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.