Skip to content

SKILL.md

Latest commit

 

History

History
91 lines (70 loc) · 4.38 KB

SKILL.md

File metadata and controls

91 lines (70 loc) · 4.38 KB
Raw
name algo-mfg-spc
description Implement Statistical Process Control charts to monitor production process stability. Use this skill when the user needs to detect process shifts, set control limits, or distinguish common cause from special cause variation — even if they say 'process monitoring', 'control chart', or 'is our process in control'.
metadata
category tags
WP-48 製造演算法
manufacturing
spc
control-chart
quality

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

{
  "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