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Topicspredictive-maintenance

Predictive Maintenance: What the Surveys Actually Say

A sourced reference collection on predictive maintenance in industry — the measured adoption figures, why two credible surveys disagree about whether adoption is rising or falling, and what is actually stopping deployment.

Assertions
5
Sources consulted
3
Read in full
3/3
Cited as evidence
3
Disputed
1

3 sources sit behind this page — including any that arrive with a concept this page shares with another collection. 3 were retrieved and read in full, and only those can back an assertion. Every one of them is named in the register below, with the reason in view. How we source this.

Background

Our own synthesis, written to orient you — not evidence. Every factual statement here is asserted and sourced further down this page.

Predictive maintenance means servicing a machine shortly before it fails, using condition data from the equipment itself — as opposed to preventive maintenance, which services on a schedule whether or not anything is wrong, and reactive maintenance, which waits for the breakdown. The idea is old. What is new is that the sensing and the models are cheap enough to attempt at scale, and the results are being surveyed.

The surveys disagree, and this collection publishes the disagreement rather than averaging it. Fluke’s 2026 Censuswide survey of more than 600 decision-makers across the United States, United Kingdom and Germany reports predictive maintenance rising from 9% to 18%. The 2025 State of Industrial Maintenance Report reports it falling from 30% to 27%. Those cannot both describe the same thing: a baseline of 9% and a baseline of 30% are measuring different thresholds, or different populations, or both. Anyone quoting a single adoption number for this technology is choosing one survey and not telling you.

Where the Fluke figures are internally consistent, they say something more interesting than the headline. Reactive maintenance held flat at 36% while scheduled proactive work fell from 55% to 45% and predictive reached 18%. On those numbers predictive maintenance is displacing planned servicing, not the emergency repairs it is usually sold as preventing — the firefighting is unchanged.

The barrier is people, not technology. Skills-related obstacles made up roughly 78% of all reported barriers to digital maintenance progress: expertise 23%, skilled labour 19%, knowledge 18%, workforce 17%. Over the same period the share of respondents expecting to complete their Industry 5.0 transition within six months fell from 33% to 22% — a schedule slipping, measured.

The reason anyone pays for this is downtime. Siemens’ True Cost of Downtime 2024 put unplanned downtime at roughly $2.8 billion a year across Fortune 500 companies, about 11% of revenue, and about $253 million a year for a large plant.

This collection is small and rests heavily on one survey publisher. That is a real limitation rather than a stylistic one: the contradiction described above is visible precisely because two surveys were found, and a third would probably produce a third answer.

Figures

Every number below is asserted and sourced elsewhere on this page.

Two surveys, opposite directions

Reported predictive-maintenance adoption, before and after, from two surveys published a year apart. They disagree on the level and on the sign of the change.

30%
9%

21 pts

Earlier reading

27%
18%

9 pts

Later reading

One survey has adoption falling; the other has it doubling.

Fluke’s 2026 Censuswide survey of 600+ decision-makers in the US, UK and Germany, and the 2025 State of Industrial Maintenance Report. Self-reported survey responses from different populations; the gap between the two series is the finding, not a measurement error to be split.

What predictive maintenance is actually replacing

Maintenance mix in Fluke’s 2026 survey. Reactive work is unchanged — the growth in predictive has come out of scheduled servicing, not out of firefighting.

Total % of maintenance activity

  • Proactive (scheduled)45%
  • Reactive36%
  • Predictive18%

Fluke 2026 Censuswide survey. Self-reported shares; reactive is stated as flat at 36% and proactive as having fallen from 55% to 45% over the same period.

The barrier is people

Reported obstacles to digital maintenance progress. Skills-related answers account for about 78% of all barriers named.

Total ~78% of barriers named

  • Expertise23%
  • Skilled labour19%
  • Knowledge18%
  • Workforce17%

Fluke 2026 Censuswide survey. Self-reported obstacle categories; the four shown are the skills-related ones and do not sum to all barriers reported.

Concepts

The vocabulary this subject is built from, and what we can show about each.

