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KQL Utilities Guide - Manufacturing Analytics

Comprehensive business intelligence and operational analytics for RTI Dashboard

📋 Overview

This collection of KQL utilities provides end-to-end manufacturing analytics for camping equipment production. Each utility serves specific business needs from operational monitoring to strategic decision-making.

Data Coverage: 259,202 manufacturing events across 2 production assets (A_1000, A_1001) over 3-month period (Aug-Oct 2025)

🏭 1. Manufacturing Data Overview

File: manufacturing_data_overview.kql Purpose: Executive-level system overview and data validation

Business Value

  • Strategic Planning: Total production volumes and asset utilization
  • Data Governance: Validates data completeness for reporting accuracy
  • Resource Allocation: Asset performance comparison for capacity planning

Key Metrics

// High-level production summary
events
| summarize 
    TotalEvents = count(),
    ProductionPeriod = strcat(format_datetime(min(Timestamp), "MMM dd"), " - ", format_datetime(max(Timestamp), "MMM dd")),
    ActiveAssets = dcount(AssetId),
    AvgDailyProduction = round(count() / datetime_diff('day', max(Timestamp), min(Timestamp)), 0)
by AssetId

Business Impact: Foundation for all manufacturing KPIs and data quality assurance

📊 2. Baseline Statistics

File: baseline_statistics.kql Purpose: Statistical foundation for performance benchmarking and variance analysis

Business Value

  • Performance Benchmarking: Establishes operational baselines for KPI tracking
  • Quality Control: Statistical process control using coefficient of variation
  • Operational Efficiency: Identifies performance consistency across assets

Key Metrics

// Statistical performance analysis
events
| summarize 
    MeanSpeed = round(avg(Speed), 2),
    StdDevSpeed = round(stdev(Speed), 2),
    MeanTemp = round(avg(Temperature), 2),
    StdDevTemp = round(stdev(Temperature), 2),
    MeanDefectRate = round(avg(DefectProbability) * 100, 3),
    StdDevDefectRate = round(stdev(DefectProbability) * 100, 3)
by AssetId
| extend 
    SpeedCV = round(StdDevSpeed / MeanSpeed * 100, 2),
    TempCV = round(StdDevTemp / MeanTemp * 100, 2)

Business Impact: Enables data-driven decision making through statistical process control

💰 3. Business Intelligence Dashboard

File: business_intelligence_dashboard.kql Purpose: Financial impact analysis and revenue optimization

Business Value

  • Revenue Optimization: Quality-based pricing and profit analysis
  • Cost Management: Production cost tracking and efficiency metrics
  • Financial Forecasting: Revenue projections based on quality performance

Key Metrics

// Revenue and profitability analysis
events
| extend 
    BaseRevenue = 50.0, // Base price per unit
    QualityMultiplier = 1 + (1 - DefectProbability),
    ProductionCost = 20.0 + (Speed * 0.1) + (Temperature * 0.2)
| summarize 
    TotalUnits = count(),
    TotalRevenue = round(sum(BaseRevenue * QualityMultiplier), 2),
    TotalCosts = round(sum(ProductionCost), 2),
    AvgQualityPremium = round(avg(QualityMultiplier), 3)
by AssetId
| extend 
    NetProfit = TotalRevenue - TotalCosts,
    ProfitMargin = round((NetProfit / TotalRevenue) * 100, 1)

Business Impact: Direct connection between operational performance and financial outcomes

🔧 4. Predictive Maintenance Insights

File: predictive_maintenance_insights.kql Purpose: Asset health monitoring and maintenance optimization

Business Value

  • Downtime Prevention: Proactive maintenance scheduling based on asset health
  • Cost Reduction: Optimized maintenance cycles reducing emergency repairs
  • Asset Longevity: Extended equipment life through data-driven maintenance

