C Chart

Count of Defects Chart for monitoring defect occurrences in constant area of opportunity.

Chart Overview

What it monitors:

Count of defects (c) in items with constant inspection units or area of opportunity. Assumes each unit can have multiple defects and follows Poisson distribution.

Applications:

• • Surface defects per panel
• • Software bugs per module
• • Hospital infections per month
• • Safety incidents per shift
• • Scratch marks per automobile

Key Requirements:

• • Constant inspection unit size
• • Defects are countable events
• • Multiple defects possible per unit
• • Poisson distribution assumption
• • Independent defect occurrences

c̄ Statistic & Control Limits

Statistical Foundation:

The count of defects per unit, c, is modeled as a Poisson random variable with rate parameter λ estimated by c̄. Control limits follow the standard 3-sigma convention using the Poisson property that the variance equals the mean.

Center line (average count)

= defect count for a sample

= number of samples (subgroups)

Upper control limit

= average defect count (center line)

Lower control limit

LCL is set to 0 whenever the computed value is negative.

Key Assumptions

• • Defect counts follow a Poisson distribution
• • Area of opportunity (inspection unit) is constant across samples
• • Defects occur independently of one another
• • The mean defect rate is stable in the baseline period

Reading the c Chart

Example Chart & Signal Interpretation

sixsigmacenter.com Implementation Guide

Common Issues & Solutions

Example Data Layout for C Chart

Count defects in constant sampling units. One defect count per sample with consistent inspection criteria. Sample Size Recommendation: Minimum 20-25 samples for stable control limits, with average defect count ≥ 5 for adequate Poisson approximation.

This is how your spreadsheet or CSV file should be structured before you upload it. Match the column names, keep one row per observation, and leave the analysis to the platform.