This so-called “dark data”, often retained purely for compliance purposes, incurs storage costs for manufacturers without generating commensurate value. By some estimates, more than two thirds of all manufacturing data collected goes unused. More often than not, industrial data is siloed, meaning that it’s only accessible by one department or division and otherwise isolated from the rest of the organization, limiting its usefulness. Of course, that data needs to be accessible in order to derive those insights. Other manufacturing data sources –including business transactions, maintenance records, geospatial data, and RFID scans–can all provide insights into industrial operations. There are many potential sources of information in manufacturing, not just the obvious ones such as sensors and PLCs. But first, we need to look at manufacturing data. To understand why, we need to understand what machine learning is and how it can actually be useful in an industrial environment.
It’s also a valuable tool and the next logical step in the long history of manufacturing’s evolution. Unfortunately, there remains considerable skepticism toward machine learning in manufacturing, no doubt due to its close ties to buzzwords like “digitalization” and “Industry 4.0”.īut machine learning isn’t just a buzzword. Machine learning can augment an engineer’s capabilities, processing the huge volumes of data generated during production so that they can make decisions with as much information as possible.
#MACHINE LEARNING FACTORY HOW TO#
The sheer number of variables involved in an assembly line is enormous, far more than a single person can manage on their own.ĭeciding when or how to change manufacturing lines is nothing like deciding when or how to change lanes.Īnd yet, machine learning has the potential to be much more valuable in the factory than on the road, at least for the foreseeable future. It’s much more difficult to understand how artificial intelligence and machine learning apply to something like automotive manufacturing. We’ve all seen the videos of what an autonomous vehicle “sees” through its cameras and-aside from the unhelpful yet ongoing debates about trolley problems-the execution seems fairly straightforward.īasically, we can conceive of how autonomous vehicles drive because we’re able to drive ourselves. It’s understandable: self-driving cars make it easy to visualize something as abstract as machine learning. When it comes to automotive applications of artificial intelligence, most people will think of autonomous vehicles.
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