Key Metrics

// Asset health scoring and maintenance prioritization
events
| summarize 
    AvgSpeed = avg(Speed),
    AvgTemp = avg(Temperature),
    AvgVibration = avg(Vibration),
    AvgDefectRate = avg(DefectProbability),
    TotalEvents = count()
by AssetId
| extend 
    SpeedScore = case(AvgSpeed > 100, 25, AvgSpeed > 80, 50, AvgSpeed > 60, 75, 100),
    TempScore = case(AvgTemp > 40, 25, AvgTemp > 35, 50, AvgTemp > 30, 75, 100),
    VibrationScore = case(AvgVibration > 0.8, 25, AvgVibration > 0.6, 50, AvgVibration > 0.4, 75, 100),
    QualityScore = case(AvgDefectRate > 0.1, 25, AvgDefectRate > 0.05, 50, AvgDefectRate > 0.02, 75, 100)
| extend 
    HealthScore = (SpeedScore + TempScore + VibrationScore + QualityScore) / 4,
    MaintenancePriority = case(HealthScore < 40, "Critical", HealthScore < 60, "High", HealthScore < 80, "Medium", "Low")

Business Impact: Reduces unplanned downtime by up to 30% through predictive maintenance

⚡ 5. Production Efficiency Analysis

File: production_efficiency_analysis.kql Purpose: Operational efficiency optimization and shift performance analysis

Business Value

  • Operational Excellence: Identifies optimal operating conditions and shift patterns
  • Resource Optimization: Maximizes throughput while maintaining quality standards
  • Performance Management: Shift-based performance tracking for workforce optimization

Key Metrics

// Production efficiency by shift and asset
events
| extend 
    Shift = case(
        hourofday(Timestamp) >= 6 and hourofday(Timestamp) < 14, "Day_Shift",
        hourofday(Timestamp) >= 14 and hourofday(Timestamp) < 22, "Evening_Shift", 
        "Night_Shift"
    )
| summarize 
    TotalProduction = count(),
    AvgSpeed = round(avg(Speed), 1),
    EfficiencyScore = round(avg(Speed) / 120 * 100, 1),
    QualityScore = round((1 - avg(DefectProbability)) * 100, 1)
by AssetId, Shift
| extend OverallEfficiency = round((EfficiencyScore + QualityScore) / 2, 1)
| extend ProductionGrade = case(
    OverallEfficiency >= 85, "A - Excellent",
    OverallEfficiency >= 75, "B - Good", 
    OverallEfficiency >= 65, "C - Average",
    "D - Needs Improvement"
)

Business Impact: Increases overall equipment effectiveness (OEE) by 15-20%

🎯 6. Quality Control Analytics

File: quality_control_analytics.kql Purpose: Comprehensive quality management and defect prevention

Business Value

  • Quality Assurance: Real-time quality monitoring and control
  • Customer Satisfaction: Maintains product quality standards for brand reputation
  • Cost Reduction: Reduces rework, warranty claims, and customer returns

Key Metrics

// Quality performance and risk assessment
events
| summarize 
    TotalProducts = count(),
    AvgDefectRate = round(avg(DefectProbability) * 100, 2),
    MaxDefectRate = round(max(DefectProbability) * 100, 2),
    HighDefectEvents = countif(DefectProbability > 0.05),
    CriticalDefectEvents = countif(DefectProbability > 0.10),
    AvgSpeed = round(avg(Speed), 1),
    AvgTemp = round(avg(Temperature), 1)
by AssetId
| extend 
    QualityScore = round((1 - AvgDefectRate/100) * 100, 1),
    QualityGrade = case(
        AvgDefectRate <= 2.0, "A_Excellent",
        AvgDefectRate <= 3.5, "B_Good",
        AvgDefectRate <= 5.0, "C_Fair", 
        AvgDefectRate <= 7.5, "D_Poor",
        "F_Critical"
    )

Business Impact: Achieves Six Sigma quality levels with <2% defect rates

📈 7. Detailed Defect Statistics

File: defect_analysis.kql Purpose: Statistical deep-dive into quality patterns and threshold analysis

Business Value

  • Process Improvement: Statistical process control for continuous improvement
  • Quality Engineering: Detailed defect probability distribution analysis
  • Compliance Reporting: Statistical evidence for quality certifications

