SPC (Statistical Process Control) charts
SPC (Statistical Process Control) charts
📊 1. X̄-R Chart (Mean and Range Chart)
Purpose:
- Monitors process average (X̄) and variability (R) over time.
- Suitable for small subgroup sizes (typically 2–10).
Components:
- X̄ chart: Tracks the mean of each subgroup.
- R chart: Tracks the range (max – min) within each subgroup.
Use Case:
- Ideal for manual measurements or small batch processes.
📊 2. X̄-S Chart (Mean and Standard Deviation Chart)
Purpose:
- Similar to X̄-R but uses standard deviation (S) instead of range.
- More accurate for larger subgroup sizes (typically >10).
Components:
- X̄ chart: Tracks subgroup means.
- S chart: Tracks subgroup standard deviations.
Use Case:
- Automated measurements or continuous processes with larger data sets.
📊 3. p-Chart (Proportion Chart)
Purpose:
- Monitors the proportion of defective items in a sample.
Components:
- p = number of defectives / sample size
- Control limits vary with sample size.
Use Case:
- Visual inspection processes, where items are classified as pass/fail.
📊 4. np-Chart (Number of Defectives Chart)
Purpose:
- Tracks the number of defective items in samples of constant size.
Components:
- Easier to interpret than p-chart when sample size is fixed.
Use Case:
- Assembly line inspections with fixed sample sizes.
📊 5. c-Chart (Count of Defects Chart)
Purpose:
- Monitors the number of defects per unit (not necessarily defective items).
Components:
- Used when multiple defects can occur on a single item.
Use Case:
- Surface inspections, welds, or printed circuit boards.
📊 6. u-Chart (Defects per Unit Chart)
Purpose:
- Tracks the average number of defects per unit, useful when sample size varies.
Use Case:
- Variable-sized lots or units with different inspection areas.
📐 Choosing the Right Chart
| Chart Type | Data Type | Subgroup Size | Use Case |
|---|---|---|---|
| X̄-R | Continuous | 2–10 | Small batch measurements |
| X̄-S | Continuous | >10 | Automated or large samples |
| p | Attribute | Variable | Proportion of defectives |
| np | Attribute | Constant | Count of defectives |
| c | Attribute | Constant | Count of defects |
| u | Attribute | Variable | Defects per unit |
Small interpretation of these carts :
📊 1. X̄-R and X̄-S Charts (for continuous data)
What to look for:
- X̄ chart shows the average of each subgroup.
- R chart (or S chart) shows the variability within each subgroup.
Interpretation:
- In control: All points are within control limits, and no patterns (e.g., trends, cycles).
- Out of control: Points outside limits or patterns like:
- 7 consecutive points above/below center line
- 2 out of 3 points near control limits
- Sudden shifts or trends
Action:
- Investigate special causes (machine issues, operator changes, material variation).
- If only the R chart is out of control, the process variability is unstable.
- If only the X̄ chart is out of control, the process mean is shifting.
📊 2. p-Chart and np-Chart (for attribute data)
What to look for:
- p-chart tracks proportion of defectives.
- np-chart tracks number of defectives (used when sample size is constant).
Interpretation:
- In control: Proportion or count of defectives stays within limits.
- Out of control: Sudden spikes or drops, or consistent upward/downward trends.
Action:
- Check for changes in inspection criteria, operator performance, or incoming material quality.
📊 3. c-Chart and u-Chart (for defect counts)
What to look for:
- c-chart: Number of defects per unit (fixed sample size).
- u-chart: Defects per unit (variable sample size).
Interpretation:
- In control: Defect counts fluctuate randomly within limits.
- Out of control: Unusual spikes or consistent increases/decreases.
Action:
- Investigate process steps where defects originate.
- Consider preventive actions or process redesign.
🔍 General Signs of Trouble Across All Charts
| Pattern | Meaning |
|---|---|
| Point outside control limits | Likely special cause |
| Run of 7+ points on one side of center line | Process shift |
| Trend (e.g., 6 points increasing/decreasing) | Gradual drift |
| Cycles or repeating patterns | External influence (e.g., shift changes) |
| Sudden jump | Change in process or measurement system |
SPC (Statistical Process Control) charts