The c-chart (also C Chart) is used to monitor a process over time based on the number of defects per subgroup. The process is described by a key figure that indicates how many defects, complaints, or anomalies occur in a subgroup in total. The prerequisite is that the subgroup size or the inspection possibility remains constant.
This can be, for example, the number of labeling errors per shift, documentation deficiencies in a fixed ticket sample, or packaging deficiencies in a constant shipment sample. The goal is to detect changes early, systematically analyze possible causes, build process knowledge, and avoid unnecessary interventions.
In the filling of tomato sauce, the same sample of 200 jars is always checked per shift. It is recorded how many label errors occur in total – a jar can have multiple errors (crooked label, creases, incomplete batch labeling). The goal is to determine whether the number of label errors per shift remains stable over time.
Interpretation of the results:
There are no points outside the control limits and no noticeable patterns are visible. The number of label errors per shift fluctuates randomly around the centerline – the process can be considered stable.
Explanations of the graphic:
- The points show the number of errors per subgroup in chronological order.
- The centerline corresponds to the average number of errors per subgroup.
- The control limits are calculated from the average number of errors.
Preparation
- Clearly define which events are counted as errors.
- Ensure that the total number of errors occurring per data line is available.
- Check if the subgroup size remains constant.
Usage in AlphadiTab
- Select the c-chart tool in the Measure Phase or Control Phase.
- Enter the number of errors per subgroup.
- Generate the chart with the "Create New" button.
Interpretation
- Are points outside the control limits?
- Are non-random patterns recognizable?
Historical values
If historical reference values are known, they can be used as a fixed basis. The centerline and control limits then remain constant.
Sections
Sections are useful if the process has deliberately changed, e.g., after a supplier change or a process adjustment. Separate centerlines and control limits are calculated for each section.
Non-random patterns are detected with the tests:
Documentation deficiencies per daily sample
In IT service, a constant sample of 40 completed tickets is checked per day. It is recorded how many documentation deficiencies occur in total (missing mandatory field, unclear category, missing closing note). The c-chart helps to assess whether the number of deficiencies in this daily sample remains stable.
Interpretation
No point outside the control limits and no Nelson test triggers. At the same time, a recurring pattern is recognizable: at intervals of seven days, the error numbers are higher.
→ Statistically inconspicuous, but 7-day pattern – possible weekday effect to check.
Formal Defects per Offer Sample
In sales, a constant sample of 30 offers is checked monthly. It records how many formal defects occur in total (missing price validity, incomplete delivery conditions, missing approvals). This allows tracking whether the number of formal defects remains consistently stable.
Interpretation
The values are close together over a long period; additionally, nine consecutive points are on the same side of the centerline. The pattern is not random.
→ Noticeably close values + 9 points on one side – question the inspection system.
Packaging defects per tour sample
In the logistics sector, a constant sample of 50 shipments is checked per tour. It is recorded how many packaging defects occur in total (damaged corners, faulty labels, insufficient securing). The goal is to detect extraordinary loads early.
Interpretation
An outlier is recognizable; the 13th tour is affected. A rear-end collision was reported for this tour – the deviation can be explained by a known special cause.
→ Outlier due to known special cause (accident) – no new basic pattern.
Complaints per Period
In purchasing, a constant sample of 40 goods receipts is evaluated per period. It is recorded how many complaints occur in total. During the observation period, there was a switch from Supplier A to Supplier B, so two sections are sensible.
Interpretation
After the supplier change, the number of complaints is at a noticeably higher level. The separate consideration of the sections shows a change in the process level.
→ Level shift after supplier change – evaluate sections separately.
Planning Warnings per Cycle
In production planning, a constant sample of 60 positions is considered per planning cycle. It records how many planning warnings occur in total. The c-chart shows whether the number of warnings changes over time.
Interpretation
An increasing trend is noticeable over the period. Since the values decrease in between, no trend is signaled according to the Nelson rules.
→ Slightly increasing trend, but no Nelson rule violated – continue to observe.
With historical fix, c̄ is replaced by the given reference value.