Solutions that drive value for CPG plants
Lock in profits with live optimization insights
Traditional process optimization projects can take months. And they only work under pre-defined circumstances.
Fero Lab's white-box machine learning algorithms instantly analyze current and past production data to discover the most efficient process settings, taking into account any sources of variation. What used to take you months can now be done in minutes.
You don't have to take our word for it--all models automatically quantify uncertainty to give you confidence in your results, even in unseen operating conditions.
Failure detection before it's too late
A failed batch of CPG products can cost thousands of dollars to replace. It also takes up hours on the production line that could have been spent on mixing new products.
With Fero Labs, operators receive alerts that a batch will fail hours ahead of time.
Our machine learning algorithms even tell them how to change their operating parameters to fix the uncovered issues, so they can step in and adjust before it's too late. Flawed products no longer have to be reworked and shipped, saving your time, costs, and reputation.
Soft sensors continuously monitor emissions
Sustainability objectives are more important than ever. However, filling the gap between goals and tangible emissions reduction can be challenging.
Fero Lab's emissions blueprint is especially designed for plants to reduce emissions while simultaneously minimizing costs, preventing the usual prohibitive investment of the typical sustainability program.
Soft sensors allow you to keep ahead of tightening regulations and monitor NOx, SOx, and CO2 emissions continuously to ensure that you meet corporate sustainability goals.
Models quantify and prevent asset degradation
Understanding asset efficiency decreases can save maintenance teams the valuable time and effort of dealing with unplanned downtime. Yet many existing solutions will only detect anomalies.
Fero Labs goes beyond flashy predictive maintenance to take a more holistic approach to overall machine health.
Engineers can predict when heat exchangers, reactive catalysts, and other assets will no longer run optimally. They can also explore root causes of degradation to find the optimal maintenance window.
