Using Big Data Analytics to Improve Logistics Processes and Forecast Demand

This study is relevant due to the increasing necessity to enhance the efficiency of logistics operations amidst rapid digitalization and globalization of markets. Our research aims to investigate the application of Big Data analytics in optimizing logistics processes and demand forecasting. The study employed diverse methodologies such as literature review, systematic terminology development, surveys, and graphical and tabular data visualization techniques. It underscores that Big Data analytics in supply chains involves leveraging various tools, processing systems, and algorithms to analyze voluminous data effectively. Traditionally, supply chain management (SCM) systems rely on ERP and other separate systems for data storage. However, the advent of analytics enables data integration, fostering more efficient decision-making processes. Big Data is profoundly impacting every facet of the supply chain. During the planning phase, integrated data across the network enhances demand prediction through statistical models. The results revealed that many Ukrainian logistics firms have begun adopting Big Data analytics. Specifically, they are leveraging it to optimize delivery routes, manage inventory based on demand forecasts, and evaluate warehouse operations' efficiency. This initiative contributes to cost savings in transportation, enhances forecasting accuracy by 10-15%, and reduces delivery times by a similar margin. However, the study underscores the challenges associated with managing large datasets and emphasizes the critical need for well-trained staff to effectively handle these analytics. Despite these challenges, most companies recognize Big Data analytics as pivotal in their logistics management strategies, enabling more efficient resolution of complex problems. The article concludes that domestic companies possess substantial potential to enhance their logistics processes through the adoption of Big Data analytics but must first address existing challenges. To achieve successful implementation, continuous improvement of technological and organizational processes is essential, alongside ensuring high-quality data collection and analysis. These findings hold practical significance as they provide Ukrainian logistics companies with insights into the benefits and challenges of leveraging Big Data analytics. This understanding will facilitate the development of effective strategies to bolster competitiveness and streamline logistics operations.