Key takeaways
Short answer: Edge computing processes data on or near the machine, so decisions happen in milliseconds and the line keeps running even if the network drops. Cloud computing processes data centrally, where it scales cheaply for heavy analytics, long-term storage and cross-site aggregation. The real question is not edge-or-cloud but which work belongs where: time-critical and resilience-critical processing at the edge, scale and analytics in the cloud. See also iiot vs iot.
Edge computing runs processing on hardware at or beside the machine — a gateway, an industrial PC, sometimes the controller itself. Because the data does not have to travel to a data centre and back, decisions happen in milliseconds, and the local system keeps working even if the internet connection fails. It is built for speed and resilience close to the process.
Cloud computing centralises data and compute. It scales storage and analytics cheaply, runs heavy models that no edge device could host, and aggregates data across many machines and sites into one view. Its trade-off is latency and dependence on connectivity — the round trip and the network are unavoidable.
A line uses computer vision to reject defective parts. That decision must happen in under 100 milliseconds, every part, even if the internet drops — so it runs at the edge, on a local device beside the camera. The same plant also wants to trend defect rates across twelve lines and three sites, retrain the vision model monthly, and report to the board — work that needs scale and history, so it runs in the cloud. The edge made the real-time call; the cloud did the analytics and the learning. Forcing either job into the wrong layer would have failed: cloud is too slow for the reject, the edge too small for the fleet analytics.
Edge and cloud solve different halves of the problem. Edge answers "act now, locally, reliably"; cloud answers "store, aggregate and analyse at scale." A modern architecture splits the work: time-critical and resilience-critical processing stays at the edge, while storage, fleet analytics and model training go to the cloud, with the edge feeding summarised data up.
The most common edge mistake is putting time-critical or capture-critical work in the cloud and then losing it during an outage. If OEE capture lives only in the cloud and the network drops for an hour, that hour vanishes from the data. Edge capture that buffers locally and syncs on reconnection is what keeps the record complete regardless of connectivity.
1. Real-time control in the cloud. Latency and outages make it unreliable for closed-loop decisions.
2. Heavy analytics on edge devices. They lack the scale; this belongs in the cloud.
3. Capture that does not buffer at the edge. Network drops become holes in the data.
4. Treating it as a single choice. The right design splits the work by latency and resilience needs.
OEE benefits from both layers: edge capture keeps the data complete and real-time even through network blips, while cloud aggregation trends OEE across lines and sites. A platform that captures at the edge and analyses in the cloud gives you both an unbroken record and fleet-wide insight.
See how Fabrico captures this automatically on your lines — explore OEE for manufacturing or book a demo.
Neither — edge suits real-time and resilient work, cloud suits scale and analytics. Most plants use both.
Real-time control, safety, local OEE capture, and anything that must survive a network outage.
Long-term storage, cross-site analytics, heavy models and reporting.
Edge capture that buffers locally prevents network outages from creating holes in your data.
No — it is an ongoing split, assigning each workload to the layer that fits its latency and resilience needs.
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