Computer Vision Can Help Address the Shortage of Skilled Truck Drivers

According to the Bureau of Labor Statistics (BLS), there are about 1.75 million heavy and tractor-trailer truck drivers in the US, along with 877,670 light truck or delivery services drivers and 427,000 driver/sales workers. Truck drivers provide a critical component to the US economy hauling over 60% of all freight by value.¹

With the very tight labor market and changes in working norms, fewer and fewer men and women are selecting driving as a career. The old allure of long-haul drivers is fading with drivers finding comparable paying jobs which let them sleep in their own beds at night. Employers not only face a shrinking labor pool, but persistently high turnover rates approaching 100% for long haul drivers.

To counter the shortage, employers are offering higher salaries and bonuses. The Washington Post interviewed several drivers and their employers with surprising results – much higher pay is not enough to keep drivers from leaving the profession because of the rough lifestyle and strain on marriages.

Brenny Transportation based in Minnesota increased driver pay 15% in an effort to attract additional drivers. Even with many drivers earning $80,000, the company could not find enough drivers.² Exacerbating the driver shortage is the rapid growth on-line retailing with fast delivery services. The American Trucking Association estimates that 50,000 more drivers will be needed to meet the growing demand.³

So how can technology help alleviate the shortage of drivers? Computer vision based on artificial intelligence and machine learning provides continuous monitoring, analytics, and alerts. Instead of managing by hit or miss observations and data points, you manage by fact. A.I. increases human efficiency and AI models can find patterns that humans do not have the capacity to process. These patterns lead to predictive analytics not easily recognized by humans.

Such predictive analytics can have a major effect on safety by aggregating a variety of data to predict which drivers are most likely to get in preventable accidents or those most likely to incur a compliance or safety violation. With A.I. and computer vision, an individual driver’s data over time, such as their service hours are tracked. Potential safety trends for fleet managers or human resources can also be evaluated.

Such information not only allows a manager to identify drivers who could use additional training, but also which loads are better suited to higher-risk drivers. As an example, A.I. will help to identify high-risk drivers and avoids assigning them to high-priority loads traveling through challenging weather.⁴

For the first time driver performance, both good and bad, can be captured in near real-time. Drivers can be rewarded for timely deliveries made in a compliant manner. Less experienced drivers can receive very quick feedback to help them improve performance. Drivers who continually struggle to meet delivery schedules and follow safety protocols can be quickly identified for additional training or replacement.

The greatest value may come in the continuous improvement of fleet operations, especially in improving the cycle times in yards, depots, and distribution centers. Atollogy’s clients have reported discovering and resolving a variety of bottlenecks in which everyone suffers – idle trucks don’t make money. With computer vision, drivers can be alerted to excessive wait times so they can better schedule delivery and pickup times.

While computer vision alone cannot solve the growing shortage of drivers, it can help to make your fleet become more efficient and safer.

Notes

  1. Stephen V. Burks and Kristen Monaco, “Is the U.S. labor market for truck drivers broken?,” Monthly Labor Review, U.S. Bureau of Labor Statistics, March 2019, https://doi.org/10.21916/mlr.2019.5
  2. https://www.washingtonpost.com/news/wonk/wp/2018/05/28/america-has-a-massive-truck-driver-shortage-heres-why-few-want-an-80000-job/?noredirect=on&utm_term=.f3611620f7e4
  3. https://www.trucking.org/News_and_Information_Reports_Driver_Shortage.aspx
  4. https://www.ttnews.com/articles/ai-trucking
Author:

Anthony Tarantino, PhD

Adjunct Professor, Santa Clara University – Operations, Finance, Lean Six Sigma

Six Sigma Master Black Belt, Certified Scrum Master, CPIM (APICS), CPM (ISM)

Senior Advisor to Atollogy

Tony@atollogy.com