Manufacturing Quality Control AI

How It Works

1. 1
   Extract Production Data

   Connect to NetSuite via MCP to retrieve past 24 hours of production records, quality inspection results, and defect codes across all production lines and shifts

   NetSuite MCP Connector
2. 2
   Statistical Analysis

   Calculate defect rates, dimensional variation statistics, and failure mode distributions, comparing against 30-day rolling averages and control limits

   Supabase Analytics
3. 3
   Pattern Recognition

   Submit aggregated data to Claude API for advanced pattern analysis, identifying correlations between defect types, production parameters, and timing patterns

   Claude API
4. 4
   Alert Generation

   Generate severity-based alerts for statistical outliers, emerging patterns, or control limit violations, with detailed root cause analysis suggestions

   Internal Logic
5. 5
   Notification Dispatch

   Send formatted alerts via Slack to quality managers and production supervisors with actionable recommendations and trend visualizations

   Slack API

Implementation

# Defect Pattern Detector Implementation

## File Structure
```
defect_detector/
├── main.py              # Primary execution script
├── data_extractor.py    # NetSuite MCP integration
├── pattern_analyzer.py  # Statistical and AI analysis
├── alert_generator.py   # Alert logic and formatting
├── slack_notifier.py   # Slack API integration
├── config.py           # Configuration management
├── requirements.txt    # Dependencies
└── logs/               # Execution logs
```

## Key Functions

### main.py
```python
import logging
from data_extractor import extract_production_data
from pattern_analyzer import analyze_patterns
from alert_generator import generate_alerts
from slack_notifier import send_notifications

def run_defect_detection():
    try:
        # Extract yesterday's production data
        production_data = extract_production_data()
        
        # Run pattern analysis
        patterns = analyze_patterns(production_data)
        
        # Generate alerts for significant patterns
        alerts = generate_alerts(patterns)
        
        # Send notifications if alerts exist
        if alerts:
            send_notifications(alerts)
    except Exception as e:
        logging.error(f"Defect detection failed: {e}")
```

### data_extractor.py
```python
from netsuite_mcp import NetSuiteConnector
import pandas as pd

def extract_production_data():
    connector = NetSuiteConnector()
    
    # Get production records
    production_query = """
    SELECT work_order, part_number, quantity_produced, 
           defect_count, inspection_date, shift_id
    FROM manufacturing_data 
    WHERE inspection_date >= CURRENT_DATE - INTERVAL '1 day'
    """
    
    return connector.query(production_query)
```

## Environment Variables
```bash
NETSUITE_API_KEY=your_netsuite_key
NETSUITE_SECRET=your_netsuite_secret
NETSUITE_ACCOUNT_ID=your_account_id
CLAUDE_API_KEY=your_claude_key
SUPABASE_URL=your_supabase_url
SUPABASE_KEY=your_supabase_anon_key
SLACK_BOT_TOKEN=your_slack_token
SLACK_CHANNEL=#quality-alerts
```

## Cron Setup
```bash
# Add to crontab for daily 6 AM execution
0 6 * * * cd /opt/defect_detector && python main.py >> logs/execution.log 2>&1
```

Data Flow

Prerequisites

• -NetSuite manufacturing module access
• -Quality control data with consistent defect coding
• -Claude API subscription
• -Supabase project with analytics capabilities

Error Handling