There’s a lot of focus on how artificial intelligence in self-driving trucks, digital load matching and other freight and logistics functions will remove humans from the process, disrupting the industry.
But Mike Capps, the chief executive of AI startup Diveplane, and former president of Epic Games, the producer of blockbusters Fortnite and Gears of War, sees how it can also support traditional shipping and logistics.
Here are 10 ways Capps thinks AI should be applied to the trucking industry.
- Using Synthetic data sets will help logistics providers analyze their vast quantities of customer-based data without fear of the repercussions to their reputation or financial loss due to data loss. Implementing Synthetic data “twins” allows for broader internal and external analysis of data to better understand customer demand and patterns.
- Machine learning analytics can support a better understanding of driver behavior and the cause of collisions that will directly link to the evolution of improved training and driver education.
- Additionally, machine learning will provide a greater depth of understanding of required preventative maintenance routines for vehicles to maximize the efficiency of the fleet. There is also a link to driver behavior where speed, braking, steering data can help identify where a certain style of driving may correlate with mean-time-between-failure (MTBF) data.
- AI can be used to determine the optimal route and fleet mix required to enhance the potential of cost leadership by maximizing vehicle capacity and reducing fuel expenditure
- It can help ensure better quality candidates: Using AI in the recruiting process can help better place candidates in trucking positions by eliminating bias in selection. That allows ways to narrow the gender gap and alleviate the shortage of drivers. Technology will provide the opportunity to determine the factors and features of their best employees to support the selection process for new recruits
- Interpretable AI ensures that self-driving vehicles operate correctly. Autonomous vehicles need transparent and understandable technology in order to function properly and efficiently. Without a purview into why a machine makes each and every decision, AI cannot be trusted to perform securely at all times – an imperative requirement for self-driving vehicles
- AI is evolving to support inventory mix, qualities and stock locations to minimize stock rebalancing across a network. The application of AI for inventory optimization will result in less inventory, lower vehicle and fuel costs.
- Machine learning is already being deployed in many industries susceptible to fraud, waste and abuse. The logistics industry is always looking for innovation to determine the root cause for stock loss, or shrinkage. Machine learning will play a major part of this process.
- AI has the capacity to take data from multiple sources to support strategic decisions such as identifying relationships between population migration and macroeconomics. That can support where to locate new regional or national distribution centers.
- AI will play a central role to identify profitable and non-profitable customers. Using historical data, machine learning solutions will be able to support decisions on whether a particular customer will be prone to late or non-payment. The technology will also provide an insight into credit worthiness.