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2min June 24, 2024 2min Jun 24, 2024
June 24, 2024 2 minute read

Introducing Braincube Multiple linear regression (MLR) predictive analytics

Get early insights into process health, performance, and production results for improved process stability, uptime and resource allocation—in just a few clicks.

We are excited to announce that Braincube now offers predictive analytics capabilities!

This powerful new feature equips production teams with early insights into process health, performance, and results to maximize operational efficiency.

By bringing predictive analytics models into the hands of process experts, operations can prevent process deviations and downtimes, plus spotlight the most valuable optimization opportunities quickly and accurately.

How does it work?
  • Use AI (via the Advanced Analysis Application) to find the most critical variables to include in a predictive model based on your objective
  • The app builds a multiple linear regression (MLR) predictive model using the most impactful variables for your objective
    • Each prediction includes a model accuracy score to bolster confidence in prediction viability
  • Utilize the model in other Braincube applications to track results, compare live performance to predicted outcomes, set alerts, and more

See predictive analytics in action

See predictive analytics in action

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Predictive analytics deliver real results

0 hours
saved on quality checks every month
increase in production volume
0 minutes
average time to build a prediction

Download the data sheet to learn more about Braincube’s predictive analytics capabilities.

Download data sheet
industrial worker outline with yellow check mark

Build accurate predictive models quickly

Streamline optimization workflows and save your SMEs significant time and effort. Process experts can build accurate MLR predictive models in just a few clicks, using the most impactful variables identified by Braincube AI.

Stabilize production and increase uptime

Take corrective actions before process drifts occur. Proactively identify machine downtimes, quality issues, or energy drifts using predictive models that enable teams to anticipate—and reduce—critical situations

Improve resource allocation

With accurate prediction modeling, teams gain valuable information that they can use to make cost-saving decisions. Early insights into process health and performance enable operations teams to maintain production stability and optimize resource allocation.