What is a Control Chart? Definition & Manufacturing Examples

What is a Control Chart?

A control chart (also called a Shewhart chart) is a statistical tool used in statistical process control (SPC) to monitor a manufacturing process over time. It plots measured data points in time order against three reference lines: a center line representing the process average, an upper control limit (UCL), and a lower control limit (LCL). These control limits are calculated from the process data itself — typically set at three standard deviations from the mean — and represent the boundaries of expected natural variation.

The fundamental purpose of a control chart is to distinguish between two types of variation. Common cause variation is the inherent, random variability built into every process — it is always present and can only be reduced by fundamentally changing the process. Special cause variation is variation due to specific, identifiable factors such as a worn tool, a new operator, a defective material batch, or an environmental change. Control charts detect special causes so they can be investigated and eliminated.

Walter Shewhart developed control charts at Bell Laboratories in the 1920s, and they remain the foundation of modern quality control. W. Edwards Deming later popularized their use in manufacturing, famously helping Japanese manufacturers transform their quality systems after World War II.

How Control Charts Work in Manufacturing

Control charts are constructed in two phases. In Phase I, historical data is collected from a stable process to establish the center line and control limits. This requires at least 20 to 25 subgroups of data. Any out-of-control points in the Phase I data are investigated, and if assignable causes are found and corrected, those points are removed and limits recalculated.

In Phase II, the established control limits are used for ongoing process monitoring. As new data is collected during production, each point is plotted on the chart and evaluated against the limits and pattern rules.

There are several types of control charts, grouped by data type:

Variable data charts for continuous measurements:

• X-bar chart — monitors subgroup means
• R-chart — monitors subgroup ranges
• S-chart — monitors subgroup standard deviations
• Individuals and moving range (I-MR) chart — for individual measurements

Attribute data charts for count/classification data:

• P-chart — proportion defective
• Np-chart — number defective
• C-chart — count of defects per unit
• U-chart — defects per unit with varying sample sizes

The selection of chart type depends on the type of data being collected, the sample size, and whether the sample size is constant or variable.

Control Chart Example

A machining operation produces cylindrical pins with a target diameter of 10.00 mm. The operator measures 5 pins every 30 minutes and records the subgroup average. After 25 subgroups, the data yields:

• Grand mean (X-double-bar) = 10.002 mm
• Average range (R-bar) = 0.018 mm
• UCL for X-bar = 10.002 + (0.577 × 0.018) = 10.012 mm
• LCL for X-bar = 10.002 - (0.577 × 0.018) = 9.992 mm

During the next shift, the X-bar chart shows three consecutive points trending upward: 10.005, 10.008, and 10.011 mm. While all points are still within the control limits, the trend is a warning signal. The operator inspects the cutting tool and discovers progressive wear. Replacing the tool brings the process back to center.

Without the control chart, the tool wear would not have been detected until parts exceeded the specification limits — by which time dozens of borderline or out-of-spec parts would have been produced. The control chart provided early warning, preventing scrap.

Why Control Charts Matter for Production Scheduling

Control charts are directly relevant to production scheduling because process stability determines scheduling reliability. A process that is in statistical control produces predictable output — the scheduler can trust the planned cycle times, yields, and quality levels. An out-of-control process is unpredictable and creates scheduling chaos through unexpected scrap, rework, and machine downtime.

When control chart data shows a process going out of control, the production scheduler needs to respond. Using scheduling software like Resource Manager DB, the planner can quickly assess the impact of unplanned downtime for investigation and repair, additional rework operations that need capacity, and potential delays to downstream operations and customer deliveries.

Control chart trend data also supports longer-term scheduling decisions. If a process shows slowly increasing variation over weeks, it signals that equipment maintenance should be scheduled before a breakdown occurs. Proactive maintenance scheduling based on SPC data is far less disruptive than reactive downtime.

Related Terms

• Control Limits — the UCL and LCL boundaries on a control chart
• SPC — the broader methodology that uses control charts as its primary tool
• X-bar Chart — the most common variable data control chart for monitoring process means

FAQ

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This term is part of our Manufacturing & Production Scheduling Glossary. Learn more about quality control, scheduling, and manufacturing terminology.