THE USE OF BIG DATA IN LOGISTICS DECISION-MAKING

Abstract

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.