One of many downstream use cases. This example focuses on spare parts optimization and turnaround planning, reducing inventory costs while ensuring critical parts are available when needed.
Too much inventory ties up capital. Too little risks extended outages when critical equipment fails. Your maintenance and supply chain teams manage thousands of SKUs across multiple facilities, making stocking decisions based on historical usage and vendor lead times.
Turnaround planning compounds this: a 30-day shutdown requires thousands of parts, coordinated contractor schedules, and precise sequencing. The cost of getting it wrong is measured in days of lost production.
Traditional approaches were not built for the scale and complexity of modern operations.
Reorder points are set manually and rarely updated. They don't account for equipment condition, usage trends, or lead time variability.
Purchase orders, vendor performance, and price history live in separate systems. Spend analysis requires manual data pulls.
Turnaround scope, sequencing, and contractor selection depend on the planning team's experience from previous events.
Parts are ordered on schedule or after failure. Not triggered by equipment condition data that predicts the need.
Three layers of intelligence working together: reasoning agents, proactive detection, and multi-agent deliberation.
Agents analyze consumption patterns, lead time variability, equipment criticality, and vendor performance to optimize stocking levels. Every recommendation is backed by SQL against your CMMS and procurement data.
Every calculation is SQL you can verify. No black box.
Analyzing SKU P-4420 (Compressor Seal Kit): Usage: 8 units/yr (3-year average), increasing trend +12%/yr Lead time: 45 days (vendor avg), 90th percentile: 72 days Criticality: A (compressor downtime = $180K/day) Current stock: 2 | Recommended: 4 (safety stock = 2) → Reorder point: 3 units | Annual holding cost increase: $2,400 → Risk reduction: Eliminates 98% probability of stockout
ALERT: Vendor VX-200 lead times increased 40% over last 3 orders Affected SKUs: 47 parts across 3 facilities Critical exposure: 8 SKUs with A-criticality equipment → Recommend: Qualify backup vendor for critical SKUs. Increase safety stock for 8 critical items immediately.
The Radar monitors vendor performance trends, lead time shifts, price volatility, and consumption anomalies across all facilities.
The Radar surfaces issues the operator didn't know to look for. Before they become incidents.
Maintenance, supply chain, and finance agents reason together on turnaround scope, procurement timing, and budget allocation.
The output is grounded in facts (SQL results), not hallucination. Every recommendation carries a full audit trail.
Maintenance Agent: Turnaround scope includes 340 work orders. Critical path: heat exchanger bundle replacement (14 days) Supply Chain Agent: Bundle lead time is 16 weeks. Must order within 2 weeks to meet schedule Finance Agent: Budget variance is +8%. Defer 45 non-critical work orders to next outage → Consensus: Order bundle immediately. Defer non-critical scope. Net budget impact: -3% from original estimate.
Lumina addresses the four strategic problems that hold operators back.
Optimize stocking levels across thousands of SKUs and multiple facilities automatically.
Reduce inventory carrying costs while eliminating critical stockouts. Optimize turnaround budgets.
Vendor performance history, consumption patterns, and turnaround lessons learned are retained, not lost between events.
Every stocking recommendation, lead time analysis, and cost estimate is SQL-verifiable against your CMMS and procurement data.
Specialized AI agents that power this workflow.
Supply Chain Analyst
Specializes in inventory optimization, vendor performance analysis, lead time forecasting, and procurement pattern intelligence.
Maintenance Planner
Manages turnaround scope, work order prioritization, contractor scheduling, and equipment criticality assessment.
Data Scientist
Handles CMMS data integration, procurement analytics, and cross-facility correlation.
This is just one of many use cases
Supply chain & maintenance is one example of how Lumina reasons on operational data. Across Oil & Gas, every domain has use cases where AI agents can add value.
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