Truck Driver Shortage: Is Tech the Answer?

The driver shortage in the US trucking industry, which the American Transportation Research Institute named a top concern for a fourth consecutive year[1], erodes margins and increases uncertainty across the supply chain: higher driver wages, hiring, training and retention costs for carriers and higher transportation spend and inventory holding costs for shippers as capacity tightens. The average age of a for-hire over-the-road trucker is 46, and 57 for a private fleet driver[2]. There is an urgent need in the industry for young workers who can replace drivers as they retire. Unfortunately, the demanding nature of trucking and the availability of alternative careers are drawing young workers away from the road.

For example, demand for supply chain jobs in warehousing and distribution has grown exponentially with the rise of ecommerce, and the trend of distribution centers moving closer to the consumer means that these jobs are now recruiting from the same labor pools as carriers. While trucking jobs offer comparatively higher earning potential on average than warehousing jobs – and many other jobs for which no college degree is required – they typically fall short in the realm of work-life balance, a crucial measure for young workers. The compensation advantage of trucking is further diminished by the availability of gig work with services such as Uber, Lyft, Instacart, Postmates, TaskRabbit, and Rover, which give workers additional earning opportunities with complete flexibility.

Rate increases are not a sustainable solution to the driver shortage problem and put enormous pressure on small and medium sized shippers, many of which were hit hardest by the effects of the COVID-19 pandemic. The onus is on carriers to deploy technology solutions such as AI and machine learning and end-to-end visibility solutions to remove common pain points from the driver’s workday, enhance safety and improve the overall appeal of trucking as a career choice – especially for millennials and GenZ. Doing so will provide ROI in the form of higher driver retention, more productive fleets, less detention payout, and better service to shippers.

It Begins with Data

Driver feedback is typically gathered through individual conversations between drivers and managers, driver roundtables, and occasional surveys. While these feedback methods are important for building a driver-centric culture, they provide only anecdotal evidence and not the abundant, consistent data required to drive real change. Real transformation requires current, sufficient, reliable data from which actionable intelligence can be derived. Data must be collected in real-time, organized in accessible databases, and analyzed using modern analytics and visualization tools.

Consistent Feedback through micro-surveys

Micro-surveys are an effective way to gather data at critical points in the driver’s trip, and can be completed on a driver’s phone or on-board device in under a minute. Driver micro-surveys can be utilized to gather feedback on shipper facilities, interactions with management, equipment, and the overall experience on the trip. They can be structured similar to the micro-surveys that Rideshare and ecommerce companies use to gather customer feedback, with multiple choice layouts, canned responses, and a star rating. Keyword analysis can also be used for written responses.

This approach will produce consistent data that can be structured and organized in a central database from which it can be easily queried. The concept hinges on driver compliance, which can be incentivized through gamification and rewards for consistency. Buy-in can also be achieved through transparency – showing drivers how their feedback is used to improve their day-to-day life on the road. Transparency is also critical for building trust in the system and ensuring drivers that their feedback is anonymous and confidential. This kind of transparency has proven effective in achieving buy-in for other trucking technologies such as in-cab cameras[3].

Enriching Data with IoT

IoT is used in trucking to track shipments and collect driver behavior data that is used to manage risk and coach drivers to be safer on the road. Carriers should consider how the reach of this technology can be expanded to capture additional data that can be used to identify and correct driver pain-points. One example is to capture idle time data at shipper locations. Carriers could use this data to identify shippers that deviate from their peers in terms of wait time and work with them to increase throughput at their facilities, reducing detention time for drivers.

Improving the Driver Experience with AI and Machine Learning

AI and machine learning technology is deployed in transportation to automate backend processes, increase forecast accuracy, improve driver safety risk analysis and management, optimize routing and scheduling, and improve other operational and organizational KPIs. There is significant opportunity to use this technology to directly enhance the driver experience.

Increasing Visibility Downstream

Transportation managers use AI-enriched insights and predictive analytics to maximize fleet productivity. However, these insights are not always visible to the driver. Drivers typically plan every detail of their trips down to the minute, so this visibility would be of great value to them. Going back to the example of idle time at shipper facilities: if a driver knows that the estimated wait time at a shipper is two hours before he even departs with the load, he can plan ahead to use this time for calls with friends and family, plan his next trip, or take care of personal matters.

Chatbots to Shorten Hold Times and Driver Service Backlog

A common pain point for drivers is excessive time spent on hold when they contact managers for information or to report an issue. In fact, an Infosys research team analyzing over 600 driver reviews of leading Digital Freight Matching apps found that the largest proportion of negative reviews – fifteen percent – involved excessive time spent on hold when trying to contact a dispatcher. Chatbots can instantly provide information and directives to drivers, and AI advances have made them nearly indistinguishable from human operators. Advanced speech recognition technology reduces the risk of misunderstandings and miscommunications that occur between humans.

Choosing the Right Solutions

These are just a few examples of technology solutions that can enhance the driver experience and help carriers improve retention rates and attract a new generation of truckers. Once the driver challenges are understood, carriers can explore numerous cloud-based and SaaS-based solutions that can be customized to fit specific business needs and implemented quickly. Choosing the right technology and solution is key for carriers who want to revolutionize trucking and stay ahead of the curve.

[1] The American Transportation Research Institute, Oct. 2020, “Critical issues in the trucking industry – 2020”, ATRI-Top-Industry-Issues-2020.pdf (truckingresearch.org)

[2] Jim Stinson, Transport Dive, “Driver turnover accelerates with burnout from long trips: WorkHound”, Oct. 30, 2020, Driver turnover accelerates with burnout from long trips: WorkHound | Transport Dive

[3] Jim Stinson, Transport Dive, “How cameras change the narrative on truck driver safety”, Jan. 11, 2021, How cameras change the narrative on truck driver safety | Transport Dive