceptual framework linking technological evolution, analytics maturity, and data-driven logistics applications. Additionally, the paper develops a research agenda focused on the transition toward smart and green logistics, emphasizing the need for more research on sustainability integration, ethical data management, and resilient digital supply chains. The proposed framework and methodological integration of PRISMA-based review procedures with text mining analysis constitute the main contribution of this research, providing both scholars and practitioners with a structured roadmap for understanding the evolution and future direction of Big Data–enabled logistics systems.</p>

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Impact and Evolution of Big Data in Logistics: A Review of Current Trends and Practices

  • 期刊论文

  • 发表日期：

    2026年7月1日

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  • Dicsiel Cordoves Mustelier,
  • Mariano Frutos

摘要

The increasing complexity and volatility of global supply chains have accelerated the adoption of Big Data technologies in logistics, enabling the transition from reactive operations toward proactive and data-driven decision-making systems. In this context, this study presents a systematic literature review (SLR) based on a PRISMA inspired methodology to analyze the evolution, applications, and operational impact of Big Data in logistics and supply chain management between 2010 and 2024. The review synthesizes research across thirteen strategic logistics domains, examining the role of advanced analytical technologies such as machine learning, Internet of Things (IoT) sensor integration, predictive analytics, and blockchain in applications including route optimization, predictive maintenance, demand forecasting, warehouse automation, and supply chain resilience. To complement the literature review, text mining techniques were applied to the abstracts of the selected studies to identify thematic patterns and emerging research trends within the field. The findings indicate that Big Data technologies contribute significantly to improvements in operational efficiency, logistics visibility, forecasting accuracy, agility, and customer-oriented decision-making. However, the analysis also reveals persistent challenges associated with data governance, cybersecurity, algorithmic transparency, infrastructure requirements, and the limited digital maturity of small and medium enterprises (SMEs). Based on the reviewed literature, the study proposes an integrated conceptual framework linking technological evolution, analytics maturity, and data-driven logistics applications. Additionally, the paper develops a research agenda focused on the transition toward smart and green logistics, emphasizing the need for more research on sustainability integration, ethical data management, and resilient digital supply chains. The proposed framework and methodological integration of PRISMA-based review procedures with text mining analysis constitute the main contribution of this research, providing both scholars and practitioners with a structured roadmap for understanding the evolution and future direction of Big Data–enabled logistics systems.