Industrial Statistical Process Control & Data Analysis
Master SPC: Analyze manufacturing data for continuous improvement
What You Can Do
Upload manufacturing process data and Claude will generate control charts, calculate process capability indices (Cp, Cpk, Pp, Ppk), detect special causes using Western Electric rules, and recommend statistical interventions for improvement. You'll receive actionable insights on whether your process is in statistical control and what corrective actions to prioritize.
Features
Create X-bar & R, I-MR, p, np, c, and u charts with automatic scale selection and trend visualization to monitor process performance over time.
Calculate Cp, Cpk, Pp, and Ppk indices to assess whether your manufacturing process meets specifications and identify capability gaps.
Identify out-of-control points using multiple detection rules (runs, trends, point placement) to pinpoint when intervention is needed.
Test for normality, detect outliers, check for autocorrelation, and flag data quality issues that affect statistical validity.
Receive prioritized corrective action recommendations based on which variation source is detected (common vs. special cause).
Support DMAIC and DMADV methodologies with dashboards, hypothesis testing, and structured improvement planning aligned to lean principles.
Analyze multiple production batches or time-series data to identify cyclical patterns, seasonal shifts, and gradual drift in performance.
Example Output
Control Chart Interpretation:
- Process Status: OUT OF CONTROL (point 47 exceeds upper control limit)
- Variation Type: Special Cause (equipment drift detected via 8-point upward trend)
- Recommended Action: Calibrate press alignment before next production run
Process Capability Report:
- Cpk = 0.87 (below target of 1.33) — Process cannot reliably meet ±3σ specification
- % Out of Spec (projected): 1.2% — Reduce variability by 38% to achieve Cpk ≥ 1.33
- Root Cause: High within-batch variation suggests tooling wear (R-chart shows upward drift)
Next Steps:
- Adjust tool offset (estimated 40% variation reduction)
- Reduce material supplier variability (20% opportunity)
- Re-sample after corrections to verify improvement
What's Included
- SPC Analysis Framework: Structured methodology for data collection, charting, interpretation, and intervention planning with decision trees for common scenarios.
- Control Chart Interpretation Guide: Western Electric rules, Nelson rules, and ASTM detection criteria with visual examples of each out-of-control pattern.
- Data Template Library: CSV and Excel templates for common chart types (I-MR for continuous, p-chart for pass/fail, c-chart for defect counts).
- Statistical Formulas & Calculators: Built-in computations for control limits, capability indices, and hypothesis tests — no manual calculation needed.
- Root Cause Decision Framework: Diagnostic flowchart to distinguish special causes (assignable variation) from common causes (inherent process variability).
- Improvement Tracking Templates: Before/after data collection sheets and KPI dashboards to measure impact of corrective actions over time.
Who It's For
- Quality Engineers
- Manufacturing Process Engineers
- Six Sigma Black Belts & Green Belts
- Plant Operations Managers
- Supply Chain Quality Specialists
Best For
- Manufacturing process capability studies
- Control chart analysis and out-of-control detection
- Root cause identification for process deviations
- Six Sigma DMAIC improvement projects
- Statistical process improvement planning and tracking
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