Case study · From the Pluris libraryReceipt no. 0177
What sucked
A global automaker was losing $50M a year to unplanned equipment failures.
Scheduled maintenance was either too frequent, wasting 30% of the budget on healthy machines, or too late, causing cascading line shutdowns. 10,000+ sensors were generating data nobody was acting on.
What they built
An edge-AI predictive maintenance platform that reads 10,000+ sensors across 12 plants to predict equipment failure 48–72 hours out, with prioritized alerts and digital twins.
What changed
$12M Annual Savings
Avoided emergency repairs and downtime.
15% Less Downtime
Reduction in unplanned production loss.
94% Prediction Accuracy
On critical equipment, 48–72h before failure.
More fixes like this
- How a Manufacturer Cut ERP Rollout From 12 Months to 3VestedInYou · Manufacturing & Industrial
- How a PE-Backed Manufacturer Cut Downtime 39% With AIPluto AI · Manufacturing & Industrial
- How a PE Fund Saved $2.7M Across Three Factories With AIPluto AI · Manufacturing & Industrial
- How a Glass Manufacturer Replaced Spreadsheets With AITensile · Manufacturing & Industrial