What is a C-Chart? Definition & Manufacturing Examples

What is a C-Chart?

A c-chart is an attribute control chart used in statistical process control (SPC) to monitor the count of defects per inspection unit over time. The "c" stands for count. Unlike p-charts and np-charts which track defective units (each unit is either good or bad), a c-chart tracks the total number of individual defects found on each unit — recognizing that a single unit can have multiple defects.

The c-chart is based on the Poisson distribution, which models the probability of a given number of events occurring in a fixed interval when events happen independently at a constant average rate. This makes it appropriate for counting defects when each inspection unit provides the same area of opportunity for defects to occur.

The key requirement for a valid c-chart is a constant sample size. Every inspection unit must represent the same opportunity for defects. If you inspect one circuit board per sample and each board is the same size, a c-chart works. If board sizes vary or you inspect different numbers of boards each time, you need a u-chart instead.

How a C-Chart Works in Manufacturing

Setting up a c-chart requires several steps. First, define the inspection unit — this is the constant area of opportunity for defects. It could be one product, one square meter of fabric, one length of wire, or one time period of production.

Next, collect data from at least 20 to 25 inspection units to establish the baseline. For each unit, count the total number of defects found. Calculate c-bar, the average number of defects per unit across all samples.

The control limits are calculated as follows:

• Center line (CL) = c-bar
• Upper control limit (UCL) = c-bar + 3√c-bar
• Lower control limit (LCL) = c-bar - 3√c-bar (or 0 if negative)

Plot each inspection unit's defect count on the chart against these limits. Points within the control limits indicate a stable process with only common cause variation. Points outside the limits — or patterns like runs, trends, or cycles — signal special cause variation that requires investigation.

Operators update the c-chart in real time or at regular intervals, plotting each new defect count as it is collected. When a point exceeds the upper control limit, the operator stops production and investigates the assignable cause. This immediate feedback loop prevents the manufacture of additional defective products.

C-Chart Example

A furniture manufacturer inspects each finished table for surface defects including scratches, dents, stain inconsistencies, and hardware misalignments. Each table represents one inspection unit. Over 20 consecutive tables, the defect counts are:

3, 2, 5, 4, 1, 3, 2, 6, 3, 4, 2, 3, 1, 4, 3, 2, 5, 3, 11, 4

The average (c-bar) is 3.55 defects per table. The control limits are:

• UCL = 3.55 + 3√3.55 = 3.55 + 5.65 = 9.20
• LCL = 3.55 - 5.65 = -2.10 → 0 (set to zero since counts cannot be negative)

Table number 19 had 11 defects, which exceeds the UCL of 9.20. This is an out-of-control signal. Investigation reveals that a new batch of stain was used starting at table 19, and the stain consistency was different from the previous batch, causing visible streaks counted as defects. The corrective action is to adjust the stain application procedure for the new batch and inspect incoming stain batches before use.

Why C-Charts Matter for Production Scheduling

C-charts provide early warning signals that directly affect production scheduling. When defect counts trend upward — even before hitting the upper control limit — schedulers should prepare for potential rework capacity needs. A single out-of-control point can mean an entire production run needs additional inspection or rework time.

Production scheduling software like Resource Manager DB enables planners to adjust schedules quickly when quality signals indicate problems. If a c-chart shows increasing defect rates on a particular work center, the scheduler can build additional inspection time into operations, schedule maintenance, or route work to alternative equipment.

The c-chart also helps schedulers understand normal process variation versus real problems. Not every high defect count requires a scheduling change — sometimes the count is within control limits and represents normal variation. The c-chart provides the statistical framework to distinguish between the two.

Related Terms

• Control Chart — the broader category of SPC charts that includes c-charts
• P-Chart — tracks proportion defective units rather than defect counts
• Attribute Data — the type of data used in c-charts and other attribute control charts

FAQ

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