As process engineers, the decisions we make rely heavily on data, and the more of it, the better. Tools based on BI and data analysis used today may seem sufficient, but the information they gather and process is limited, and so is the analysis they provide. We still need to do most of the job “manually”.
Neural Flow changes this dynamic
Neural Flow’s system automates data collection, adding a wide range of information that is there but never used, and analyzes it based on benchmarks and historical data to predict the outcome throughout the process. With real time information, process engineers are able to make necessary adjustments throughout the manufacturing process and guarantee an optimal final outcome.
Using machine learning and AI, the system examines correlation between sensors, compares current batch to historical ones, and identifies non-linear correlations. Analysis is presented clear and simply on the Neural Flow dashboard, indicating whether the production line is optimal or not.
With a system that allows to predict outcomes in real time, companies are able to prevent process inefficiencies, continuously improve production capabilities, and cut costs associated with suboptimal production.
In addition to the dashboard, real-time alerts are sent to designated users, allowing them to immediately react to any deviation and manage suboptimal batches, before it’s too late.