MRO360 · Spare Parts Intelligence
Forecast spare parts and consumables demand at plant and network level. MRO360 is an AI-native intelligence layer that works above your existing ERP, CMMS, or EAM. No replacement required.
Demand planning is the process of forecasting how much of an item an operation will consume over a future period, so the right quantity is available at the right place and time without tying up excess capital.
In finished-goods supply chains, demand planning predicts what customers will buy. In maintenance operations, the item being forecast is not a product for sale. It is the spare part or consumable a plant needs to keep equipment running.
That difference matters. Spare parts demand is intermittent, driven by failures and maintenance schedules rather than steady sales. A single part can be critical at one plant and routine at another. Standard sales forecasting models miss this, which is why generic tools tend to over-stock some items and leave stockout exposure on others.
Good spare parts demand planning connects consumption history, the maintenance calendar, asset criticality, and supplier lead times into one forecast. It then feeds directly into stocking decisions like inventory levels and reorder points.
No single model fits every part. Strong spare parts planning layers several models and lets context decide which signal leads.
Projects future consumption from historical usage using moving averages, exponential smoothing, and trend or seasonal decomposition. Best for predictable, fast-moving parts with steady demand history.
Ties forecast to actual material movement and production volumes, closer to how MRP and consumption-based planning work in ERP. Best for parts whose usage scales with production ramp-up.
Segments parts by value (ABC) and demand variability (XYZ), so stocking policy matches each segment instead of one blanket rule. Best for right-sizing safety stock across a large catalog.
A statistical engine sets the structural baseline. An AI engine adds context and pattern recognition: turnaround spikes, correlated failures, and maintenance-calendar signals. Best for intermittent, event-driven spare parts demand.
Forecast planned, corrective, and emergency demand independently. Each behaves differently and blending them hides risk.
Upcoming turnarounds and PM schedules shape demand more than a rolling historical average ever will.
Hold buffer aggressively for critical, high-variability parts and conservatively for stable, low-criticality items.
When a planner adjusts a forecast, the correction is logged and fed back so the model learns your operating context.
MRO360 pairs a statistical baseline with an AI-native engine, then acts on the result across your network. It reads directly from your ERP, CMMS, or EAM, so there is nothing to rip out.
Two engines, one forecast. Statistics set the structural baseline; AI adds turnaround spikes, correlated failures, and seasonal signals.
Planned, corrective, and emergency demand are forecast separately, so critical exposure is never averaged away.
Each forecast feeds a dynamic reorder point that recalculates as criticality, lead time, and cross-site availability change.
Every planner override is captured and the model learns, so accuracy compounds against your criticality framework.
Figures reflect anticipated MRO360 outcome ranges across asset-intensive deployments. Actual results vary by data quality, catalog size, and operating context.
Bring one plant's material master and consumption history. We will show you the forecast, the reorder points, and the working capital sitting idle in your storerooms.
The AI-native intelligence layer for maintenance and reliability operations.
InventorySet stock levels and reorder points that reflect real consumption and criticality.
CriticalityScore parts at the SKU level so demand planning protects what actually matters.
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