Continuous Process Verification
Chemicals & Petrochemicals
Food & Beverage
Mining, Metals & Materials
Oil & Gas
Pharma & Life Sciences
Power Generation
Pulp & Paper
Use Case Activity
Asset Optimization
Use Case Business Improvement

Defining and monitoring statistical control limits for critical process parameter (CPP) trends is essential in optimizing product quality for today’s process manufacturers. What’s needed is an efficient way to monitor quality measurements against static or variable specification limits, for early identification of run rule violations with product quality impact. This takes countless, manual hours when using spreadsheets to organize and analyze the related data.

By implementing Seeq’s advanced analytics, it’s easy to identify data over which to define CPPs and calculate Statistical Quality Control (SQC) metrics using capsules. This may include segregating by product grade, operating mode, or identifying golden profiles. Engineers and SMEs can quickly find statistical averages and standard deviations over key periods of interest, while simultaneously creating monitoring boundaries and creating conditions to indicate periods when rule violations occurred.

With the power to easily monitor procedures for CPPs within boundaries and automatically update deviation reports with the latest data, SMEs can make fast decisions that reliably and accurately improve product quality and overall operational performance.

Data Sources:

  • OSIsoft PI or other process data historian
  • SQL database for quality and/or batch event data

Data Cleansing:

  • Training window for creating SQC metrics is defined.

Calculations and Conditions:

  • Reference Profile
  • Formula
  • Signal from Condition
  • Boundaries
  • Deviation Search
  • Scorecard Metric

Reporting and Collaboration:

  • SQC monitoring views and supporting scorecards can be brought into a monitoring dashboard or used in historical deviation reports. 
  • Deviations are automatically identified with capsules for quick visualization and summary statistic aggregation.