Proficy CSense industrial analytics demos

See how Proficy CSense industrial analytics software helps process engineers turn historical and real-time industrial data into actionable insights to improve product quality, process stability, and operational performance.

In this six-part demo series, see how Proficy CSense can help you:

• Predict product quality: Build machine learning models using process and laboratory data to predict quality in real time.
• Prevent product quality defects: Identify the process conditions driving defects and determine how to reduce them.
• Reduce process variation: Uncover the variables causing variation and identify operating conditions for more stable production.
• Calculate control limits: Use historical process data to establish control limits that support stable, consistent operations.
• Detect process problems early: Create multivariate process fingerprints to monitor process health and identify abnormal conditions.
• Optimize PID control loops: Analyze poorly performing loops and calculate recommended PID tuning settings to improve control performance.

See how Proficy CSense can help process engineers analyze industrial process data, uncover relationships and root causes, develop predictive and diagnostic analytics, and turn insights into real-time recommendations that support better operational decisions.

Complete the form to access all six Proficy CSense demos.

See how process engineers can use Proficy CSense to combine process and laboratory data, build machine learning models, and predict product quality in real time to enable more targeted quality testing.

See how Proficy CSense uses process and quality data to identify the conditions driving product defects and provide real-time recommendations that help operators prevent quality issues.

See how process engineers can use Proficy CSense to uncover the variables driving process variation, identify optimal operating conditions, and provide recommendations for more stable production.

See how Proficy CSense uses historical process data to calculate control limits for different products and operating conditions, helping maintain stable and consistent production.

See how Proficy CSense uses multivariate process fingerprints and machine learning to monitor process health, detect abnormal conditions early, and identify potential causes.

See how process engineers can use Proficy CSense to analyze poorly performing control loops, calculate recommended PID tuning settings, and improve process stability and control performance.