AI project reference, manufacturing: an illustrative example.
This manufacturing AI project reference is an illustrative example, not an approved customer reference. It shows, through our own model calculation, how a project to forecast spare-parts demand with Orpheon typically unfolds at a manufacturing operation - from starting position, through project phases, to result. Real, named customer quotes from actual projects are still to come.
Head: industry, size, timeframe
| Attribute | Detail |
|---|---|
| Industry | Manufacturing, series production |
| Company size | Mid-sized manufacturing operation, one plant |
| Project timeframe | 8 weeks from pilot to productive rollout |
| Result figure (our own model calculation) | About 90 hours of calculated time saved per month in spare-parts demand planning |
The result figure stated here is our own model calculation with disclosed assumptions, and an illustrative example - not a confirmed customer number. A real, approved reference with the same metrics will be published once alignment with the relevant customer is complete.
Starting position
At the example operation, spare-parts demand was until now estimated manually, based on experience and simple spreadsheets. With around 1,400 active spare-parts items, that manual estimation cost, by our own model calculation, around 280 hours per month, spread across several staff in purchasing and maintenance. Shortfalls in urgently needed parts repeatedly led to unplanned downtime.
Items with an irregular but fundamentally predictable consumption pattern were especially affected - wear parts, for example, whose demand tracks the utilization of specific machines. Those relationships were not previously analyzed systematically; instead, they relied on the experience of individual maintenance staff, knowledge that was partly lost with staff turnover and difficult to transfer to other plants.
Approach, in phases
| Phase | Duration | What happened |
|---|---|---|
| Internal pilot | 72 hours | First data sources connected, first test forecasts run on real plant data |
| Model refinement | Weeks 2–5 | Further data sources brought in, coordination with purchasing and maintenance |
| Productive rollout | Weeks 6–8 | Handover into day-to-day operations, system connection to ERP and the maintenance system |
During model refinement, further data sources were connected step by step, including sensor data from individual critical machines, to further improve the forecast for especially important spare-parts items. In this phase, purchasing and maintenance worked with NexPatch to check whether the forecast results held up against their own experience, before the platform moved into productive rollout.
Result: before and after
| Metric | Before | After (our own model calculation) |
|---|---|---|
| Time spent on demand estimation per month | about 280 hours | about 190 hours |
| Calculated time saved per month | not measured | about 90 hours |
| Forecast updates | Manual, irregular | Continuously automated by Orpheon |
The biggest effect in this illustrative example lies less in the raw time saved than in the continuous updating: instead of a periodic, manually built estimate, a forecast is available that adjusts automatically with every new booking and every new maintenance entry. Purchasing and maintenance end up working from the same, always-current numbers, instead of each maintaining their own, slightly different estimate in parallel.
A voice from the project
Putting this example in context
This page exists to make the future structure of real references visible before the first one is finally approved. Company size, starting position, and result figure are set to realistic orders of magnitude drawn from projects of this kind, but are explicitly labelled as our own model calculation and are not tied to any specific, named customer. Once a real manufacturing reference is available, it will replace this example page or supplement it as its own entry in the references overview, with the same sections but with figures actually confirmed by the customer and a real quote.
Further examples and the current status of real customer references are on the references overview; background on the technology used is on the Orpheon overview page; details on spare-parts demand forecasting in manufacturing are on the Orpheon Forecasting page.