The need for logistics innovation continues to grow. As the decline in industrial manpower is observed as a global trend, it has been suggested that the field introducing logistics automation must necessarily be accompanied by the ability 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 the course of three days: △Direction of logistics innovation using AI, △2023 logistics AI technology fusion trends, and △Chat GPT and smart logistics mobility.

On the first day, Professor Jang Yoon-seok of Korea Aerospace University presented the prerequisites and tasks for introducing logistics AI technology and emphasized the importance of the 'dynamics process' in logistics. The dynamic aspect of logistics is a key factor to consider in connections 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 that “in a batch process, items come out all at once,” and that if a serial process is followed, issues such as bottlenecks can occur. He pointed out that “there are limitations to dynamic process analysis in factories and logistics sites,” and “there is a need to consider these aspects in the process, but the sites lack the capacity to build systems.”

To this end, it is most important for developers and field managers with knowledge and capabilities in analyzing these dynamics to collaborate when building AI algorithms and automation systems.

The global trend of decreasing industrial manpower continues, and Professor Jang predicted, citing data from the Boston Consulting Group (BCG), that “by 2030, the industrial manpower 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 Robots), which are autonomous driving mobility technologies used in logistics automation, each have clear advantages and disadvantages. In the case of AMR, “if the flatness is not good, it can be recognized as an obstacle, and in spaces where people work together or places with many table legs, it stops too much and the work efficiency is only about 20%.” AMR is currently difficult to use for logistics automation in manufacturing processes because of its low precision.

He also said, “Autostore’s solution, which has 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 a lot of goods moving or products that are too heavy.”
Professor Son Byeong-hee of Kookmin University, who explained the direction of AI innovation and logistics/mobility convergence, said, “The importance of AI utilization in the logistics and mobility sector lies in the following: △real-time monitoring and decision-making support △improvement of customer experience including workers and users △flexibility △automation △future prediction, etc.”

If future predictions using deep learning and machine learning using big data are applied to the field, logistics network optimization such as inventory management and warehouse operation can be achieved, and these technologies are already being introduced mainly by large logistics companies such as Amazon. Representative examples include Autostore's logistics automation solution and the Kiva system built by Amazon.

He pointed out that it is necessary to consider the following challenges that AI will face in the future: △quality and availability of learning data, △AI human resources technical capabilities, △data security and personal information protection, and △ethical issues.