If smart manufacturing vendors hope to fulfill the potential of their solutions and platforms for digital factories, they must build environments where apps can deliver immediate results with stream processing and cloud integration at the edge.
Edge intelligence refers to the analysis of data and development of solutions at the site where the data is generated, such as the shop floor. Edge intelligence software can run on small compute footprint devices on-premise in order to provide complex event processing on streamed data. These platforms integrate factory data with the supply chain and the rest of the enterprise, train machine learning models, and help scale other transformative technologies with the help of edge intelligence. The aim is full integration with operational technology, lower total cost of ownership and financial viability for customers with limited information technology (IT) resources and increasing amounts of data.
As in many other verticals, the next step for smart manufacturing will demand faster deployment of artificial intelligence and machine learning. More low-code or code-free logic configuration and app development — plus edge-to-cloud, closed-loop machine learning, whereby models train in the cloud, execute at the edge and collect more data for continuous improvement — will make this possible. Fortunately, several vendors have already proven that they can provide both:
- AWS, Azure, FogHorn Systems, Software AG, SWIM.AI and Telit, have already productized edge-to-cloud, closed-loop machine learning.
- Enterprises in automotive, electronics, oil and gas, and steel manufacturing, among several other industries, have implemented and seen return on investment from edge solutions.
- As the amount of custom code required to deploy new solutions on factory floors drops, data and analytic service revenue growth in smart manufacturing will accelerate to reach a global total of $25.6 billion in 2026.
Use cases that leverage edge solutions include networking machines from multiple original equipment manufacturers (OEMs) with proprietary protocols, quality control for automotive paint shops and windshield glazing, executing machine learning models on-premise, predictive maintenance for almost any manufacturing equipment, and automatically orchestrating setup of production lines for electronics contract manufacturers.
In the past, networking operational technology equipment from different OEMs often meant continuously working with custom code, which resulted in immense costs and time demands on the IT departments. Now, many edge solution vendors have started to offer code-free logic configuration and app development, significantly speeding up deployment and scalability of custom apps on the factory floor. As more vendors offer and deploy code-free app development and logic configuration at the edge, smart manufacturing solutions will grow faster, requiring fewer professional services.
These findings are from ABI Research’s “Balancing Edge and Cloud in the Digital Factory” report. This report is part of the company’s smart manufacturing service, which includes research, data and executive foresights.