Get Your Data Ready — The Future of Autonomous Trucking Is Already Here
October 14, 2022
With a seemingly endless series of supply chain and logistics upheavals occupying shippers’ attention, preparing for a future of self-driving vehicles can feel like a distant priority. But autonomous trucks have already hit the road in select markets on a micro scale, indicating widespread implementation may be more imminent than many predict. And with no sign the national truck driver shortage will abate any time soon, forward-thinking shippers expect autonomous technology to fill the gap in the years to come.
Whether or not you’re ready to pilot an autonomous vehicle program, it’s time to begin preparing your data so you can pivot quickly when the moment is right. If that’s not incentive enough, consider that clean and comprehensive data is also essential to meet sustainability goals and decrease emissions throughout your network.
How (and how soon) will autonomous technology change logistics?
We know fully automated trucks work and are already running on low-complexity lanes on a micro scale. More widespread use is coming — the only question is when. Though it’s impossible to look into the future, many progressive shippers will likely begin autonomous truck pilots on select lanes within the next five years. Although many of the current self-driving pilot programs use diesel trucks, we’re also likely to see a shift to electric happening in parallel as EV technology becomes more affordable.
Other types of robotics, like autonomous cranes and forklifts, are already common at railyards and ports, with adoption showing no sign of slowing down. We can expect adoption of drones, on the other hand, to be a bit further out as logistics teams and engineers continue to explore use cases.
Streamline data to prepare for autonomous trucking now
As with all transportation strategies, preparing for autonomous trucking requires a granular understanding of everything that’s happening in your network — and that requires data. Self-driving trucks aren’t appropriate for all types of freight on all lanes, so analyzing shipments on a micro level allows you to identify the best opportunities. Here’s how to take action now so you can rack up wins down the road.
Centralize and scrub data. The sooner you begin consolidating and scrubbing data, the more informed you’ll be when it’s time to implement a pilot program. A transportation solutions partner that extracts, cleans, and consolidates data from multiple platforms will provide the clearest view of your network. Taking this foundational step is essential any time you’re looking to implement new technology into your freight transportation network.
Take a bottom-up approach. Work from the ground up to devise a laser-focused plan that’s optimized for success rather than starting with a broad, high-level strategy. Use your scrubbed data to identify runs that can easily transition to autonomous trucks, beginning at the actual movement level, rather than the lane level. Characteristics of good candidates include:
Short-distance lanes that run multiple times per day
Majority freeway (non-residential routes and limited turns)
Identify opportunities to trial other technologies. Granular data is useful for pinpointing opportunities to test innovative technologies beyond autonomous trucks. Battery electrics and other sustainable fuel options are a prime example. For instance, the short-distance hauls that are suitable for autonomous vehicles are also ideal for electric or potentially hydrogen-powered vehicles. As technological advancements intersect, you’ll increasingly see opportunities to achieve multiple goals at once.
The world is full of choices, challenges, and exciting technological advancements. Position yourself to take advantage of emerging opportunities in autonomous technology and beyond by harnessing granular data at every leg of your freight transportation network.
Ready to take advantage of real-time, micro-level data throughout your network? Explore Capac-ID and our FELIX platform to discover how you can make your data work harder.