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Process Compliance Temperature Analysis
Quality control analysis of manufacturing temperature data against specifications. Features histogram with spec limits, process capability metrics (Cp/Cpk), and tiered corrective action recommendations.
Input
What you provide to the skill
5000 temperature measurements from our manufacturing process. Spec: 98.6°C ± 2°C. Show the production team if we’re compliant.
Output
What the skill generates for you
Smart Chart Recommender Analysis
Manufacturing Process Temperature Compliance
Data Analysis Summary
Dataset: 5000 temperature measurements from manufacturing process
Specification: 98.6°C ± 2.0°C (acceptable range: 96.6°C to 100.6°C)
Audience: Production team (technical stakeholders)
Goal: Show compliance with quality standards
Data Quality Report
✓ No data quality issues detected
- Missing values: 0 (0.0%)
- Statistical outliers: 27 (0.54%) - within acceptable range
Key Statistics:
- Mean: 98.70°C
- Std Dev: 0.642°C
Recommended Chart #1: Histogram with Normal Curve and Spec Limits ⭐ BEST CHOICE
Why it works:
Industry-standard quality control visualization. Shows complete distribution shape, process centering, and percentage within specification limits.
What it shows:
- Full distribution of all 5000 measurements
- Whether process is centered on target (98.6°C)
- Percentage within vs. outside spec limits
- Outliers and extreme values
Best practices:
- 40 bins for 5000 data points
- Overlay fitted normal distribution curve
- Vertical lines for spec limits (red dashed) and target (green solid)
- Shaded out-of-spec regions
- Text box with: n, mean, std dev, in-spec %, Cp, Cpk
Recommended Chart #2: Box Plot with Violin Overlay
Why it works:
Compact summary for dashboards or batch comparisons.
When to use:
Executive summaries or comparing this batch to previous batches.
Charts to AVOID
❌ Bar Chart - Continuous measurements should never be binned into categories
❌ Pie Chart - Makes no sense for measurement distributions
❌ Line Chart - Only if measurements have meaningful sequential order
Key Findings
1. OVERALL COMPLIANCE: 99.46% In-Spec ✓
- 4,973 out of 5,000 within 96.6-100.6°C
- 27 measurements out of spec (0.54%)
2. PROCESS CENTERING: Slight Upward Shift
- Mean: 98.70°C (target: 98.6°C)
- Process running 0.10°C above target
3. PROCESS CAPABILITY:
- Cp = 1.04: Adequate inherent capability
- Cpk = 0.99: Slightly below 1.0 due to off-centering
- Process is CAPABLE but centering reduces safety margin
Recommended Actions
IMMEDIATE:
- Investigate the 0.10°C upward shift - check calibration
- Root cause analysis on 27 out-of-spec measurements
SHORT-TERM:
- Re-center process to 98.6°C target (adjust setpoint down 0.10°C)
- Implement continuous monitoring with control charts
LONG-TERM:
- Reduce variation further - target std dev of 0.55°C for Cp > 1.2
Bottom Line
✓ You ARE compliant (99.46% in-spec)
⚠️ But you’re close to the edge (Cpk < 1.0)
🎯 Quick win available: Re-center by -0.10°C to improve capability
About This Skill
Get intelligent chart type recommendations based on your data structure and visualization goals, plus publication-ready visualizations with matplotlib/seaborn.
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