---
name: Statistical Process Control
slug: statistical-process-control
category: Quality
description: Statistical Process Control implements control charts to monitor process stability over time and flag special-cause variation. Use it to set control limits, detect shifts, and tell whether a process is in control.
github: "https://github.com/asgard-ai-platform/skills/tree/main/algo-mfg-spc"
language: Python
stars: 228
forks: 28
install: "npx degit https://github.com/asgard-ai-platform/skills/tree/main/algo-mfg-spc ~/.claude/skills/algo-mfg-spc"
installs_to: ~/.claude/skills/algo-mfg-spc
source_path: algo-mfg-spc/SKILL.md
collection_size: 25
category_size: 1478
collection_url: "https://dirskills.com/collections/asgard-ai-platform/skills"
added: 2026-09-03T06:04:52.126Z
last_synced: 2026-09-03T06:04:52.126Z
canonical_url: "https://dirskills.com/skills/statistical-process-control"
---

# Statistical Process Control

Statistical Process Control implements control charts to monitor process stability over time and flag special-cause variation. Use it to set control limits, detect shifts, and tell whether a process is in control.

**Install:**

```bash
npx degit https://github.com/asgard-ai-platform/skills/tree/main/algo-mfg-spc ~/.claude/skills/algo-mfg-spc
```

## README

# Statistical Process Control

## Overview

SPC uses control charts to monitor process stability over time. Upper and Lower Control Limits (UCL/LCL) are set at ±3σ from the process mean. Points within limits = common cause variation (stable). Points outside or showing patterns = special cause variation (investigate). Primary charts: X-bar/R, X-bar/S, I-MR, p-chart, c-chart.

## When to Use

**Trigger conditions:**
- Monitoring production process for stability and detecting shifts
- Setting statistically-based control limits for quality metrics
- Distinguishing normal variation from assignable causes

**When NOT to use:**
- For process capability assessment (use Cpk)
- For root cause analysis of known problems (use fishbone/5-why)

## Algorithm

```
IRON LAW: Control Limits Are NOT Specification Limits
Control limits (±3σ) describe what the process IS doing.
Specification limits describe what the process SHOULD do.
A process can be in statistical control (stable) but still produce
out-of-spec products (incapable). Conversely, a capable process may
be out of control (drifting). Monitor control FIRST, then assess capability.
```

### Phase 1: Input Validation
Collect: 25+ subgroups of measurements (5 per subgroup typical for X-bar/R). Verify: measurement system is adequate (gauge R&R < 10%), data collected in time order.
**Gate:** Sufficient subgroups, time-ordered data, measurement system verified.

### Phase 2: Core Algorithm
**X-bar/R Chart (subgroup data):**
1. Compute subgroup means (X̄) and ranges (R)
2. Compute grand mean (X̄̄) and average range (R̄)
3. UCL_X̄ = X̄̄ + A₂×R̄, LCL_X̄ = X̄̄ - A₂×R̄ (A₂ from statistical tables by subgroup size)
4. UCL_R = D₄×R̄, LCL_R = D₃×R̄
5. Plot points, apply Western Electric rules for out-of-control signals

### Phase 3: Verification
Check for: points outside limits, runs (7+ consecutive on one side), trends (7+ consecutive increasing/decreasing), 2 of 3 beyond 2σ, 4 of 5 beyond 1σ.
**Gate:** Chart constructed, out-of-control signals identified.

### Phase 4: Output
Return control chart data with signals and stability assessment.

## Output Format

```json
{
  "chart": {"type": "xbar_r", "center_line": 50.2, "ucl": 52.1, "lcl": 48.3},
  "signals": [{"subgroup": 18, "rule": "point_beyond_ucl", "value": 52.8}],
  "stability": "out_of_control",
  "metadata": {"subgroups": 30, "subgroup_size": 5}
}
```

## Examples

### Sample I/O
**Input:** 25 subgroups of 5 measurements each, all within ±3σ, no patterns
**Expected:** Process in control. No signals triggered.

### Edge Cases
| Input | Expected | Why |
|-------|----------|-----|
| One point just outside UCL | Signal, but may be false alarm | ~0.27% chance per point even when in control |
| Gradual upward trend | Trend rule triggered | Process drifting, investigate |
| All points near center | Suspicious — check data | May indicate data manipulation or measurement issue |

## Gotchas

- **Rational subgrouping**: Subgroups must be collected under similar conditions (same shift, machine, operator). Poor subgrouping inflates within-group variation, making limits too wide.
- **Recalculating limits**: Don't recalculate limits every time you add data. Establish limits from a stable baseline period and keep them fixed until a known process change.
- **Chart type selection**: Variables data (measurements) → X-bar/R or I-MR. Attribute data (counts/proportions) → p-chart, np-chart, c-chart, u-chart. Wrong chart type = wrong limits.
- **Normality assumption**: X-bar chart is robust to non-normality (central limit theorem). Individual charts (I-MR) require approximate normality — check with histogram.
- **Over-adjustment**: Reacting to every small variation (tampering) INCREASES variability. Only investigate special cause signals, not common cause variation.

## References

- For control chart constants tables, see `references/chart-constants.md`
- For Western Electric rules and pattern detection, see `references/we-rules.md`