Predictive Maintenance Benchmarks in Manufacturing
Aggregate outcomes from 60 documented predictive maintenance deployments in manufacturing.
Computed on
- Maintained by
- Peter KorpakLead Editor
- Methodology
- How evidence is checked
60
Documented deployments
11
Industries
4
Vendors
31
Reporting outcomes
Reported outcomes
31 of 60 case records report a metric, but not enough of them share a comparable measure, unit, period and observed-result status to publish a median. No median is shown when the sample is not comparable.
Which vendors have the most predictive maintenance deployments?
Which industries deploy predictive maintenance?
- Energy & Utilities12 · 20%
- Metals & Mining11 · 18%
- Food & Beverage10 · 17%
- 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 directory (60 case records), recomputed as new case studies are published.
- Outcome medians: computed only within cohorts that share measure, unit, direction, period and observed-result status. Ranges, approximations, forecasts, simulations and mixed money bases are excluded rather than coerced. 0 of 60 case records currently count as a deployment in a published statistic. Each statistic links the contributing case records and cited sources so the median can be recalculated from that list. Each deployment counts once and repeated write-ups of the same deployment are merged, so sample size is distinct deployments, not cases or metric slots. A statistic needs at least 5 deployments from at least 3 distinct sources.
- Source figures are as reported by each record's linked cited source. They are not independently audited, and source type is not classified. Treat medians as the shape of reported outcomes, not a guarantee.