In recent years, artificial intelligence has come to dominate many aspects of our lives through increased computing power and advances in computer science. And transportation and logistics are no exception. The McKinsey firm estimates that the use of AI in the supply chain will create an economic value of $1.3 trillion, highlighting it as the second-largest industry to be transformed by AI, following marketing and sales. But what role can AI play in transportation and logistics?
Relying less on human intervention
The availability of well-trained personnel to operate vehicles and heavy machinery is well known to be a significant bottleneck for any operation. Data from the American Trucking Association suggests that a driver deficit of more than 60,000 currently exists in the U.S., and is expected to double in the next decade. Other areas of the transportation industry are not faring any better; data suggests that the aviation industry will need 800,000 new pilots over the next 20 years, and there is anticipated to be a mariner shortage of 150,000 positions by 2025. Most of these jobs are well-paid, and yet, the shortage seems to result from the strain put on individuals operating these vehicles.
AI-enabled automation may soon lead to autonomous vehicles, where active human participation in the operation of the vehicle is no longer required. Instead, operators will oversee the operation of the vehicle and intervene as needed. These intelligent machines can, in turn, learn from their past experiences, and over time become more efficient in its operations. Utilizing autonomous vehicles will not only make working in the transportation industry more pleasant, but it will also help overcome shortages in the workforce, allowing for a single operator to manage multiple vehicles.
Optimizing resource utilization
One challenge that the transportation and logistics industry regularly navigates is how to optimize routing in response to the network of resources available to an organization. In the past, optimizing transportation was done by individuals with insight into the operations of the enterprise who could identify and track a selection of specific few performance measures. However, in our complex world where a wealth of valuable data is now readily available to companies, synthesizing data to optimize routing is becoming more challenging for people to manage.
Today, transportation companies are investing in developing research tools specifically for operations, such as creating operation models based both on physics and data collected from previous operational experiences. These models, along with tools such as machine learning, artificial intelligence, and optimization, help to identify patterns and streamline fleet operations. For example, KLM is working with BCG to develop a suite of AI tools linked to the KLM fleet to support decision-making regarding fleet utilization. In the case of KLM, optimization not only applies to the fleet but also warehousing and distribution centers. As the use of AI grows, its application in warehouse distribution is likely to spread beyond the traditional transportation and logistics industry.
For years now, AI has been used to predict demand and improve customer satisfaction, but it is also used to ensure items are available for consumers on-demand. Through the use of AI, Amazon anticipates orders to determine what to stock is available in their warehouses. But Amazon has also transformed its operations by utilizing its fleet of robots on the warehouse floor to autonomously gather ordered items, and bring them to a handler for shipping. Intelligence machines not only help Amazon more easily scale up its operations but also shield itself against issues relating to human resource availability. By doing this, Amazon can also increase the safety and efficiency of its operations.
Last-mile delivery and door to door transport
One of the least efficient steps in any transportation operation is distributing products to smaller retailers or end-users. Product distribution has been so unprofitable that in many countries, the postal service has reduced the frequency of its deliveries.
AI helps to optimize this process in two ways. First, automation of smaller vehicles (like robots and drones) can enable direct delivery to customers efficiently and without the need for a skilled individual to operate every single vehicle. Amazon and Alphabet have been investing in this area for some time, with the intent to use both ground and air vehicles to transport goods.
Beyond drones, the move toward building smarter roads can also help make deliveries more efficient. Smart roads equipped with sensors will enable live tracking of congestion and monitor infrastructure allowing autonomous vehicles to traverse routes more efficiently. As technology advances, interactions between the vehicles will also shift; the technology will likely move away from focusing on data needed for a human operator (such as indicators) to a form where intelligence vehicles can acknowledge and interact with one another. Advances such as these will further increase the safety and efficiency of road travel.
Like any other tool, AI cannot replace the transportation and logistics industry but can help alleviate pain points in this sector. By tackling the human resource shortage issues, enabling efficient warehousing and distribution, and increasing the efficiency of door-to-door delivery, AI and automation are helping reduce some of the operational obstacles experienced by businesses in the transportation and logistics industry.
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