The goal of Industry 4.0, the digital industrial revolution in manufacturing, is near-automated, remotely managed, low-touch operations. Edge computing data analytics gets us there.
Manufacturing has traditionally been rife with inefficiencies: Machines fail without warning, leading to expensive downtime. Operational know-how resides with a few experienced technicians. Everyday production metrics about parts produced and defects mitigated are mostly opaque.
Industry 4.0 uses advanced technologies like the Internet of Things (IoT), artificial intelligence (AI) and machine learning (ML) to reduce these inefficiencies. They make both discrete and process manufacturing more transparent across the supply chain. The premise is that data from the edge—i.e., close to the source—makes operational technology (OT) more predictable.
In Industry 4.0, OT integrates with IT. Data from the shop floor feeds advanced ML algorithms that make operational decisions in near real time. Those decisions can be related to shutting down a machine that is about to fail or operating a robotic welding arm. Industry 4.0 is revolutionary, but to truly realize that potential, we need smart ways of processing all that data. That's why we need edge analytics.
Edge computing data analytics
When every asset on the shop floor is bristling with Industrial Internet of Things (IIoT) sensors, the volume of data generated can be immense. The promise of the data is that it can inform near-real-time decisions, which means the gathered data has to be processed in milliseconds. On-premises servers lack the capacity to handle extremely large volumes. Cloud computing can handle the volumes but cannot deliver on the near-real-time (eight milliseconds or less) aspect of the equation. Routing data back and forth to the cloud consumes bandwidth, and it might be a blunt force approach that may not even be needed for all the data collected on the shop floor.
Edge computing data analytics make these decision-making processes more efficient. Edge analytics enable manufacturers to be smart about both volume and latency. It allows manufacturers to decentralize select data processing operations. Those that need near-real-time computing close to the edge, such as collaborative robot use, can lean on edge computing data analytics. Long-term ML model-crunching routes to the web. With edge analytics, manufacturers can also choose which data to save—and which ones to destroy after use.
Edge analytics use cases
The future of edge computing data analytics is already here in a few enterprises that are further along the path to digital maturity. In many others, including mid-size companies, the OT/IT alignment is still a work in progress partially due to costs that can be incurred to transform.
Expect edge analytics to help manufacturers get the most out of their assets. IoT data, such as temperature, vibrations, humidity, etc., from assets feed ML models, which can predict parts failure days (if not weeks) in advance. In near real time, if machine failure is imminent, assets can be programmed to shut down automatically and alert the appropriate employee.
Discrete manufacturers can also use AI to figure out which variables to tailor to meet daily production quotas and other key performance indicators. A prescriptive approach complements the predictive maintenance approach.
Collaborative robots are becoming a fixture in manufacturing as they efficiently complement human expertise and are safer for production line use. Robots need data-driven decisions in near real time, something that edge analytics deliver.
Anomaly detection models, as applied to parts assembly and evaluation, also need near-real-time multi-access edge computing to accommodate high production volumes on the plant floor and is another use case for edge data analytics. Plenty of other advanced technologies such as augmented reality (AR) coast on the low-latency aspect of the edge equation.
Both discrete and process manufacturers can expect to fully realize the powers of advanced technologies such as AI, ML, robotics, AR and VR to drive efficiencies in the supply chain, reduce downtime and improve daily production goals. Edge computing data analytics is a key enabler of these technologies in the factory of the future.
Discover how leveraging multi-access edge computing can enhance product quality.