8M in annual cost per facility. Existing maintenance schedules were reactive and could not anticipate or prevent these costly failure events.
The fund wanted a repeatable way to solve this across multiple portfolio companies rather than a one-off fix.
Pluto AI built predictive-maintenance scheduling that surfaces predicted equipment-failure windows 72 hours ahead, trained on each facility's own historical failure data and slotted into its existing CMMS dispatch workflow. One reusable architecture was applied across all three of the fund's portfolio manufacturers, requiring no new platforms at any site.
Portfolio-wide reduction in unplanned-downtime cost across the three manufacturing facilities, at roughly $1.1M per facility.
Reusing one architecture cut rollout time from six weeks at the first facility to three weeks at the third.
Unplanned downtime per facility fell from 14% to 8.8% of scheduled production hours.
- How a Manufacturer Cut ERP Rollout From 12 Months to 3VestedInYou · Manufacturing & Industrial
- How an Automaker Saved $12M a Year Predicting BreakdownsBolt Group · Manufacturing & Industrial
- How a PE-Backed Manufacturer Cut Downtime 39% With AIPluto AI · Manufacturing & Industrial
- How a Glass Manufacturer Replaced Spreadsheets With AITensile · Manufacturing & Industrial