Maintenance Skills Gap

other

Skills-related barriers made up roughly 78% of all reported obstacles to digital maintenance progress in Fluke's 2026 survey — expertise 23%, skilled labour 19%, knowledge 18%, workforce 17% — while respondents expecting Industry 5.0 completion within six months fell from 33% to 22%.

ReportedSupported by the sources below, not yet editor-reviewed.
2 sources2 retrieved & read

Maintenance Strategy Mix

process

Reactive maintenance held flat at 36% in Fluke's 2026 survey while proactive fell from 55% to 45% and predictive reached 18% — predictive maintenance is so far displacing scheduled servicing rather than reactive firefighting, and 58% of facilities spend less than half their time on scheduled maintenance.

ReportedSupported by the sources below, not yet editor-reviewed.
2 sources2 retrieved & read

Predictive Maintenance

process

Predictive maintenance uses equipment condition data to service a machine shortly before it fails, as distinct from schedule-based preventive and after-failure reactive maintenance; Deloitte estimates it can cut maintenance costs by up to 25% and raise uptime by 10-20%.

ReportedSupported by the sources below, not yet editor-reviewed.
1 source1 retrieved & read

Surveys disagree on the direction of predictive maintenance adoption: Fluke's 2026 Censuswide survey of 600+ decision-makers in the US, UK and Germany reports a rise from 9% to 18%, while the 2025 State of Industrial Maintenance Report reports a fall from 30% to 27% — bases of 9% and 30% cannot describe the same threshold.

DisputedSources disagree. Both accounts are shown below.
3 sources3 retrieved & read

Unplanned Downtime

other

Siemens' True Cost of Downtime 2024 put unplanned downtime at about $2.8 billion a year for Fortune 500 companies (roughly 11% of revenue) and $253 million for large plants, with hourly cost roughly doubling between 2019 and 2024; 31% of maintenance managers reported rising downtime costs in 2025, 55% of them attributing it to parts prices.

ReportedSupported by the sources below, not yet editor-reviewed.
1 source1 retrieved & read

Source register

All 3 sources behind this page — what we read, what we tried to read and could not, and what we looked at and set aside, with the reason in view for each. A concept shared with another collection brings its own references with it, so some entries here were surfaced for a neighbouring topic rather than this one.

Cited as evidence
3
Tried, could not read
0
Surfaced, set aside
0
Cited sources 3 distinct links

Original publisher links. Files open on the publisher’s site; we do not host copies. A linked document is not an additional source or an independent verification.

Coverage & limits

What this page does and does not claim.

Eighth packet through the generic ingestion pipeline (August 2026). All three sources were directly retrieved; none is a neutral party. One is a survey commissioned by a company selling condition-monitoring tools, one is trade coverage of that same survey which adds no independent caveat, and one is a maintenance-software vendor's statistics roundup — valuable chiefly because it attributes each figure to its originating study rather than absorbing them. That is the honest state of the evidence base in this category: the research is almost entirely produced or commissioned by suppliers. The collection's central assertion is a preserved contradiction — Fluke reports predictive maintenance adoption doubling from 9% to 18%, another survey reports it falling from 30% to 27% — recorded as a disagreement with both attributions and the methodological differences stated, not resolved. Underlying studies from Deloitte, Siemens and the State of Industrial Maintenance Report were not retrieved at source and are cited at one remove, marked as such. This collection has no timeline: survey publications are not events, and no dated deployment milestone was retrievable, so the timeline is empty rather than padded with datelines. Not yet editor-reviewed; every assertion reads as reported.

Source check, 2026-09-17. Numeric-presence checks passed for 4 of 5 assertions using available source text, which may be cached. 1 remained unchecked because source text was unavailable. This is not human review. What this check does and does not prove →

  • Not editor-reviewed unless labelled. Assertions marked Reported are assembled from the sources shown and have not yet been checked by an editor. Only Primary source and Corroborated mean a human verified them.
  • Disagreements are preserved, not resolved. Where sources conflict, both accounts appear and the assertion is marked Disputed.
  • Retrieval status is disclosed per source. A source we could not open is never counted as evidence for an assertion.

This page is also available as structured data: /api/v1/topics/predictive-maintenance