/Working With Robots in a Post-Pandemic World

Working With Robots in a Post-Pandemic World

Summary: Plug-and-play automation systems can be rapidly set up to meet sudden surges in demand — and quickly reconfigured when needs change.

Original authors and publication date: Matt Beane and Erik Brynjolfsson – September 16, 2020

Futurizonte Editor’s Note: How much of the past we are about to leave forever in the past because of the pandemic? And how open are we to a new future?

Image courtesy MIT Sloan Management Review
Image courtesy MIT Sloan Management Review

From the article:

Whether you turn to news outlets, tech magazines, or academic sources for insight, you’re likely to hear that the COVID-19 pandemic is going to drive massive growth in automation, especially via robots.1 The arguments in favor of this view seem reasonable: Main Street might look dead, but companies that provide shippable goods have been facing double, triple, or even 10 times their previous demand. Robots, the thinking goes, should be able to reliably do that repetitive physical work when many workers aren’t safely able or willing to set foot in the building. What’s more, access to the technology is getting less expensive, with “robots as a service” models allowing companies to pay per touch rather than dipping into precious capital reserves. And robots are becoming more capable.

In just the past few years, for example, we’ve seen a small number of companies building and selling AI-enabled robots to pick things out of bins, handle parts, tend machines, and test the latest electronics. This is impressive because it’s high-mix work — that is, the products, the work conditions, the processes, and the final output shift regularly but also in surprising ways. Until recently, this made automation via robotics a nonstarter, because previous approaches to things like object detection, grasp detection, and placement verification relied on stable products, conditions, processes, and outcomes. Now? Toss some new objects into a bin, change the lighting, change their position and orientation, and these leading-edge systems can often handle it. Robotics companies are making similar advances in automating other physical jobs, such as materials transport, sorting, and palletizing. So why wouldn’t robots start flying off the shelves?

Because successfully putting robotics into production is a complex undertaking, and most companies aren’t equipped to implement and benefit from these advanced systems. As we’ve studied how organizations and front-line workers are adapting to next-generation, AI-enabled robotics in manual work throughout the U.S., we’ve found that successful adaptation is rare. That stands to reason.

History and decades of research tell us that when a qualitatively new form of automation comes along — anything from punch-card-driven looms to automated call patching — organizations spend much more time and money than anyone expected to find productive uses for that technology. Erik and colleagues call this phenomenon the Productivity J-Curve: Radical new technologies require costly investments in business process redesign, worker reskilling, and organizational transformation.3 These investments usually pay off eventually, but initially, productivity and performance, at least as conventionally measured, can take a discouraging dip.

But we also know from Matt’s research that during such times — when well-understood means of adapting fail — a small minority of users will find rule- and expectation-bending ways to get results more quickly.4 So, in our next phases of research, we’ll continue to look for and learn from these rare deviants: How do they pull it off? We’ll be collecting data from tens of thousands of U.S. enterprises with hundreds of thousands of employees. And to test for broader applicability, we’ll be enrolling a selection of organizations to try out the practices, conditions, and technologies that allowed for early success in a few isolated cases.

Meanwhile, we’re gathering and analyzing data from a diverse range of venture-funded robotics vendors and their business customers, watching implementations from the beginning, and interviewing hundreds of managers, front-line workers, and other professionals involved in implementing the technologies. We’re covering a range of industries, too — warehousing, order fulfillment, parcel handling, kitting, and food preparation, for example. These industries center on facilities and workforces that receive daily truckloads of palletized products (perfume, apparel, ostrich jerky, automotive glue, wooden toys), break them down, catalog them, store them, and then sort and package them to ship off to an end customer.

READ the complete original article here