Even the best maintenance plan can collapse if the right part isn’t on hand. Stockouts force teams into costly plan B scenarios, which can include:
- Emergency orders with rush shipping
- Improvised fixes with non-standard parts
- Extended downtime while waiting on suppliers
On the other hand, overstocking wastes capital, clogs storerooms, and increases carrying costs.
Traditional forecasting relies on averages and best guesses. But in today’s volatile supply chain and high-demand environment, guesswork isn’t good enough.
AI can mitigate these risks by analyzing historical usage, upcoming schedules, and lead times to forecast exactly which parts you’ll need and when. The result: fewer stockouts, lower costs, and higher PM compliance.
How to use AI to forecast parts usage in maintenance
AI forecasting blends historical data with future demand signals to build precise part predictions. Here is a template for how to use AI to forecast the parts you’ll need for upcoming maintenance, including what data to use, how to organize that data, examples of prompts, and what to do with the information AI gives you.
Step 1: Collect the right data
Getting the most accurate parts forecasting starts with accurate data. Here is what you should gather to help AI make the right recommendations:
- Past PM work orders: Asset, date, procedure, parts used, and notes
- Corrective work orders: Asset, date, failure code, and parts replaced
- Upcoming PM schedule: Asset, date, procedure, and BOM
- Parts inventory data: Part number, minimum/maximum count, current stock, and lead time
Step 2: Clean and structure
Simply having the data isn’t enough when working with AI—you also need to make sure that’s structured in a way that AI can use it. Here are a few tips for doing that:
- Standardize naming conventions (e.g., “Compressor-01,” not “AC1”)
- Split multiple data sections into separate fields
- Fill missing required fields (lead time, min/max levels)
- Tag assets by type, criticality, and maintenance schedule
Step 3: Use prompts to forecast demand
The right prompt can help you arrive at the right answer from AI quicker, and with more confidence. Here is a formula for constructing the perfect prompt for parts forecasting:
- Be specific with assets, parts, or work orders.
- Example: Review upcoming planned work orders for conveyors in the next four weeks.
- State the output format.
- Example: Provide a numbered list of at-risk parts with forecasted usage and reorder recommendation.
- Add operational context, like changes in production or seasonal demand.
- Example: Account for the 20% increase in runtime due to the new production schedule.
- Define stock thresholds or constraints.
- Example: Highlight parts projected to fall below the minimum count of 10 units.
Here are a few sample prompts that use this formula:
- “Based on the past three months of preventive work orders, forecast which parts are needed for the next four weeks of scheduled work. Compare to current inventory levels and lead times. Highlight parts that will fall below minimum stock levels, parts at risk of stockout, and suggested reorder quantities and times. List in order of urgency.
- “Analyze the past six months of parts usage from both PMs and corrective work orders. Identify the 10 most frequently used parts and parts with the highest variance in usage. Show average weekly usage, standard deviation, and whether current inventory and lead times are adequate to meet demand trends. Use a table to highlight high-risk items.”
- “Identify parts used in the last three months of corrective maintenance that have average lead times longer than seven days. Check if those parts are required in upcoming maintenance. Flag at-risk parts and recommend minimum stock adjustment. Create a summary list with risk levels.”
- “Given a 20% increase in production for [ASSET GROUP], forecast spare part demand for [ASSET GROUP] and flag any critical gaps in inventory.”
- “Review the past six months of work orders, scheduled maintenance for the next six weeks and inventory stock for all sites. Identify if we can source parts for upcoming maintenance internally between sites, assuming a one-week lead time for parts. Create a table with the sites, maintenance task, part, and shipment date.”
Step 4: Use the forecasts to improve maintenance efficiency and planning
Forecasts are only valuable if acted upon. Leaders should:
- Submit POs for at-risk parts. Build purchase orders based on AI recommendations.
- Adjust minimums. Update stock levels based on historical variance and lead times.
- Optimize purchasing. Identify frequently used parts for bulk buys.
- Cross-share inventory. Shift stock between sites instead of over-ordering.
- Reschedule PMs. Align work orders with part availability when feasible.
What AI parts forecasting looks like in action
AI analyzed six months of PM and corrective work data, then produced a forecast for the next four weeks:
- Urgent Reorders:
- Belt 4004 — Stockout → Reorder 6 units by Aug 10
- Bearing 5005 — Stockout → Reorder 3 units by Aug 4
- Filter 2002 — Low stock → Reorder 2 units by Aug 9
- Adequately Stocked:
- Seal Kit 1001 and Sensor 3003 remain above minimum thresholds.
Instead of reactive scrambling, the team had a clear, proactive reorder plan.Measuring the Impact
Evaluate AI forecasting by tracking:
- Stockouts avoided. Reduction in emergency orders.
- Downtime. Hours lost to missing parts before vs. after.
- Inventory costs. Reduction in carrying costs.
- PM compliance. Increase in planned work completed on schedule.
- Forecast accuracy. % of AI predictions matched by real demand.
Six more ways to use AI in maintenance
There are hundreds of maintenance teams using AI in their day-to-day tasks to help them work faster, reduce downtime, and eliminate obstacles. This includes:
- Building AI assistants for real-time repair support
- Analyzing maintenance data and KPIs
- Generating maintenance procedures
- Doing root cause analysis
- Detecting anomalies and predicting asset failure
- Collecting and actioning team knowledge
For a complete playbook on how to adopt these AI use cases, download the Maintenance Manager’s Quick Start Guide to AI, which includes templates and step-by-step instructions for building your own AI program.
Where to go from here
Parts forecasting turns inventory management from a guessing game into a predictive science. Instead of rushing orders or wasting money on excess stock, your team can plan with confidence.
MaintainX CoPilot makes this possible by:
- Analyzing historical usage and future PMs.
- Highlighting at-risk parts.
- Recommending reorder dates and quantities.
- Surfacing cross-site sharing opportunities
With AI forecasting, maintenance leaders no longer react to shortages—they prevent them, ensuring reliability and cost efficiency at scale.
The MaintainX team is made up of maintenance and manufacturing experts. They’re here to share industry knowledge, explain product features, and help workers get more done with MaintainX!
