ANALYZE-005: Picking efficiency analysis

[AliiiBenn]

ANALYZE-005: Picking Efficiency Analysis

Overview

Implement picking efficiency analysis to evaluate operator performance, pick rates, travel distance optimization, and picking patterns.

Description

Picking efficiency analysis measures how efficiently warehouse operations pick orders:

• Operator performance (picks per hour, accuracy)
• Pick rate by product type (fast/slow movers)
• Picking patterns and routes
• Zone efficiency analysis
• Time-of-day performance

Technical Approach

Operator Performance Query

def analyze_picking_efficiency(conn, lookback_days=30):
    """
    Analyze picking efficiency metrics.

    Returns:
        Dict with picking efficiency analytics
    """
    analytics = {}

    # 1. Operator performance
    analytics['by_operator'] = pd.read_sql_query(f"""
        SELECT
            operateur as operator_id,
            COUNT(*) as total_picks,
            SUM(quantite) as total_quantity,
            COUNT(DISTINCT DATE(date_heure)) as working_days,
            ROUND(
                CAST(COUNT(*) AS REAL) / NULLIF(COUNT(DISTINCT DATE(date_heure)), 0),
                1
            ) as picks_per_day,
            MIN(date_heure) as first_pick,
            MAX(date_heure) as last_pick
        FROM mouvements
        WHERE type = 'SORTIE'
          AND operateur IS NOT NULL
          AND date_heure >= date('now', '-{lookback_days} days')
        GROUP BY operateur
        ORDER BY total_picks DESC
    """, conn)

    # 2. Hourly pick rate
    analytics['hourly_pick_rate'] = pd.read_sql_query(f"""
        SELECT
            CAST(strftime('%H', date_heure) AS INTEGER) as hour,
            COUNT(*) as pick_count,
            ROUND(AVG(quantite), 2) as avg_quantity_per_pick
        FROM mouvements
        WHERE type = 'SORTIE'
          AND date_heure >= date('now', '-{lookback_days} days')
        GROUP BY hour
        ORDER BY hour
    """, conn)

    # 3. Zone picking efficiency
    analytics['by_zone'] = pd.read_sql_query(f"""
        SELECT
            source_zone,
            COUNT(*) as pick_count,
            ROUND(AVG(quantite), 2) as avg_quantity,
            COUNT(DISTINCT no_produit) as unique_products
        FROM mouvements
        WHERE type = 'SORTIE'
          AND source_zone IS NOT NULL
          AND date_heure >= date('now', '-{lookback_days} days')
        GROUP BY source_zone
        ORDER BY pick_count DESC
    """, conn)

    return analytics

Output Format

Terminal Output

$ wareflow analyze --picking

⚡ Picking Efficiency Analysis (Last 30 days)
━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━

Operator Performance (Top 10):
┌──────────┬──────────┬─────────────┬────────────┬────────────┐
│ Operator │ Picks    │ Picks/Day   │ Total Qty  │ Days Active│
├──────────┼──────────┼─────────────┼────────────┼────────────┤
│ OP-001   │ 2,345    │ 117.3 🔥    │ 12,456     │ 20         │
│ OP-002   │ 2,123    │ 106.2 🔥    │ 11,234     │ 20         │
│ OP-003   │ 1,987    │ 108.2 🔥    │ 10,567     │ 18         │
│ OP-004   │ 1,654    │ 94.5        │ 8,765      │ 18         │
│ OP-005   │ 1,543    │ 89.6        │ 8,234      │ 17         │
└──────────┴──────────┴─────────────┴────────────┴────────────┘

Picking Metrics:
  Average: 92.3 picks/operator/day
  Top performer: OP-001 (117.3 picks/day, +27% vs avg)
  Total picks: 45,678
  Working operators: 23

Hourly Pick Rate:
  Peak: 14:00-15:00 (1,234 picks, 8.2% of daily)
  Low: 01:00-02:00 (12 picks, 0.08% of daily)
  Best efficiency: 09:00-10:00 (145 picks/hour/operator)

Zone Analysis:
  Zone A (Fast movers): 23,456 picks (51%) ⚡
  Zone B (Medium movers): 15,678 picks (34%)
  Zone C (Slow movers): 6,544 picks (14%)

Recommendations:
  🏆 Recognize OP-001, OP-002, OP-003 (top performers)
  💡 Consider cross-training for low performers
  💡 Investigate 14:00-15:00 peak (resource constraints?)
  💡 Zone A has highest volume - ensure proximity to shipping

Implementation Plan

Phase 1: Core Analytics (1-2 days)

• Operator performance query
• Hourly pick rate analysis
• Zone efficiency calculation
• Format terminal output

Phase 2: Enhanced Insights (1 day)

• Pick accuracy rate (if quality data available)
• Trend analysis (improving/declining)
• Comparison between operators
• Benchmarking (operator vs avg)
• Export to Excel with highlighting

CLI Usage

# Picking efficiency overview
wareflow analyze --picking

# Operator analysis only
wareflow analyze --picking --operators

# Zone analysis
wareflow analyze --picking --zones

# Hourly patterns
wareflow analyze --picking --hourly

# Custom lookback period
wareflow analyze --picking --days 60

# Export to Excel
wareflow analyze --picking --export picking.xlsx

Key Metrics

Operator Performance

Metric	Formula	Description
Total Picks	COUNT(*) WHERE type='SORTIE'	Total pick transactions
Picks/Day	picks / working_days	Daily productivity
Avg Quantity	AVG(quantite)	Average per pick
Working Days	COUNT(DISTINCT date)	Days active

Efficiency Benchmarks

Performance	Picks/Day	Rating
Excellent	> 110	🔥 Top performer
Good	90-110	✅ Above average
Average	70-89	📊 At average
Below Average	< 70	⚠️ Needs improvement

Success Criteria

• Calculate operator performance metrics
• Determine picks per day per operator
• Identify top/low performers
• Analyze hourly pick patterns
• Calculate zone efficiency
• Provide actionable insights
• Support custom lookback periods
• Export to Excel with conditional formatting

Future Enhancements

• Pick Accuracy: Error rate by operator
• Travel Distance: Average distance per pick (if location data available)
• Pick Time: Actual time per pick (if timestamped)
• Route Optimization: Suggest efficient picking routes
• Batch Picking: Analyze multi-order picking efficiency
• Zone Balancing: Optimize zone assignments

Dependencies

Required

• CORE-002 (analyze command)
• Movement data with operator IDs

Optional (for enhanced features)

• Location data (zones, aisles, bays)
• Pick timestamps (start/end time)
• Quality data (pick errors)

Related Issues

• Depends on: CORE-002
• Related to: ANALYZE-002 (Product Performance)
• Related to: ANALYZE-003 (Movement Analytics)

References

• Movement schema: docs/SCHEMA.md
• Picking operations: Warehouse management best practices

Notes

This analysis is sensitive but valuable:

• Helps identify training opportunities
• Enables performance recognition
• Supports resource scheduling
• Drives process optimization

Privacy consideration: Operator performance data should be handled carefully. Consider anonymizing in reports.

Key insights for warehouse managers:

• "Who are our top performers?" → Recognition and best practices
• "Who needs training?" → Skill development
• "When are we most efficient?" → Scheduling optimization
• "Which zones are most active?" → Layout optimization