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.