How a PE Fund Saved $2.7M Across Three Factories With AI
$2.7M
2–4 months
Not disclosed
Three manufacturing facilities in the fund's portfolio each ran high levels of unplanned downtime — roughly 14% of scheduled production hours, calculated at about $1.8M 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 deployed predictive maintenance scheduling that surfaces predicted failure windows 72 hours in advance, integrated directly into each site's existing CMMS dispatch workflow. Models were trained on each facility's historical failure data, requiring no new platforms. A single reusable architecture was applied to each successive company.
Each engagement opened with a two-week diagnostic — interviews with maintenance leadership, analysis of two years of work-order data, and downtime-cost quantification. The first facility took six weeks to design and build; because the architecture was reused, the second took four weeks and the third just three. Predictions were inserted into the existing dispatch workflow with no change to teams' daily routines.
PE funds looking to standardize and rapidly replicate a proven AI workflow across multiple capital-intensive portfolio companies.
$2.7M annual savings
60% faster deployment
37% less downtime
Yayati Tanwar
By reusing a single predictive-maintenance architecture rather than rebuilding at each site. The experts trained a failure-prediction model on each facility's own historical work-order data and slotted it into the existing CMMS dispatch workflow, cutting unplanned downtime 37% and saving $2.7M a year across three PE-backed manufacturers.
The approach combines predictive analytics and decision support: a predictive-maintenance model trained on two years of each facility's historical work-order and failure data. It runs on the existing CMMS with no new platforms, surfacing predicted failure windows 72 hours in advance.
Three measurable outcomes across the portfolio: $2.7M in annual savings from reduced downtime, unplanned downtime cut from 14% to 8.8% of production hours (a 37% reduction), and rollout time cut 60% — from six weeks at the first facility to three at the third.
Each site went live within roughly two to four months. Because the architecture was reused, build time fell from six weeks at the first facility to four at the second and three at the third, after a two-week diagnostic at each.
Private equity funds that want to standardize and rapidly replicate a proven AI workflow across multiple capital-intensive portfolio companies — particularly manufacturers with existing CMMS systems and historical failure data.
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