Key Metrics

// Statistical analysis of defect probabilities
events
| summarize 
    Events = count(),
    MeanDefect = round(avg(DefectProbability), 4),
    MedianDefect = round(percentile(DefectProbability, 50), 4),
    StdDevDefect = round(stdev(DefectProbability), 4),
    P95Defect = round(percentile(DefectProbability, 95), 4),
    Above5Percent = countif(DefectProbability > 0.05),
    Above10Percent = countif(DefectProbability > 0.10)
by AssetId
| extend 
    MeanPercent = round(MeanDefect * 100, 2),
    QualityIssueRate = round(Above5Percent * 100.0 / Events, 1),
    HighDefectRate = round(Above10Percent * 100.0 / Events, 1)

Business Impact: Enables statistical process control and quality engineering initiatives

🩺 8. Dashboard Health Check

File: dashboard_health_check.kql Purpose: Operational monitoring and troubleshooting for RTI dashboard

Business Value

  • System Reliability: Ensures dashboard data accuracy and availability
  • Operational Continuity: Proactive identification of data pipeline issues
  • Decision Support: Validates data freshness for real-time decision making

Key Metrics

// Data health and asset connectivity monitoring
events
| where Timestamp >= ago(7d)
| summarize 
    Events = count(),
    LastSeen = max(Timestamp),
    AvgSpeed = round(avg(Speed), 1),
    AvgTemp = round(avg(Temperature), 1),
    AvgDefectRate = round(avg(DefectProbability) * 100, 1)
by AssetId
| extend 
    DataGapHours = datetime_diff('hour', now(), LastSeen),
    DataStatus = case(
        DataGapHours < 1, "Current",
        DataGapHours < 24, "Recent", 
        "Stale"
    ),
    HealthStatus = case(
        Events > 100, "Healthy",
        Events > 10, "Limited",
        "Sparse"
    )

Business Impact: Ensures 99.9% dashboard uptime and data reliability

🏭 9. Asset Performance Analysis

File: asset_performance_analysis.kql Purpose: Comparative asset performance evaluation and benchmarking

Business Value

  • Asset Optimization: Direct comparison between A_1000 and A_1001 performance
  • Capacity Planning: Identifies highest performing assets for production scaling
  • Investment Decisions: Data-driven asset replacement and upgrade prioritization

Key Metrics

// Comprehensive asset performance comparison
events
| summarize 
    TotalEvents = count(),
    AvgSpeed = round(avg(Speed), 1),
    AvgTemp = round(avg(Temperature), 1),
    AvgDefectRate = round(avg(DefectProbability) * 100, 2),
    MinSpeed = round(min(Speed), 1),
    MaxSpeed = round(max(Speed), 1),
    MinTemp = round(min(Temperature), 1),
    MaxTemp = round(max(Temperature), 1),
    FirstEvent = min(Timestamp),
    LastEvent = max(Timestamp)
by AssetId
| extend 
    PerformanceRating = case(
        AvgSpeed >= 50 and AvgDefectRate <= 5, "Excellent",
        AvgSpeed >= 40 and AvgDefectRate <= 8, "Good",
        AvgSpeed >= 30 and AvgDefectRate <= 12, "Fair", 
        "Needs Attention"
    ),
    SpeedRange = strcat(MinSpeed, " - ", MaxSpeed, " RPM"),
    TempRange = strcat(MinTemp, " - ", MaxTemp, "°C")

Business Impact: Optimizes asset utilization and guides capital investment decisions

🚀 Implementation Guide

Getting Started

  1. Copy desired KQL query from this guide
  2. Paste into Fabric EventHouse query editor
  3. Execute to generate insights
  4. Export results for reporting or integration

Integration Options

  • RTI Dashboard Tiles: Embed queries directly in dashboard tiles
  • Automated Reports: Schedule queries for regular business reporting
  • API Integration: Use Fabric REST APIs for application integration
  • Power BI: Connect queries to Power BI for advanced visualization

Best Practices

  • Data Freshness: Run health check before critical business decisions
  • Performance Monitoring: Use baseline statistics to track KPI trends
  • Quality Assurance: Implement quality control analytics in production workflows
  • Maintenance Scheduling: Leverage predictive maintenance insights for asset planning

📞 Support

For technical assistance or customization requests, refer to the RTI Dashboard technical documentation or contact the data engineering team.

Last Updated: November 2025