THE USE OF BIG DATA IN LOGISTICS DECISION-MAKING - Kalbinur Makhamataliyeva, (TADQIQOTLAR, 2026)
Abstract The global logistics industry, valued at $10.17 trillion in 2024, is undergoing one of the most consequential transformations in its history — a shift from intuition-driven,  reactive  decision-making  toward  a  paradigm  of  data-intensive,  predictive intelligence. At the center of this transformation is big data: the vast, heterogeneous, high-velocity streams of information generated across modern supply chains by GPS devices,  RFID  sensors,  electronic  commerce  platforms,  weather  systems,  financial instruments, and customer interaction records. This article examines the theoretical architecture and practical application of big data analytics in logistics decision-making. Drawing on evidence from industry leaders including UPS, DHL, Amazon, Walmart, and  Maersk,  as  well  as  market  data  from  Global  Market  Insights,  McKinsey,  and Gartner,  the  article  demonstrates  that  big  data  creates  measurable  competitive advantage across five core decision domains: route and network optimization, demand forecasting, inventory management, predictive maintenance, and customer experience personalization. The article further examines the structural challenges that constrain the  realization  of  big  data's  full  potential  —  including  data  fragmentation, cybersecurity  vulnerabilities,  talent  scarcity,  and  regulatory  complexity  —  and identifies  the  policy  and  managerial  conditions  necessary  for  their  resolution.  The article  concludes  that  in  the  logistics  sector,  the  transition  to  data-driven  decision-making  is  no  longer  a  strategic  option  but  a  structural  imperative,  and  that  those organizations that treat big data as a peripheral enhancement rather than a foundational operating capability will find themselves at an accelerating competitive disadvantage.