AI-Powered Spare Parts Optimization
The Problem:
$1.8M Trapped in Dead Stock
The Solution:
Predictive Inventory Intelligence
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The Problem: $1.8M Trapped in Dead Stock
At Coastal Energy Refinery, critical equipment failures were frequently delayed by missing spare parts, while warehouses overflowed with $2.3M in obsolete inventory. Maintenance teams wasted 15+ hours weekly manually cross-referencing Excel sheets, SAP stock levels, and supplier lead times – often ordering wrong parts during breakdowns. With 8,000+ SKUs spread across 3 remote sites, engineers couldn’t predict demand surges. This led to:
- 37 emergency air shipments monthly ($12k/avg cost)
- 68% overstock of low-usage items
- 5-hour production delays waiting for parts
- $560k/year in carrying costs for dead inventory
Our Solution: Predictive Inventory Intelligence
We deployed our AI platform to transform spare parts management into a proactive, automated system. Using Agentic AI, the solution continuously analyzes equipment sensor data, failure histories, and consumption patterns to predict part requirements. Inventory staff now use conversational prompts like “Flag bearings at risk of stockout in Q3 and compare to supplier lead times” to generate reorder plans. Crucially, the Actions Module enables commands such as *“Auto-create SAP PO when Pump P-17 seal stock drops below 15 units”* – eliminating manual interventions while integrating with procurement workflows.
Implementation: Unifying Silos in 14 Days
Our team connected Coastal’s fragmented ecosystem through:
- ERP Integration (SAP S/4HANA): Real-time inventory levels, POs, supplier data
- CMMS (IBM Maximo): Equipment hierarchies, failure histories, BOMs
- Sensor Networks (OSIsoft PI): Vibration/temperature trends predicting part fatigue
- Supplier Portals (Ariba): Dynamic lead times and pricing
- Maintenance Logs (SharePoint): Technician notes on part compatibility
The AI mapped relationships between 12,000+ data points using natural language configuration (e.g., *“Link compressor serial numbers to bearing SKU 7382-X”*). Field teams adopted the system after a single 3-hour training on prompt engineering like “Generate obsolescence report for parts unused >18 months.”
Results: From Cost Center to Profit Lever
Within 90 days:
- Stockouts reduced by 92% through predictive alerts before critical failures
- Obsolete inventory cut by $1.6M via AI-identified dead stock liquidation
- Emergency shipments fell from 37 to 3 monthly saving $408k/year
- Carrying costs slashed by 41% ($230k annual savings)
- Procurement workload decreased 70% after automating 85% of POs
