Executive Leadership Journey: Manufacturing Intelligence for the COO

  Karen's Data Catalog View
  ═════════════════════════

  apex_manufacturing/
  ├── oee/                    (Real-time OEE across all plants)
  │   ├── oee_hourly          Updated: every hour
  │   ├── oee_shift_daily     Updated: end of each shift
  │   └── oee_plant_daily     Updated: midnight daily
  ├── maintenance/            (Equipment health and maintenance)
  │   ├── machine_health_scores   Updated: continuous (Ray Serve)
  │   ├── maintenance_logs        Updated: every 15 min (CDC)
  │   └── maintenance_costs       Updated: daily
  ├── quality/                (Quality inspection data)
  │   ├── inspection_results      Updated: every 15 min (CDC)
  │   ├── defect_annotations      Updated: every 15 min
  │   └── scrap_cost_daily        Updated: daily
  ├── production/             (Production orders and output)
  │   ├── production_orders       Updated: every 15 min (CDC)
  │   └── production_plan         Updated: weekly
  ├── supply_chain/           (Supplier performance)
  │   ├── supplier_deliveries     Updated: weekly
  │   └── inventory_levels        Updated: daily
  └── energy/                 (Energy consumption)
      ├── energy_consumption      Updated: every 1 min (streaming)
      └── energy_cost_daily       Updated: daily

-- Generated by AI Agent (text-to-SQL)
-- Query: Cross-plant OEE comparison by product line, last 6 months
SELECT
    e.plant_id,
    ps.product_line,
    DATE_TRUNC('month', o.hour) AS month,
    COUNT(DISTINCT o.machine_id) AS machines_reporting,
    ROUND(AVG(o.availability_rate), 1) AS avg_availability,
    ROUND(AVG(o.performance_rate), 1) AS avg_performance,
    ROUND(AVG(o.quality_rate), 1) AS avg_quality,
    ROUND(AVG(o.oee_percentage), 1) AS avg_oee,
    ROUND(AVG(o.oee_percentage) - LAG(AVG(o.oee_percentage))
        OVER (PARTITION BY e.plant_id, ps.product_line
              ORDER BY DATE_TRUNC('month', o.hour)), 1)
        AS oee_mom_change
FROM oee.oee_hourly o
JOIN equipment_registry e ON o.machine_id = e.machine_id
JOIN production_orders po ON o.machine_id = po.machine_id
    AND o.hour BETWEEN po.start_time AND po.end_time
JOIN product_specs ps ON po.product_id = ps.product_id
WHERE o.hour >= CURRENT_DATE - INTERVAL '6 months'
GROUP BY e.plant_id, ps.product_line, DATE_TRUNC('month', o.hour)
ORDER BY e.plant_id, ps.product_line, month

  ┌─────────────────────────────────────────────────────────────┐
  │  DAILY OPERATIONS BRIEFING - February 28, 2026             │
  │  Apex Manufacturing - All Plants                           │
  ├─────────────────────────────────────────────────────────────┤
  │                                                             │
  │  YESTERDAY'S OEE:  73.8%  (target: 85%)  ▲ +0.4% vs prev │
  │                                                             │
  │  Plant 1: 76.1% ▲   Plant 2: 72.8% ▬                      │
  │  Plant 3: 70.1% ▲   Plant 4: 74.5% ▲                      │
  │                                                             │
  │  KEY EVENTS:                                                │
  │  - M-0033 (Plant 1): Extended downtime, bearing replaced    │
  │    (3.2 hours, $4,200 maintenance cost)                     │
  │  - Plant 3 Line C: Quality rate dropped to 89% for 2 hours │
  │    Root cause: tool wear on 3 machines, corrected by 14:00  │
  │  - Supplier alert: Precision Alloys delivery 4 days late    │
  │    Impact: Plant 2 has 6 days of titanium stock remaining   │
  │                                                             │
  │  PREDICTIVE MAINTENANCE ALERTS:                             │
  │  - M-0073 (Plant 2): Health score 0.28, bearing wear        │
  │    predicted within 48 hours. Work order generated.          │
  │  - M-0156 (Plant 4): Health score 0.35, spindle alignment   │
  │    trending. Schedule inspection within 1 week.              │
  │                                                             │
  │  WEEK-TO-DATE PRODUCTION: 12,450 / 15,000 planned (83%)    │
  └─────────────────────────────────────────────────────────────┘

