When reading about AI and its less than favorable aspects, such as Amazon’s data centers using over 2.5 billion gallons of water in a year and AI taking away tech jobs, it’s easy to lean into pessimism. However, there are positive sides to AI, especially when it is in the right hands. For example, a former Walmart truck driver (now regional load manager), Leo Garcia, created an AI application that solves the problem of “empty miles” for truckers.
After completing a Google AI certification program Walmart offered to its employees, the Bentonville load manager developed an app that automatically searches for truck loads in a specific geographic location. It identifies five ideal loads that truckers can pick up on the way home, to limit empty miles. These are the inefficient aspects of the job that most drivers have faced during their career: not being able to locate a return connection on their trip home.
To illustrate the app’s performance, Garcia shared the story of a driver who was supposed to pick up a van on his trip home, but learned it wouldn’t be ready for another three hours. Using Garcia’s AI application, the driver found a different load five miles away, ready to go to the same location. This allowed the driver to stick to his schedule and get home on time.
How AI solves logistics problems
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Aside from the Walmart truck driver app, are there other similar cases where AI actually helps in the workplace? Even as companies like Amazon replace delivery drivers with humanoid robots, not all AI solutions are designed to take away people’s jobs. Some supplant human workers and limit logistical hiccups for businesses. For example, DHL Express has made good use of AI-based robots to sort small parcels. The company says the robots can sort more than 1,000 of these packages in a single hour.
Although humans oversee the process, this combined approach has increased DHL’s sorting capacity by more than 40%. Frito-Lay solved a logistics problem with predictive maintenance. By deploying IoT sensors in its factories, managers can predict mechanical failures well before shutting down production. The result was no unexpected equipment failures for the first year.
From optimizing routes to identifying bottlenecks in production, AI has many uses in logistics that are in keeping with the spirit of Leo Garcia’s mood-coded application. Once you get past the apocalyptic narrative, AI is a tool that can work well when deployed correctly. Of course, problems remain and we will have to face them in the future, but it’s hard to deny how useful AI can be when it makes our lives (and jobs) easier.
