CP Control Plan Analyzer

Turning control plans into analyzable objects

Quality issues often do not start from missing checks; they come from CP control plans scattered across templates, Excel sheets, and review records, making reuse difficult. Engineers may still manually collect measured values, specification limits, and process parameters, then calculate CP, CPK, and risk. This skill turns CP work into a repeatable workflow: generate a structured template aligned with IATF 16949 or industry formats, then import CSV/Excel data for cleaning, validation, and key metric extraction.

Core capabilities and workflow

• Template generation: extract key steps from process flow diagrams and populate KPC, KPP, measurement methods, poka-yoke, inspection, SPC, reaction plans, and responsibilities.
• Data analysis: use pandas, openpyxl, and numpy to handle missing values and anomalies, then calculate mean, standard deviation, CP, CPK, PP, and PPK.
• Risk alerts: evaluate RPN = S × O × D and flag cases such as CPK < 1.0, 2 sigma trend anomalies, and drift with points on one side of the center line.
• Visualization and versioning: create CPK dashboards, X-bar/R/S control charts, histograms, and risk heatmaps, and guide naming, review, approval, and ECN change control.

Boundaries and notes

It fits scenarios where the process flow, special characteristics list, and structured quality data are available. Fields should include measured values, specification limits, and process parameters; risk thresholds can be adjusted to enterprise standards. The output is best used as analysis drafts, report material, and process guidance, while formal approval still requires the company quality system and human review.