-- Generated by AI Agent: Total Cost of Ownership analysis for capital planning
WITH machine_costs AS (
    SELECT
        e.machine_id,
        e.machine_type,
        e.plant_id,
        e.install_date,
        EXTRACT(YEAR FROM AGE(CURRENT_DATE, e.install_date)) AS age_years,
        -- Maintenance costs (last 12 months)
        SUM(ml.parts_cost + ml.labor_cost) AS annual_maintenance_cost,
        COUNT(CASE WHEN ml.maintenance_type = 'CORRECTIVE' THEN 1 END) AS breakdown_count,
        -- Downtime cost (estimated at plant avg hourly rate)
        SUM(CASE WHEN ml.maintenance_type = 'CORRECTIVE'
            THEN ml.duration_hours * 25000 ELSE 0 END) AS annual_downtime_cost,
        -- Energy cost
        SUM(ec.kwh * 0.12) AS annual_energy_cost
    FROM equipment_registry e
    LEFT JOIN maintenance_logs ml ON e.machine_id = ml.machine_id
        AND ml.timestamp >= CURRENT_DATE - INTERVAL '12 months'
    LEFT JOIN energy_consumption_monthly ec ON e.machine_id = ec.machine_id
        AND ec.month >= CURRENT_DATE - INTERVAL '12 months'
    GROUP BY e.machine_id, e.machine_type, e.plant_id, e.install_date
),
oee_data AS (
    SELECT
        machine_id,
        AVG(oee_percentage) AS avg_oee_12m
    FROM oee.oee_shift_daily
    WHERE shift_date >= CURRENT_DATE - INTERVAL '12 months'
    GROUP BY machine_id
)
SELECT
    mc.machine_id,
    mc.machine_type,
    mc.plant_id,
    mc.age_years,
    mc.annual_maintenance_cost,
    mc.annual_downtime_cost,
    mc.annual_energy_cost,
    mc.annual_maintenance_cost + mc.annual_downtime_cost + mc.annual_energy_cost
        AS total_annual_cost,
    od.avg_oee_12m,
    mc.breakdown_count
FROM machine_costs mc
JOIN oee_data od ON mc.machine_id = od.machine_id
ORDER BY total_annual_cost DESC
LIMIT 15

  ┌─────────────────────────────────────────────────────────────────┐
  │  COO DASHBOARD - APEX MANUFACTURING                            │
  ├────────────────────────────┬────────────────────────────────────┤
  │ CROSS-PLANT OEE            │ FINANCIAL IMPACT                  │
  │                            │                                    │
  │ P1: ████████████████ 76.1% │ Revenue at risk: $340K             │
  │ P2: ███████████████  72.8% │ Maintenance spend MTD: $142K       │
  │ P3: ██████████████   70.1% │ Energy cost MTD: $89K              │
  │ P4: ███████████████  74.5% │ Scrap cost MTD: $67K               │
  │ Avg: 73.4%  Target: 85%   │ On-time delivery: 91.7%            │
  ├────────────────────────────┼────────────────────────────────────┤
  │ PREDICTIVE MAINT. ALERTS   │ SUPPLY CHAIN                      │
  │                            │                                    │
  │ Critical (< 48h): 2       │ Late deliveries: 3 suppliers       │
  │ Warning (< 1 wk): 5       │ At-risk materials: Titanium (6d)   │
  │ Monitored: 14             │ Supplier OTD: 87.3% (target: 95%)  │
  │                            │                                    │
  │ Next scheduled stops: 8    │ Open POs: 47                       │
  ├────────────────────────────┴────────────────────────────────────┤
  │ PRODUCTION STATUS (This Week)                                   │
  │                                                                 │
  │ Orders completed: 234 / 280 planned  ████████████████░░░  84%  │
  │ On-time: 91.7%  |  Quality: 94.3%  |  Backlog: 12 orders      │
  └─────────────────────────────────────────────────────────────────┘

{
  "report": "monthly_operations_summary",
  "schedule": "0 8 1 * *",
  "format": "pdf",
  "sections": [
    {
      "title": "Executive Summary",
      "type": "ai_generated",
      "prompt": "Summarize key operational metrics, trends, and notable events for the month"
    },
    {
      "title": "OEE Performance",
      "type": "chart_table",
      "metrics": ["oee_by_plant", "oee_trend_12m", "oee_by_product_line"]
    },
    {
      "title": "Financial Impact",
      "type": "chart_table",
      "metrics": ["maintenance_cost_trend", "scrap_cost_trend", "energy_cost_trend"]
    },
    {
      "title": "Predictive Maintenance ROI",
      "type": "chart_table",
      "metrics": ["downtime_prevented", "false_positive_rate", "model_accuracy"]
    },
    {
      "title": "Supply Chain Health",
      "type": "chart_table",
      "metrics": ["supplier_otd", "inventory_days_of_supply", "material_risk"]
    },
    {
      "title": "Capital Planning Update",
      "type": "ai_generated",
      "prompt": "Update on equipment replacement recommendations and ROI projections"
    }
  ],
  "recipients": ["karen.singh@apex.com", "board-distribution@apex.com"],
  "cc": ["cfo@apex.com"]
}

  Operational Efficiency Improvement Tracker
  ══════════════════════════════════════════

  Target: 15% improvement over 24 months (baseline: Jan 2025)
  Current: Month 14 of 24

  OEE Improvement          ████████████████████░░░░  6.2pp  (target: 12.6pp)
  Downtime Reduction        █████████████████████░░  34%    (target: 40%)
  Quality Improvement       ████████████████████░░░  2.9pp  (target: 4.9pp)
  Energy Efficiency         ████████████░░░░░░░░░░░  8%     (target: 20%)
  Maintenance Cost/Unit     █████████████████████░░  17%    (target: 30%)

  Overall Progress:         ████████████████████░░░  63%    (Month 14/24)
  On Track?                 Trending to hit ~78% of target by Month 24
                            Need acceleration in Energy + Maintenance Cost