**London**: The logistics industry anticipates growth to $6.03 trillion by 2024 as companies leverage AI and big data to tackle challenges like delivery delays and capacity shortages. Experts like Dmytro Verner reveal the critical role of technology in optimising supply chains and enhancing operational efficiency.
Logistics is increasingly recognised as one of the most complex and rapidly evolving industries, characterised by the critical demands of precision, speed, and reliability. The global freight and logistics market has demonstrated substantial growth, reaching an estimated $6.03 trillion in 2024 with an average annual growth rate of 4.57%, as reported by Mordor Intelligence. However, this growth is met with escalating challenges, such as warehouse capacity shortages, delivery delays, and fluctuations in transportation routes.
As companies navigate these obstacles, the integration of innovative technologies becomes essential. The use of big data and machine learning is recognised as a pivotal direction in the technological transformation of logistics. These advancements not only facilitate real-time tracking of cargo movements but also enable predictions of potential disruptions, optimised routing, and cost reductions.
Dmytro Verner, an experienced software engineer and AI specialist, has been at the forefront of these developments, shaping how data is utilised in logistics operations. With a robust background in high-load distributed systems and predictive analytics, Verner has significantly contributed to the field by authoring several publications focused on AI applications within supply chain optimisation, as well as cloud computing and backend system automation.
Verner’s approach has involved developing and implementing AI-driven data solutions aimed at enhancing supply chain visibility and mitigating disruptions. Through the integration of machine learning into logistics frameworks, companies benefit from the ability to analyse millions of real-time events, which enhances decision-making processes and reduces operational inefficiencies.
A noteworthy project led by Verner involved the implementation of machine learning in the logistics operations of TransVoyant, a firm that specialises in predictive supply chains. This project saw the optimisation of cloud infrastructure using a Data Lake based on technologies such as Apache Spark, AWS Glue, Athena, and S3. These developments effectively halved AWS costs while doubling data processing speeds, demonstrating the tangible benefits of technological integration.
Verner elaborated on the project, stating, “Working with big data in logistics is not just about processing information, but about finding optimal business solutions. The more data we analyse, the more accurately we can predict future scenarios.”
At TransVoyant, Verner and his team designed a system capable of analysing millions of real-time events daily, tracking cargo vessels and aircraft globally. A key achievement of this initiative was creating a large-scale Data Lake, which involved optimising the data ingestion process and enhancing query execution speeds. Verner explained, “One of the critical steps was restructuring storage layers and reducing redundant computations, which significantly decreased processing overhead.” These advancements led to a more reliable and cost-effective cloud infrastructure.
An additional challenge faced by Verner’s team included stabilising Docker Swarm clusters, which were prone to service crashes due to uneven workload distributions. The implementation of strict resource limits significantly reduced the cluster size while improving fault tolerance. “We were able to reduce the cluster size by four times while significantly improving its fault tolerance,” he recalled.
The development of a data mirroring system, utilising HTTP and Kafka events, has also ensured that clients, including notable companies like McKesson, Merck, and Bridgestone, receive real-time updates on logistics processes. This innovation facilitated a twofold acceleration in the integration of external partners by automating more than 90% of processes involved.
Interest in AI-driven logistics solutions has spurred considerable investment from industry leaders. Notable backers such as the Merck Global Health Innovation Fund and P74 Ventures have indicated a long-term market trend favouring automation and data-driven decision-making. Verner remarked on this trend, stating that “this sustained investment activity highlights the industry’s commitment to advancing logistics technologies and integrating AI-driven innovations.”
Looking ahead, the predictive analytics market in logistics is projected to grow from USD 18.02 billion in 2024 to USD 95.30 billion by 2032, reflecting a compound annual growth rate (CAGR) of 23.1%. Verner anticipates that AI will increasingly optimise logistics functions, including risk prediction, route optimisation, and inventory management.
In order for companies to adapt effectively to these transformative technological realities, the implementation of advanced solutions and the cultivation of robust teams is imperative. Verner strongly emphasizes the importance of continuous learning and adaptability. He intends to persist in his focus on high-load distributed systems and AI-based projects.
As Verner aptly summarises, “Investments in predictive analytics and advanced technologies are not just risk mitigation — they are strategic assets shaping the future of logistics.” The insights provided by industry experts like Verner highlight a pivotal moment in logistics, where technological prowess is increasingly steering the future of the sector.
Source: Noah Wire Services
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