The need for logistics innovation continues to grow. With a global decline in the industrial workforce observed, the adoption of logistics automation requires the capacity to respond to "logistics dynamics."

On the 21st, the 'Logistics and Distribution AI Leader Conference' was held at COEX in Samseong-dong, Seoul, hosted by the National IT Industry Promotion Agency and the Korea Integrated Logistics Association.

This event is a place to explore the future of the logistics industry using AI and big data, and the direction in which the smart mobility field will be integrated into the logistics industry. The following topics will be discussed over three days: △Direction of logistics innovation using AI, △2023 logistics AI technology convergence trends, and △Chat GPT and smart logistics mobility.

On the first day, Professor Jang Yoon-seok of Korea Aerospace University presented the prerequisites and challenges for adopting logistics AI technology, emphasizing the importance of "dynamic processes" in logistics. The dynamic aspect of logistics is a key factor to consider, such as the connection between serial and batch processes. A serial process is a process that performs one task at a time, while a batch process is a process that performs multiple tasks at once.

Professor Jang explained, "In batch processes, items come out all at once," and that if serial processes are followed, bottlenecks and other issues can arise. He added, "In factories and logistics settings, there are limitations to dynamic process analysis. While these aspects need to be considered in the process, the field lacks the capacity to build systems."

To achieve this, it is crucial that developers and field managers with the knowledge and capabilities to interpret these dynamics collaborate when building AI algorithms and automation systems.

The global trend of decreasing industrial workforce continues, and Professor Jang, citing data from the Boston Consulting Group (BCG), predicted that “by 2030, the industrial workforce pool will decrease by 5.4% and 60% of the world’s population will live in urban areas.”

Professor Jang emphasized that AGV (Automated Guided Vehicle) and AMR (Autonomous Mobile Robot), which are autonomous driving mobility technologies used in logistics automation, each have clear advantages and disadvantages. In the case of AMR, “if the level is not good, it can be recognized as an obstacle, and in spaces where people work together or where there are many table legs, it stops too much, so the work efficiency is only about 20%.” AMRs are currently difficult to use for logistics automation in manufacturing processes due to their low precision.

He also said, “Autostore’s solution, which offers excellent space utilization and density, is expected to be useful in certain logistics applications in logistics warehouses in urban areas where space is limited,” but “it is not suitable for places with high volumes of goods or excessively heavy products.”
Professor Son Byeong-hee of Kookmin University, who explained the direction of AI innovation and the convergence of logistics and mobility, said, “The importance of AI utilization in the logistics and mobility fields lies in △real-time monitoring and decision-making support △improving customer experience for workers and users △flexibility △automation △future prediction, etc.”

When deep learning and machine learning, leveraging big data, are applied to predict the future, logistics networks can be optimized for inventory management and warehouse operations. These technologies are already being adopted by major logistics companies like Amazon. Examples include AutoStore's logistics automation solution and Amazon's Kiva system.

He pointed out that it is necessary to consider the challenges that AI will face in the future, such as the quality and availability of learning data, AI human resources technical capabilities, data security and privacy protection, and the occurrence of ethical issues.