Unlocking the value of robots as team players

This is an excerpt of the original article. It was written for the January-February 2022 edition of Supply Chain Management Review. The full article is available to current subscribers.

January-February 2022

Well, that’s over, and aren’t we all glad to put 2020 in the rear-view mirror? For a minute, however, let’s look at a silver lining, because I think there is one for supply chain managers. That’s because the pandemic put supply chain in the spotlight like never before—and, with the approval of a vaccine just a few weeks ago in December, supply chain and cold chain are back in the news… While sales usually gets all the attention, maybe 2021 is our time to shine.” That’s the beginning of the column I wrote for the January 2021 issue, and maybe I was a little too pollyannish.

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Some 1.8 million industrial robots are in operation around the world, yet these machines are not fully integrated into the human workforce.

Research underway at the Massachusetts Institute of Technology is facilitating more effective collaboration between humans and machines, and recent advances indicate that human/machine interdependency may not be far off. MIT researchers are using artificial intelligence and machine learning to give robots the ability to work in teams with humans and even identify ways to improve team performance.

Low profile, low value

Julie Shah, professor in the Department of Aeronautics and Astronautics at MIT, and leader of the Interactive Robots Group of MIT’s Computer Science and Artificial Intelligence Laboratory, has spent much of her career developing robots for manufacturing applications. Her overriding vision is developing machines that augment human capabilities.

People often underestimate the number of working robots over and above those in industrial settings, says Shah. For example, there are some 30 million robots in U.S. homes, and numerous units working in other locations including sidewalks and supermarkets.

However, “it doesn’t feel like” the population is this large, she says, mainly because these machines are engaged on narrowly defined tasks supervised by humans. The problem is “if they are not integrated at scale, we miss opportunities to gain value from these systems.”

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