Extra-condensed knowledge
- 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.
- Plug-and-play automation systems can be rapidly set up to meet sudden surges in demand — and quickly reconfigured when needs change.
- Since plug-and-play automation is still somewhat new, it requires intensive customer support.
Genioux knowledge fact condensed as an image.
The “genioux facts” Knowledge Big Picture (g-f KBP) chart
Condensed knowledge
- Working With Robots in a Post-Pandemic World.
- Moving Forward. Companies and front-line workers are struggling to find their way in a sea of automation opportunity.
- A small minority will have some genuine innovation to show for it in general and when we get through the COVID-19 storm in particular.
- But a healthy economy does not turn on the atypical success of a few while most fail. We need to find and learn from those rare successes as quickly as we can so that everyone can adapt more constructively.
- 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.
- 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.
- 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 Brynjolfsson and colleagues call this phenomenon the Productivity J-Curve: Radical new technologies require costly investments in business process redesign, worker reskilling, and organizational transformation. These investments usually pay off eventually, but initially, productivity and performance, at least as conventionally measured, can take a discouraging dip.
- We also know from Matt Beane’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. 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?
- Reconciling Potential With Reality.
- The fulfillment processes, despite all the advances in automation, are still quite manual at their core. People have to move pallets; cut them open; lift, place, and scan products; drive forklifts to store and retrieve products from racks; place them into bins or sorters; pick them out; put them into a final configuration; inspect, seal, and label boxes; and move them onto outbound trucks.
- 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.
- The arguments in favor of massive growth in automation seem reasonable:
- 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.
- 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.
- Plug-and-Play Automation
- Plug-and-play automation systems can be rapidly set up to meet sudden surges in demand — and quickly reconfigured when needs change.
- Examples among the companies we’re studying include modular, computer-controlled conveyors; automatic guided vehicles (AGVs); and sorting machines.
- Since plug-and-play automation is still somewhat new, it requires intensive customer support.
Category 2: The Big Picture of the Digital Era
[genioux fact extracted from MIT SMR]
Authors of the genioux fact
References
ABOUT THE AUTHORS
Matt Beane (@mattbeane) is an assistant professor in technology management at the University of California, Santa Barbara, and a digital fellow at the Digital Economy Lab at Stanford University. Erik Brynjolfsson (@erikbryn) is the director of the Digital Economy Lab at Stanford and coauthor of The Second Machine Age (W.W. Norton, 2014).
Erik Brynjolfsson (born 1962) is an American academic. He is a senior fellow and professor at Stanford University where he directs the Digital Economy Lab at the Stanford Institute for Human-Centered AI, with appointments at SIEPR, the Stanford Department of Economics and the Stanford Graduate School of Business. He is also a Research Associate at the National Bureau of Economic Research and a best-selling author of several books. He is known for his contributions to the world of IT productivity research and work on the economics of information and the digital economy more generally.
BIOS
50 words:
Professor Matt Beane does field research on work involving robots to help us understand the implications of intelligent machines for the broader world of work. Beane is an Assistant Professor in the Technology Management Program at the University of California, Santa Barbara and a Digital Fellow with Stanford’s Digital Economy Lab.