High-performance computing, robotics, and artificial intelligence are extending the range of tasks that machines can perform on the factory floor adjusting workflows in response to change in consumer demand, and helping to contain spiraling production costs with more efficient manufacturing practices.
But technology is only part of what's needed to drive more flexible, more efficient production lines process re-engineering is also needed to make sense of, and quickly act upon, large volumes of sensor data streaming from interconnected devices.
ProductScope360 crunches through machine-generated data streams to improve the operating reliability of in-service devices.
Our approach is deeply rooted in techniques from computer science called "machine learning," and more complex algorithms used for decision-making ("deep learning") that can spot anomalies and trends in large volumes of unstructured data specially designed logic similar to the reasoning behind popular voice-, text-, and image-recognition apps, where context is important for arriving at the right answer.
Here, predictive analytics improve with experience at anticipating mission-critical failures, to alert about preventative maintenance in advance learning to better use key manufacturing assets and to boost already tight profit margins in highly commoditized industries.