Theoretical Advancements in Logistics: Digitalization, Big Data, and Machine Learning in Decision Making

Abstract: This study conducts a systematic bibliometric analysis of the evolving role of digitalization, big data, and machine learning in shaping decision-making processes within logistics. As computational techniques become increasingly embedded in supply chain management, these technologies have redefined operational efficiency, enhanced predictive accuracy, and transformed logistics frameworks. The findings reveal a 57% annual growth rate in research output, highlighting the field's accelerated expansion and the widespread adoption of data-driven methodologies. The analysis identifies predictive analytics, artificial intelligence, and digital transformation as central thematic pillars, marking a paradigmatic shift from conventional logistics models to computationally driven decision-making frameworks. The geographical distribution of scholarly influence positions India, the United States, France, and China as leading contributors, reflecting the global research engagement in this domain. Moreover, international collaboration accounts for 23.48% of the dataset, underscoring logistics scholarship's interdisciplinary and transnational nature. Emerging research trajectories emphasize the increasing integration of blockchain, the Internet of Things, and Industry 4.0, reinforcing the growing reliance on automation and real-time analytics in supply chain optimization. Thematic clustering reveals distinct conceptual domains, including supply chain resilience, algorithmic logistics management, and sustainable digital logistics, reflecting a fundamental reconfiguration of theoretical perspectives in response to technological innovation. Despite these advancements, unresolved challenges related to data integrity, algorithmic transparency, and governance mechanisms in automated decision-making persist, warranting further critical inquiry. By systematically mapping the intellectual landscape of logistics research, this study provides a nuanced understanding of the discipline's evolving trajectory, identifying both dominant and emerging trends. The findings highlight the need for further exploration into the socioeconomic implications of digital logistics, particularly in emerging markets where computational methodologies are increasingly shaping supply chain strategies. As the field evolves, future research should prioritize refining predictive modeling techniques and assess the long-term implications of automation and data analytics on supply chain adaptability and resilience. The complete text is accessible at: Theoretical Advancements in Logistics: Digitalization, Big Data, and Machine Learning in Decision Making https://www.researchgate.net/publication/395890298_Theoretical_Advancements_in_Logistics_Digitalization_Big_Data_and_Machine_Learning_in_Decision_Making