As emerging technologies like Artificial Intelligence (AI) and Machine Learning (ML) continuously evolve and find applications within the supply chain, disrupting existing markets and creating entirely new addressable markets will disrupt the logistics and supply chain industry sectors. This article delves into the driving forces behind AI’s emergence in logistics and its projected market growth. We explore use cases of AI in freight transportation and their disruptive potential in reshaping the future of logistics.
Relevance of Artificial intelligence in supply chain
Artificial intelligence (AI) is the broader concept of computer systems being able to simulate human intelligence processes. These processes include learning, reasoning, and self-correction. AI can speed up mundane tasks, such as choosing a carrier, by narrowing down the possible choices. AI improves data analytics, demand prediction, capacity estimation, and network analysis.
Artificial intelligence in the supply chain market is expected to reach $46.2 billion by 2029 from $2.3 billion in 2021, at a CAGR of 45.5%.
Figure 19: Global artificial intelligence in supply chain market
Drivers of AI in the supply chain ecosystem
The requirement for transparency and end-to-end visibility across the supply chain industry is one of the key market drivers. The rise of AI has also raised awareness of the quality of data from which the AI system learns. Thus, the big data era and advanced data analytics technologies are other drivers of market growth. AI technologies, including augmented intelligence and ML, reduce the need for human effort, allowing time and money to be saved – Gartner expects that augmented intelligence created $2.9 trillion in business value and led to an increase of 6.2 billion hours of worker productivity globally by the end of 2022. Additionally, the development of powerful AI-specialized chipsets by manufacturers, including Nvidia, Intel, AMD and Microsoft further supports market growth.
Use cases for AI in freight transportation
While autonomous vehicles hold great promise for revolutionizing the freight transportation industry, their disruptive potential remains relatively restricted due to regulatory challenges and the need for further technological advancements. Technology startups like Nuro, an American robotics company based in California, are driving innovations that will ultimately disrupt the last-mile delivery of goods. It develops autonomous delivery vehicles and is the first company to receive an autonomous exemption from the National Highway Traffic Safety Administration.
Implementing AI-driven smart warehousing solutions optimize warehouse processes, improve inventory management, and increase efficiency through automation and data-driven decision-making. An example is 6 River Systems which combines collaborative warehouse automation solutions and autonomous mobile robots to improve warehouse productivity and efficiency.
AI-powered route optimization has the potential to reduce transportation times and costs for freight transportation companies significantly. Companies can achieve substantial efficiency gains by optimizing routes based on real-time data and various parameters. OptimoRoute is a cloud-based software powered by an algorithm that uses artificial intelligence and machine learning to help logistics companies optimize their delivery routes and schedules.
Intelligent Load Matching
AI-based load-matching platforms help reduce empty miles by efficiently pairing available trucking capacity with shipments needing transportation. This improves overall fleet utilization and leads to cost savings while enhancing industry efficiency. Loadsmart is a digital freight brokerage platform that matches Carrier Availability with Shipper Demand Through Advanced Integrations and Machine Learning.
Real-time Tracking and Visibility
AI-driven real-time tracking and visibility solutions are poised to improve operations and customer satisfaction by providing accurate, up-to-date information on shipment status and location. Startups like Portcast improve supply chain resilience and profitability by accurately predicting arrival times and monitoring container shipments.
Last-Mile Delivery Optimization
AI applications in last-mile delivery optimization can significantly reduce delivery times and costs and increase customer satisfaction. Companies like Treggo utilize AI algorithms to optimize delivery routes and schedules, enhancing the overall delivery process.
Robotic Sorting and Packaging
While robotic sorting and packaging solutions show promise for improving warehouse efficiency, their disruptive potential is currently relatively low. However, they offer a short time to value in specific use cases where repetitive tasks can be automated effectively. Kindred, for example, develops robotic systems that automate the sorting and handling of items in e-commerce fulfillment centers and warehouses.
AI can analyze vast amounts of data, enabling companies to identify potential risks, assess their impact, and develop proactive strategies to mitigate disruptions. Companies like Everstream Analytics provides risk management and predictive analytics solutions to mitigate supply chain disruptions caused by weather events, geopolitical issues, and other factors.
AI-powered dynamic pricing solutions can potentially increase revenue and profits for freight transportation companies by optimizing pricing strategies based on real-time market conditions and demand fluctuations like Transfix.
Artificial Intelligence in various aspects of the supply chain, including freight transportation, offers immense disruptive potential with the promise of improved efficiency, reduced costs, and increased customer satisfaction. While some use cases have a shorter time to value and lower disruption, others require more time to mature and integrate fully into existing supply chain operations. Nonetheless, the overall impact of AI on the supply chain industry sector is set to be transformative in the coming years.