One place where an embryonic form of human-machine teaming already takes place is in the world of retail: Walmart uses robots to scan store shelves for stock levels and has automated truck unloading (via a system called the “Fast Unloader”) at many stores—using sensors and conveyor belts to sort shipments onto stocking carts. And robotic systems have already taken over the role of warehouse “picking” at Amazon, working with humans to retrieve and ship purchases.
Conversely, an element of Industry 4.0 that has evolved past the embryonic stage is the use of sensor data to drive plant operations—especially for the task of predictive maintenance. Unexpected equipment downtime is the bane of all industries, especially when the failure of a relatively minor part leads to the total failure of an expensive asset.
By some estimates, about 80 percent of the time currently spent on industrial maintenance is purely reactive—time spent fixing things that broke. And nearly half of unscheduled downtime in industrial systems is the result of equipment failures, often with equipment late in its life cycle. Being able to predict failures and plan maintenance or replacement of hardware when it will have less impact on operations is the Holy Grail of plant operators.
It’s also a goal that industry has been chasing for a very long time. The concept of computerized maintenance management systems (CMMS) has been around in some form since the 1960s, when early implementations were built around mainframes. But CMMS has almost always been a heavily manual process, relying on maintenance reports and data collected and fed into computers by humans—not capturing the full breadth and depth of sensor data being generated by increasingly instrumented (and expensive) industrial systems.
Doing something with that data to predict and prevent system failures has gotten increasingly important. As explained by MathWorks’ Industry Manager Philipp Wallner, the mounting urgency is due to “[T]he growing complexity that we’re seeing with electronic components in assets and devices, and the growing amount of software in them.” And as industrial systems provide more data about their operations on the plant floor or in the field, that data needs to be processed to be useful to the operator—not just for predicting when maintenance needs to occur, but to optimize the way equipment is operated.