algo-mfg-spc

Solid

"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'.".

AI & Automation 22 stars 8 forks Updated 1 weeks ago MIT

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Skill Content

# 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 subgr...

Details

Author
charlieviettq
Repository
charlieviettq/awesome-agent-skill
Created
2 months ago
Last Updated
1 weeks ago
Language
Python
License
MIT

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