Cloud-based IIoT solutions can provide insight from long-term data trends, identifying gaps between benchmark performance and actual performance. However, this is only the start of the digital transformation.
More and more sensors are being installed, due to the increased affordability of sensors as well as technological advancement in sensor technologies, allowing enterprises to collect more data. This opens up a tremendous opportunity to measure and monitor real-time performance gaps between the ideal performance and the actual performance.
Enterprises have more digital use cases, applications, and services built on sensor data. Edge computers connect and integrate these data, transforming them into useful information. Digital transformation can increase efficiency in operations, support predictive maintenance, and improve customer service. However, all of these uses require sensor data and edge computing capabilities.
The challenge of digital transformation is efficient and secure connectivity to sensor data sources in addition to integration with the edge and cloud platform to transform raw sensor data into actionable intelligence near the machine in a real-time sustainable way.
The manufacturing environment demands stringent data security, uninterrupted data flow even with intermittent network for latency-sensitive applications (time it takes for a round-trip query to the cloud and back restricts several outcomes in most industrial environments). Another requirement is a real-time recommendation engine so that the system can act quickly. In the manufacturing environment, machine learning on the edge can reduce the amount of data that needs to be streamed thus allowing faster recommendations to the right person at the right time.
There are several key ways in which intelligent edge computing accelerates the digital transformation journey.