{"schema_version":"2026-09-05.topic-graph-v1","canonical_url":"https://www.manufacturing.ai/topics/predictive-maintenance","topic":{"slug":"predictive-maintenance","name":"Predictive Maintenance: What the Surveys Actually Say","description":"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.","coverage_notes":"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.","primer":"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.\n\nThe 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.\n\nWhere 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.\n\nThe 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.\n\nThe 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.\n\nThis 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.","primer_note":"Manufacturing.ai's own editorial synthesis, not evidence. Every factual statement in it is separately asserted and sourced in this response's concepts and events.","reviewed_through_date":null,"source_count":3,"inspected_source_count":3,"consulted_reference_count":0,"blocked_reference_count":0,"set_aside_reference_count":0},"figures":[{"kind":"share-comparison","title":"Two surveys, opposite directions","caption":"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.","sourceNote":"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.","points":["Earlier reading","Later reading"],"seriesA":{"label":"State of Industrial Maintenance 2025","values":[30,27]},"seriesB":{"label":"Fluke Censuswide 2026","values":[9,18]},"gapLabel":"One survey has adoption falling; the other has it doubling."},{"kind":"composition","title":"What predictive maintenance is actually replacing","caption":"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.","sourceNote":"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.","totalDisplay":"% of maintenance activity","parts":[{"label":"Proactive (scheduled)","value":45,"display":"45%"},{"label":"Reactive","value":36,"display":"36%"},{"label":"Predictive","value":18,"display":"18%"}]},{"kind":"composition","title":"The barrier is people","caption":"Reported obstacles to digital maintenance progress. Skills-related answers account for about 78% of all barriers named.","sourceNote":"Fluke 2026 Censuswide survey. Self-reported obstacle categories; the four shown are the skills-related ones and do not sum to all barriers reported.","totalDisplay":"~78% of barriers named","parts":[{"label":"Expertise","value":23,"display":"23%"},{"label":"Skilled labour","value":19,"display":"19%"},{"label":"Knowledge","value":18,"display":"18%"},{"label":"Workforce","value":17,"display":"17%"}]}],"blocked_references":[],"set_aside_references":[],"concepts":[{"slug":"maintenance-skills-gap","name":"Maintenance Skills Gap","category":"other","canonical_url":"https://www.manufacturing.ai/topics/predictive-maintenance#maintenance-skills-gap","assertions":[{"id":"b2652924-9285-41db-805e-8e7d9509f047","predicate":"concept.description","statement":"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%.","assessment":"reported","editor_reviewed":false,"reference_only_source_count":0,"evidence":[{"id":"140de850-601e-4af9-be41-e09efa020755","stance":"supports","is_primary_for_assertion":false,"origin_independence":"unknown","note":null,"quote":null,"source":{"title":"Fluke Survey Finds Predictive Maintenance Adoption Doubles as Manufacturers Boost Digital Investment","publisher":"The Manila Times (reproducing Fluke's GlobeNewswire release)","url":"https://www.manilatimes.net/2026/05/07/tmt-newswire/globenewswire/fluke-survey-finds-predictive-maintenance-adoption-doubles-as-manufacturers-boost-digital-investment/2338228","source_type":"press_release","retrieval_status":"fetched","content_inspected":true,"published_at":"2026-05-07","documents":[]}},{"id":"f6ae7a72-ea06-4105-9098-a57bfca79b08","stance":"supports","is_primary_for_assertion":false,"origin_independence":"unknown","note":null,"quote":null,"source":{"title":"Fluke survey shows growth in predictive maintenance 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maintenance.","assessment":"reported","editor_reviewed":false,"reference_only_source_count":0,"evidence":[{"id":"80af3356-d576-4a86-872c-de86933c0933","stance":"supports","is_primary_for_assertion":false,"origin_independence":"unknown","note":null,"quote":null,"source":{"title":"Fluke Survey Finds Predictive Maintenance Adoption Doubles as Manufacturers Boost Digital Investment","publisher":"The Manila Times (reproducing Fluke's GlobeNewswire release)","url":"https://www.manilatimes.net/2026/05/07/tmt-newswire/globenewswire/fluke-survey-finds-predictive-maintenance-adoption-doubles-as-manufacturers-boost-digital-investment/2338228","source_type":"press_release","retrieval_status":"fetched","content_inspected":true,"published_at":"2026-05-07","documents":[]}},{"id":"f00527dc-a751-4991-8277-728f00ff3cfe","stance":"supports","is_primary_for_assertion":false,"origin_independence":"unknown","note":null,"quote":null,"source":{"title":"25 Maintenance Stats, Trends, And 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a fall from 30% to 27% — bases of 9% and 30% cannot describe the same threshold.","assessment":"disputed","editor_reviewed":false,"reference_only_source_count":0,"evidence":[{"id":"0e62ac11-d2e2-4ee3-98d2-dbe436f7a793","stance":"supports","is_primary_for_assertion":false,"origin_independence":"unknown","note":null,"quote":null,"source":{"title":"Fluke Survey Finds Predictive Maintenance Adoption Doubles as Manufacturers Boost Digital Investment","publisher":"The Manila Times (reproducing Fluke's GlobeNewswire 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Downtime","category":"other","canonical_url":"https://www.manufacturing.ai/topics/predictive-maintenance#unplanned-downtime","assertions":[{"id":"68ab45f1-a2fa-46da-b568-e9acdb0a1e7a","predicate":"concept.description","statement":"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.","assessment":"reported","editor_reviewed":false,"reference_only_source_count":0,"evidence":[{"id":"5c79c520-67d0-43bb-9f33-6d5f7cd4ef61","stance":"supports","is_primary_for_assertion":false,"origin_independence":"unknown","note":null,"quote":null,"source":{"title":"25 Maintenance Stats, Trends, And 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Sources that could not be retrieved are counted per-assertion in `reference_only_source_count`, listed for the whole collection in `consulted_references`, and never presented as evidence. `source_count` counts only sources an assertion actually rests on — it deliberately excludes `consulted_references`, so it is never inflated by material nobody could read.","topic_scope":"Assertions and source counts are limited to the topic's current packet lineage. Reusing a concept in another topic does not silently import that other topic's assertions.","rights":"Statements are Manufacturing.ai's own prose. Source excerpts are not redistributed; `quote` is present only where explicitly cleared.","attribution":"Cite as Manufacturing.ai, https://www.manufacturing.ai/topics/predictive-maintenance","correction_url":"https://www.manufacturing.ai/methodology#corrections"}}