What is it, and why use it?
A control chart plots observations in time order against limits estimated from a chosen baseline. It helps distinguish routine variation from patterns that warrant investigation.
It supports more disciplined responses than treating every rise or fall as a process change.
Explore the Control Chart Builder features and local-file workflowWhen to use it
Use it when repeated observations have comparable definitions and a defensible sequence or subgroup structure.
When another tool fits better
Use Pareto for category priorities and Process Capability to compare a sufficiently supported process model with specifications. Control limits are not customer acceptance limits.
Before you start
- Time-ordered measured values, or counts with appropriate denominators/exposure.
- A justified chart family, subgroup structure and stable measurement method.
- A baseline period that can be reviewed, with documented exclusions.
Use fictional or approved non-sensitive information. Do not include patient details or proprietary records in practice worksheets.
Step-by-step in CI Tools
Choose the chart matching the measured values or count/exposure structure; review eligibility and subgroup size.
Enter observations in chronological order. Review missing values, denominators and warnings before calculating.
Review the baseline observations and capture an explicit baseline with justification. Frozen baseline records do not change when monitoring values are added.
Add monitoring observations and inspect the documented signal rules and phase information. Investigate signals using process knowledge.
Keep specification limits separate and save the editable records, baseline snapshots and rules to your computer.
A frozen baseline supports a consistent comparison
Capture the reviewed period and record why it is appropriate. Monitoring values are compared with that snapshot rather than quietly redefining it. A signal does not automatically identify a cause; specifications and capability require separate judgments.
Completed worked examples
Demonstrations only: all organizations, observations, targets and results below are fictional. They are not clinical instructions or universal performance standards.
Healthcare operations — fictional
Administrative processing time
Starting information: A fictional referral team has 24 baseline individual handling times in minutes: 9, 10, 11, 10 repeated six times. The demonstration treats the baseline as comparable observations.
| Baseline | Mean = 10 minutes; average moving range = 1 minute |
|---|---|
| Individuals limits | Approximately 7.34 to 12.66 minutes using the individuals/moving-range model |
| New monitoring value | 14 minutes exceeds the upper control limit; review the observation and process conditions |
What the result means: This short synthetic teaching series illustrates a signal, not a recommended production baseline. A point beyond the limit does not prove a particular cause; investigate recording accuracy and process conditions, and review the baseline and model assumptions.
General business — fictional
Packaging defect proportion
Starting information: A fictional supplier has 20 baseline samples of 100 inspected units each, with defective-unit counts 8, 10, 12, 10 repeated five times. The pooled baseline proportion is 200/2,000 = 10%.
| Sample of 80 | 18 defective units = 22.5%; upper proportion-chart limit is approximately 20.06% |
|---|---|
| Sample of 120 | 12 defective units = 10%; upper limit is approximately 18.22% |
| Completed conclusion | The first sample exceeds its upper limit; the second does not. Investigate the first sample without claiming a specific cause. |
What the result means: The upper limits vary because the sample denominators differ. These are synthetic binomial-model calculations, not specification limits. Count defective units once per unit; multiple defect occurrences need a different count/exposure model. Review independence and model suitability.
Practise in a separate worksheet
Open the tool’s built-in synthetic sample as a new editable worksheet. It may differ from the worked demonstrations above. It never replaces an existing worksheet. Save any work you want to retain to your computer.
Open built-in practice worksheetYou’ll be asked to log in if needed. No worksheet is created just by reading this page.
Common mistakes and how to avoid them
- Plotting categories instead of time order: use an appropriate sequence.
- Using specifications as control limits: separate requirements from estimated variation.
- Recalculating the baseline whenever a signal appears: investigate and document deliberate phase changes.
Interpret the result
A signal identifies a pattern worth investigating under the documented rule set. No signal does not prove stability or acceptable performance. Check model assumptions, baseline quality and the limited implemented rule set before interpreting results.
Record the assumptions and unanswered questions alongside the result. Use the relevant local review process before making operational decisions.
Credible further learning
These independent educational resources explain the methods. They do not endorse CI Tools Suite. Some external resources have their own access requirements.
Resource links reviewed 29 October 2026. Our interface instructions and fictional examples are original CI Tools learning material.
Put the method into practice
Start a new worksheet, use the guide beside your work and keep an editable .citools copy on your computer.
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