Predictive Maintenance Benchmarks in Manufacturing
Aggregate outcomes from 61 documented predictive maintenance deployments in manufacturing — median reported annual savings $200K across 8 reporting deployments.
Maintained by Peter Korpak, Founder & Chief AnalystHow evidence is checked
Based on 61 documented implementationsCorpus published through
Maintained by Peter Korpak, Founder & Chief AnalystHow evidence is checked
61
Documented deployments
11
Industries
4
Vendors
31
Reporting outcomes
What outcomes do predictive maintenance deployments report?
Medians computed from 18 of 61 deployments whose reported figures could be parsed into comparable units. Values are as reported by the original source, not independently audited.
| Outcome | Median | Range | Deployments |
|---|---|---|---|
| Reported annual savings | $200K | $20K – $45M | 8 |
| Productivity & OEE gain | 9% | 0.1% – 40% | 5 |
Which vendors have the most predictive maintenance deployments?
Which industries deploy predictive maintenance?
- Energy & Utilities12 · 20%
- Metals & Mining12 · 20%
- Food & Beverage10 · 16%
- Industrial Machinery8 · 13%
- Automotive6 · 10%
- Chemicals4 · 7%
- Consumer Goods3 · 5%
- Aerospace2 · 3%
- Electronics2 · 3%
- Packaging1 · 2%
- Pharmaceuticals1 · 2%
How these numbers are computed
- Population: every published predictive maintenance case study in our manufacturing corpus (61 deployments), recomputed from the database — no figure on this page is hardcoded.
- Outcome medians: computed only within like-for-like buckets, and only where a reported value parses into a comparable unit. 18 of 61 deployments contributed; the rest report figures in units we cannot compare, and are excluded rather than coerced.
- Source figures are as reported by each record's linked cited source. They are not independently audited, and the current corpus does not classify source type. Treat medians as the shape of reported outcomes, not a guarantee.