- By Joe Berti
- IIoT Insight
Like many buzzwords, there isn’t necessarily a strict, widely agreed-upon definition for Industry 4.0, or the fourth industrial revolution. Within industrial and manufacturing settings, it usually encompasses a broad array of technologies and automation techniques that promise to help optimize processes.
Industry 4.0 can refer to models that decide which fulfillment center will ship materials so they arrive at factories sooner, or product suites like IBM Maximo, which use advanced analytics and artificial intelligence (AI) to guide asset management and decide which repairs should be made next. It can also refer to robots that help us do the heavy lifting, or visual inspection technologies that help organizations identify flaws on the assembly line or make rapid changes to their operations.
Like the steam engine or the assembly line, Industry 4.0 offers a steep improvement to productivity and production by automating work that humans find tedious, or, on the other hand, by augmenting human ability with tools that help people do their jobs significantly better. However, to make any of these functions work at scale, a large array of enabling technologies is needed, from sensors and Internet of Things devices that gather data, to edge computing, hybrid cloud, and 5G, which allow models and operations to run wherever organizations need them.
The technical infrastructure beneath Industry 4.0
In the 21st century, meaningful changes to the way we work require deliberate investment and a sophisticated digital infrastructure with a footprint all the way from the edge to your data center to your cloud. A hybrid cloud architecture achieves this, giving you a common container-based platform across all your infrastructure locations, the ability to auto-scale based on your workloads, and the flexibility to run your platform in any cloud—public, private, or edge and across all of them.
Edge computing and next-generation mobility networks like 5G also make it possible to gather insights and process data in motion.
When computing can be done at the edge, organizations can interpret vastly larger amounts of data without degrading network performance. With edge computing, you can also run certain workloads such as some AI models on site, allowing AI to do much of the work of determining which data and insights are valuable enough to merit analysis in a central hub.
With a hybrid cloud architecture and other key infrastructure in place, a great deal of the automation implied by Industry 4.0 becomes possible. You can gravitate away from human interventions toward digital ones. Cameras and beacons can detect when equipment is running hot or indicate some sort of mechanical flaw. AI models can assign holistic health scores to different assets, and predictive maintenance models can take a fixed budget and determine the optimal repairs under different criteria. In the very near future, and in situations where the problem is being caused by a software and not a mechanical issue, AI models will likely even be able to carry out interventions of their own, identifying the problem and then determining and running the software patch needed to fix it. This has ramifications not only for company savings and efficiency, but employee safety: Why send an inspector into a cell tower or some other potentially dangerous situation if you don’t have to?
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