Moving Range Chart
Control chart that monitors the variability between consecutive measurements using moving ranges
A Moving Range Chart monitors the variability in a process by tracking the absolute difference between consecutive measurements over a moving window. It's commonly used alongside Individual (I) charts to monitor both process location and variation.
Chart Overview & Applications
Moving Range Charts are particularly effective when subgrouping is not feasible or when dealing with individual measurements. They provide essential information about process variability changes over time.
When to Use:
- • Individual measurements without natural subgroups
- • Need to monitor process variability changes
- • Complement to Individual (I) charts
- • Detect changes in process consistency
- • Monitor measurement system stability
Key Characteristics:
- • Monitors short-term process variation
- • Uses consecutive measurement differences
- • Sensitive to variation changes
- • Always non-negative values
- • Typically paired with I-chart
Moving Range Formula & Control Limit Constants
Reading the Moving Range Chart
Example Chart & Signal Interpretation
In Control Signals
- • Moving ranges fluctuate randomly below UCL
- • No sustained upward trend in range values
- • Process variability appears stable over time
Out of Control Signals
- • A moving range point exceeds the UCL
- • Increasing trend suggesting equipment degradation
- • Sudden spike indicating measurement or process disruption
Always investigate the Moving Range chart before the Individual chart: an out-of-control range point means the process variance itself has shifted, which can distort the Individual chart's control limits until corrected.
Implementation on sixsigmacenter.com
Example Data Layout for Moving Range Chart
Individual measurements with moving ranges of different spans. Range calculations start when sufficient consecutive points are available. Sample Size Recommendation: Minimum 20-25 individual measurements for reliable moving range control limits - adequate baseline ensures stable process variation estimation for short-term variability monitoring.
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.