Artificial Intelligence, Logistics Analytics, and Automation
A section of Logistics (ISSN 2305-6290).
Considerable attention is focused on the transformation potential of artificial intelligence—especially methods of machine learning, deep learning and natural language processing—and digital technologies like sensors and robotics on logistics and supply chain management. The availability of these tools and technologies is enabling new capabilities in automation and responsive logistics processes that are generating value for shippers and customers alike. The McKinsey Global Institute estimates that the transportation and warehousing industries have the third-highest automation potential of any sector of the economy.
E-commerce has been a major driver of this transformation. The Covid-19 pandemic only accelerated these changes as people relied on online retailers for everything from food and medicine to clothing and furniture. This rapid growth in e-commerce can be expected to continue and will demand an equally rapid and innovative response from the logistics and transport industries. At the same time, adoption of AI and data analytics is being fueled by exponentially increasing amounts of data generated by digital devices and IoT-enabled sensors that track products, equipment, and transport vehicles across logistics networks.
Artificial intelligence will increase the speed of order fulfillment, reduce inefficiencies and dramatically accelerate productivity and effectiveness. Machine learning will make it possible to discover fine-grained and complex patterns in data that can improve decision-making for functions ranging from warehouse and inventory management to transportation planning. Pattern recognition can also help to both predict and recover from disruptive events along the supply chain. And AI-enabled vehicle routing can increase fleet efficiencies and reduce costs. In warehouses and distribution centers, machine learning combined with computer vision is powering robots and drones to autonomously monitor and manage inventories.Open questions remain about how to best implement artificial intelligence and other digital technologies to drive transformational change. This Section aims to accelerate the development and adoption of machine learning and related AI methods for logistics and supply chain management. This Section invites high-quality, original research papers, review articles, empirical studies and case studies that demonstrate the value of artificial intelligence and automation to the management of logistical systems and supply chain networks.
Prof. Dr. Noel Greis