The edge brings computing as close to the data source as possible. Industrial edge computing is connecting all assets used in manufacturing, oil and gas, energy, or transportation and processing that data locally. Running fewer processes in cloud and enterprise systems and moving them closer to the devices generating data transforms the way data is handled, processed, and delivered for immediate business benefits.
Many companies have taken a cloud-first approach to digital transformation, without properly considering the importance of the edge. Gartner estimates that installed IoT endpoints in the manufacturing and natural resource industries will grow to 1.9 billion units by 2028. Data volumes are only going to continue to multiply, and higher volumes of data being sent to the cloud means increased latency and costs. The edge not only complements the cloud by offsetting some of those challenges, but also activates key use cases and applications that are better hosted on-premise.
Cloud has its place for long-term analysis, but the value of industrial edge computing lies in making use of the data at the asset, where it has the greatest impact and zero latency.
Edge computing allows companies to collect data, analyze it at the edge, and then take immediate action to solve maintenance problems, increase efficiency, improve production, and more.
Networks and infrastructure haven’t quite caught up to the data explosion, so utilizing edge computing to only send the data that is needed to the cloud saves time and money.
On-Premise Use Cases
The cloud makes sense for machine learning and other long-term or big data analysis, but the edge activates on-premise use cases like condition-based monitoring and OEE for quick ROI.
The edge has three main components. Edge connectivity is the ability to connect to any industrial system and collect and normalize data for immediate use Edge intelligence is concentrating data processing and analytics functions at the edge to take action and derive value at the data source. Edge orchestration is the ability to create, deploy, manage and update edge applications.
The industrial edge has matured rapidly, and most factories have achieved some level of connectivity. However, the ideal edge strategy takes all three of these components into account, activating the edge for device connectivity, data collection, real-time analytics, application orchestration, integration to cloud and enterprise systems, and running machine learning models at the edge. A modern edge platform bridges the gap between industrial devices at the edge and advanced analytics in the cloud.
The vast importance of the edge is beginning to come to light as more industrial use cases are enabled. Industrial edge computing powers preventative maintenance, asset condition monitoring, OEE, vision systems, quality improvements and more. Edge data also powers more advanced use cases like artificial intelligence and machine learning in the cloud.
Many companies are either sending all of their data to the cloud or keeping it at the edge, they don’t know how to effectively do both. Edge and cloud technologies need to work together. As companies continue to embrace digital transformation, Industry 4.0, Smart Manufacturing, and all of the advanced use cases those initiatives bring with them, they will recognize the importance of the edge and the cloud working in harmony to drive intelligent business decisions.
Want to learn more about how an Industrial IoT Edge platform works? Watch the Litmus On-Demand Demo now.