2 posts tagged with "Edge Computing"

The edge vs. cloud debate in industrial IoT has been argued for years, and both sides have valid points. Edge advocates emphasize latency, reliability, and bandwidth costs. Cloud advocates point to scalability, advanced analytics, and reduced on-site infrastructure. The reality — as experienced by anyone who's actually deployed IIoT in a manufacturing environment — is that the answer is almost always "both."

But "both" isn't helpful without specifics. Which data should be processed at the edge? What belongs in the cloud? How should the two layers communicate? And what does this architecture actually look like when you're connecting PLCs on a factory floor to AI-powered analytics?

This guide provides practical answers for manufacturing engineers and plant managers who need to make architecture decisions without a PhD in distributed systems.

Every second, a modern manufacturing plant generates millions of data points. PLC registers cycle through readings, sensors capture temperatures and pressures, vision systems inspect parts, and motor drives report speed and torque values. The question isn't whether to collect this data — it's where to process it.

For the past decade, the default answer was "send everything to the cloud." But manufacturers are learning the hard way that shipping every data point from every machine to a cloud server creates problems: network bandwidth costs, latency that prevents real-time action, dependency on internet connectivity, and enormous cloud compute bills.

Edge computing — processing data at or near the source — is emerging as the practical answer for most manufacturing IIoT applications. Here's why it matters, how it works, and what to consider for your factory.