Logistics stands as one of the most complex and rapidly evolving industries, where precision, speed, and reliability are of critical importance. With the growth of global shipments and the increasing complexity of supply chains, companies are facing new challenges: warehouse capacity shortages, delivery delays, and instability in transportation routes. According to Mordor Intelligence, the global freight and logistics market reached $6.03 trillion in 2024, with an average annual growth rate of 4.57%.
It is becoming evident that further industry development is impossible without the implementation of innovative technologies. One of the key directions in the technological transformation of logistics has been the application of big data and machine learning. These technologies allow not only tracking cargo movements but also predicting potential disruptions, optimizing routes, and reducing costs.
Dmytro Verner is an experienced software engineer and AI specialist with an extensive background in developing high-load distributed systems for logistics and predictive analytics. With years of experience in cloud infrastructure, data architecture, and AI-driven automation, Verner has played a pivotal role in transforming how data is leveraged in logistics operations. As an expert in predictive logistics, he has authored numerous publications on AI applications in supply chain optimization, cloud computing efficiency, and backend system automation. His contributions to the field have earned him recognition among industry professionals, and his work continues to drive innovation in AI-powered logistics.
His contribution has been in designing and implementing AI-driven data solutions to enhance supply chain visibility, mitigate disruptions, and optimize logistics operations. His expertise in integrating machine learning into logistics systems has enabled businesses to analyze millions of real-time events, improving decision-making and reducing operational inefficiencies.
Working on projects in predictive logistics, engineers develop and implement innovative solutions that analyze millions of real-time events and help companies avoid supply disruptions. For example, a significant project involving the implementation of machine learning for data analysis took place in the logistics system of TransVoyant, a company specializing in predictive supply chains. With a team of engineers, Verner led the efforts to optimize cloud infrastructure, implementing a Data Lake based on Apache Spark, AWS Glue, Athena, and S3 (a data storage system that integrates various sources of information and enables fast analysis of large data volumes using distributed computing), and developing automated integration systems. Thanks to these solutions, AWS costs were reduced by more than 45%, and data processing speed was doubled.
āWorking with big data in logistics is not just about processing information, but about finding optimal business solutions. The more data we analyze, the more accurately we can predict future scenarios,” notes Verner.
The technological transformation of the logistics sector requires deep technical expertise. Therefore, at TransVoyant, Verner and his colleagues created a unique system that analyzes millions of events in real time every day. “We worked on projects that tracked the location of all cargo ships and airliners across the globe,” says Dmytro.
A key achievement was the development of a large-scale Data Lake based on Apache Spark, AWS Glue, Athena, and S3. To achieve this, engineers focused on optimizing data ingestion pipelines, improving query execution speed, and implementing automated resource scaling. By refining how data was structured and processed, they managed to enhance system efficiency and reliability. “One of the critical steps was restructuring storage layers and reducing redundant computations, which significantly decreased processing overhead,” explains Verner. These improvements ultimately led to a more cost-effective and stable cloud infrastructure.
One of the most impactful tasks was stabilizing Docker Swarm clusters. The team faced frequent service crashes due to uneven workload distribution. The solution was found through the implementation of strict resource limits for Docker containers. “We were able to reduce the cluster size by four times while significantly improving its fault tolerance,” recalls Dmytro.
Another innovative approach to data integration was used. The team developed a data mirroring system between environments using HTTP and Kafka events (a real-time data streaming mechanism that allows systems to exchange information between different services instantly). This enabled clients such as McKesson, Merck, and Bridgestone to receive up-to-date logistics process information in real time. The solutions he developed helped accelerate the integration of external partners by twofold, automating more than 90% of processes.Ā
The growing interest in AI-driven logistics solutions is reflected in ongoing investments from major industry players. For instance, companies like Merck Global Health Innovation Fund and P74 Ventures have previously backed predictive logistics initiatives, signaling a long-term market trend toward automation and data-driven decision-making. Verner notes that this sustained investment activity highlights the industry’s commitment to advancing logistics technologies and integrating AI-driven innovations.
Speaking about the future of the industry, Verner notes that the predictive analytics market in logistics is projected to grow from USD 18.02 billion in 2024 to USD 95.30 billion by 2032, exhibiting a CAGR of 23.1% during the forecast period. In the coming years, AI will be even more deeply integrated into logistics. It will not only predict risks but also optimize routes and manage inventory independently,” he predicts.
For companies to successfully adapt to new technological realities, they must implement advanced solutions and build strong teams. Verner emphasizes the importance of continuous learning and flexibility. Dmytro plans to continue working on high-load distributed systems and AI-based projects.Ā
In the logistics of the future, the winners will not be those who follow trends but those who create them, the expert believes. “Investments in predictive analytics and advanced technologies are not just risk mitigation ā they are strategic assets shaping the future of logistics,” Verner concludes.
