Using Control Charts to Strengthen Your Quality Program in Presage Analytics

Control charts are a powerful and widely used statistical tool for monitoring process behavior and identifying when variations require attention. Within Presage Analytics, the Control Chart Report helps teams visualize performance trends, detect special-cause variation, and make informed decisions to maintain process stability and product quality.

What Are Control Charts?

A control chart tracks whether variations in a process are random (expected) or consistent (potentially problematic). Random variation is a natural part of production, but consistent patterns or shifts may signal underlying issues. Identifying these signals early helps prevent process failures, maintain compliance, and reduce waste.

How the Control Chart Report Works

The Control Chart Report in Presage Analytics displays the average result for selected analysis options plotted against a mean and calculated control limits. These limits are based on data from a separate date range that you define, allowing you to analyze current performance relative to a stable historical baseline.

You can filter the report by analysis, option, location, plant, lot number, brand, and other parameters to focus on the data most meaningful to your process.

Understanding Special Cause Tests

On the Control Chart Report, we’ve included a list of special cause tests. When your data meets these criteria, it indicates the process is behaving as expected. Failing one or more tests suggests that special-cause variation may be present.

These signals help determine the appropriate response, from taking a re-check sample to pausing production in order to investigate and correct potential issues.

Keep in mind that, like any statistical tool, control charts balance sensitivity and false alarms. Occasional false signals may occur, but the goal is to reliably detect meaningful changes without overreacting to normal variation.

What Is the Standard Error of the Mean (SEM)?

SEM represents how accurately your sample mean (from your selected date range) estimates the true population mean (from the full data set). A larger sample size reduces SEM and improves accuracy.

Using a limited date range for calculating control limits, such as a comparison to the same period from a previous year, helps account for expected seasonal or cyclical variation in your process.

Why It Matters

By combining historical context, real-time performance visualization, and special-cause detection, the Control Chart Report supports proactive quality management. It helps you:

• Identify shifts before they become problems
• Confirm when your process is stable
• Understand variation within context
• Strengthen decision-making across teams and locations

Take the Next Step

Control charts have long been a cornerstone of process improvement, and Presage Analytics makes them accessible, flexible, and actionable for any production workflow. Interested in seeing them in action? Schedule a demo of Presage Analytics today to see more!