A $28 bearing sitting out of stock for eleven days once cost a mid-sized plant $94,000 in lost production — not because the part was rare, but because nobody asked the right question of the data already sitting in the CMMS. That bearing failed on a predictable schedule, and the usage pattern was there the entire time. Across power generation maintenance teams, 30 to 50% of MRO stock sits untouched for 24 months or longer while critical components simultaneously run out at the worst possible moment. OxMaint's AI inventory engine reads your existing work order and asset health data to forecast exactly which parts you need and when. Book a demo to see predictive replenishment running against your own parts history.
$125K/hr
cost of lost production during an emergency stockout-driven shutdown
4–5x
price premium paid for emergency procurement versus standard ordering
30–50%
of MRO stock sitting unused for 24+ months while other parts run out
35%
average inventory cost reduction once AI forecasting replaces fixed reorder points
Stock Policy by Asset Criticality
Not every part deserves the same forecasting attention — AI applies the right level of rigor to each tier
Tier A · Critical
A stockout here means a shutdown. Local safety stock is mandatory, and AI forecasting delivers its highest-impact accuracy on this tier.
Tier B · High Value
Critical equipment with reliable supply chains. AI-triggered reorder points keep stock lean without risking availability — second-highest forecasting ROI.
Tier C · Low Priority
Low criticality, easily sourced parts. Vendor-managed inventory and minimal internal stock — not a priority for AI forecasting investment.
Oxmaint builds its initial demand forecast directly from your existing work order history — no new sensors or IoT feeds required to start.
The Six Data Streams AI Combines
1Work order consumption history per asset
2Equipment age and condition score trends
3Failure rate by part and asset class
4Seasonal production cycle patterns
5Supplier lead time performance
6Preventive maintenance schedule density
Spreadsheet Reorder Points vs. AI Forecasting
| Factor | Fixed Reorder Point | AI Demand Forecasting |
|---|---|---|
| Adjusts as equipment ages | No — set once, rarely revisited | Automatic, continuous recalibration |
| Accounts for supplier lead time changes | Manual update required | Tracked and applied automatically |
| Handles intermittent failure-driven demand | Poorly — averages mask spikes | Models probabilistic demand directly |
| Typical inventory cost outcome | Excess stock + recurring stockouts | 15–35% lower cost, fewer stockouts |
Expert Review
Carlos Esteban — MRO and Materials Planning Consultant, 14 years across power generation and heavy industry
The clearest sign a plant's spare parts strategy is broken isn't the stockout itself — it's the ratio of emergency to planned procurement creeping upward month after month. Nobody notices because each individual emergency order feels like a one-off. AI forecasting works because it treats the work order history as the asset it already is, instead of asking a planner to remember that the bearing supplier runs nine days slow or that the conveyor motor failure rate climbed after the last overhaul. The data was always there; it just needed to be asked the right question.
Frequently Asked Questions
Do we need IoT sensors before AI forecasting can work?
No — the highest-value signal already lives inside your CMMS as work order history, failure records, and PM schedules, and that alone builds a meaningful initial forecast. Start a free trial to build your first forecast directly from existing maintenance data.
How quickly will we see improvement after switching to AI forecasting?
Most plants see meaningful forecasting improvement within 60 days of connecting their existing maintenance records, with accuracy continuing to improve as more work orders close. Book a demo to see a sample 60-day forecasting timeline for your asset fleet.
Will AI forecasting also reduce excess inventory, or just prevent stockouts?
Both — AI forecasting addresses overstock and stockouts simultaneously, since the same model that flags an under-stocked critical part also identifies slow-moving parts tying up working capital unnecessarily. Sign in to OxMaint to view your current excess and at-risk stock side by side.
How does criticality tiering change how parts are forecasted?
Tier A critical parts receive the most forecasting attention and mandatory local safety stock since a stockout there means a shutdown, while low-criticality Tier C parts shift to vendor-managed inventory with minimal internal stock. Book a demo to see how your parts catalog maps across the three tiers.
OxMaint · Inventory Management · Spare Parts Planning
Stop finding out about a stockout at 2 AM when the shelf is already empty. Let AI tell you three weeks earlier.
