Predictive maintenance readiness maturity
Your predictive maintenance readiness, measured by theme and converted into a roadmap.
9 themes, a 5-level scale. And the action that moves each level to the next.
The framework’s 9 themes, already written from L1 to L5. One company, one business unit, or 300 at once.
Predictive maintenance readiness maturity
9 themes, 5-level scale.
Nordhavn Industries
53 / 100
They measure their maturity with Datamensio
An example
This could be your situation.
Take one company as an example: three sites, three spreadsheets, no shared answer.
Nobody can consolidate.
Nordhavn Industries, 2,400 people in Hamburg, Lyon and Porto. A client asks where the group stands. Each site answers in its own spreadsheet, with its own scales.
Three weeks, a single base.
One Datamensio maturity framework · predictive maintenance readiness (CMMI scale, 5 levels) assessment launched across all three sites at once, from the managers’ interview notes. The framework was already written, its 9 themes and levels L1 to L5 too.
Two costs avoided before being committed.
A score of 53 out of 100, with the gap concentrated on three themes. The AI companion spotted that two actions duplicated those of another audit. The committee report took one sentence to request.
What it saved them
- 3sites measured on the same base, instead of three questionnaires to reconcile
- 2duplicate actions caught before the spend
- 1committee report, with no manual rework
These figures are an example. They could be yours.
The standard imposes processes. Datamensio says where you stand.
01
The framework is already written
Themes, questions and levels L1 to L5, all written. You do not start from an empty spreadsheet.
02
The score lands the same day
Online, by self-assessment link or in interview. Theme by theme, comparable over time.
03
The gap becomes a costed plan
Every step up carries its action. The AI prioritises on expected effect, not on the order of the standard.
04
Progress can be demonstrated
Campaign after campaign, against your target and against your own past. That is what your board asks for.
The maturity scale
One level, the next, and the action that links the two.
This mechanism (a level, a higher level, and the action linking the two) is what turns an observation into a trajectory.
Are past failures described in a way that can be used for analysis?
- N1
Failures are not described beyond the intervention report. No data links a breakdown to an identified piece of equipment.
- N2
Interventions are logged in the CMMS, but the cause is free text and the link to the asset is incomplete for part of the fleet.
- N3
Failures on critical assets are coded according to a failure mode taxonomy and linked to the equipment. Data entry is checked.
- N4
Coding covers the entire monitored fleet, with the start date of degradation recorded and cross-referencing possible with sensor signals over the period.
- N5
The taxonomy is revised based on reliability analyses and model feedback, with documented tracking of revisions and their effect on prediction quality.
Action to move from level 2 to level 3
Define a failure mode taxonomy for critical asset families, make it mandatory when closing work orders in the CMMS, and check the completeness of entries during the monthly maintenance review.
« With Datamensio, we meet our objectives far more efficiently. The ERDF inspection services and our supervising ministry particularly appreciated an approach that gives them reliable data. »
Director, CCI 94CCI Île-de-France
« We believe this is the most suitable solution to scale our transformation project and measure impact according to our needs. »
Maja SucekChief Operating Officer, Interreg Danube
Rarely on its own
Frameworks combine. Put several together to cover your business, or have the AI write yours.
Take your first measurement
What this framework covers
Predictive maintenance readiness is not just about choosing an algorithm. It describes an industrial organisation’s ability to move from corrective or calendar-based maintenance to maintenance triggered by the actual condition of equipment. This involves instrumenting assets, collecting and historising signals, ensuring the quality of equipment registers, tracing interventions and failures, building data science skills, and reorganising work orders. The framework covers this entire chain, from sensor to planning decision.
In practice, programmes almost always stumble on the same points. Does vibration or temperature data exist with enough history to learn something, or did historisation start six months ago? Are past failures described in the CMMS with a usable cause code, or in free text? And when a model flags a drift, who decides to stop the equipment, on what authority, and what happens when the signal turns out to be wrong twice in a row? The difficulty is rarely technical, it is organisational.
A common confusion is worth clearing up: condition-based maintenance and predictive maintenance are not the same thing. Threshold monitoring on a real-time measurement falls under the former and requires no learning. The latter estimates a remaining useful life or a probability of failure over a given horizon, and requires a history of documented degradations. Many programmes announce the latter while deploying the former, then find that the promised value never arrives. Knowing precisely where the organisation stands avoids this misunderstanding.
The maturity assessment does not frame the question in binary terms. It does not ask whether you do predictive maintenance, but at what level each link in the chain is mastered, on which assets, and what concrete action moves you to the next level. A site can be advanced on instrumentation and weak on how field teams use the outputs. The score by theme makes this asymmetry visible, the gap to the target generates the action plan, and the AI groups these actions into a prioritised roadmap.
The framework is ready to use and adapts to your asset base. The AI adjusts the themes, questions and levels to your sector, continuous process, discrete manufacturing, energy or transport, or builds a version from your own documents: maintenance policy, preventive maintenance plans, critical asset register.
Reference standard: Datamensio maturity framework · predictive maintenance readiness (CMMI scale, 5 levels)
The themes assessed
Maintenance strategy and asset criticality
Formalised maintenance policy, inventory and prioritisation of critical assets, choice of targeted equipment families, availability and cost objectives.
Instrumentation and acquisition
Sensor coverage, measured variables, sampling frequency, connectivity of controllers and supervision systems, management of non-instrumented equipment.
Historisation and machine data quality
History depth, continuity of series, management of gaps and sensor drift, timestamping, equipment identification register.
Traceability of interventions and failures
Completeness of the CMMS, coding of causes and failure modes, linking interventions to the asset, description of observed degradations.
Models and analytical methods
Approaches used (thresholds, supervised learning, remaining useful life estimation), performance validation, false positive management, retraining.
Platform and technical architecture
Collection chain, storage, edge or centralised computing, interoperability with the CMMS and industrial information system, OT network cybersecurity.
Integration into operational processes
Turning an alert into a work order, shutdown decision rules, coordination with planning and spare parts, feedback loop to the models.
Skills and organisation
Reliability, data and operations roles, training field teams to read indicators, dependence on suppliers, executive sponsorship of the topic.
Value measurement and improvement
Availability, maintenance cost and avoided failure indicators, before and after comparison, scaling up pilots, periodic review of the programme.
A short version of the framework is available for the online self-assessment.
Frequently asked questions
How does this assessment differ from an audit?
It does not conclude with a compliance verdict. Predictive maintenance is not a certifying framework: there is no binding requirement and no certifying body behind it. The assessment places each link in the chain on a progressive scale and indicates the action that raises it by one level.
How long does the assessment take?
The short version takes 20 to 30 minutes for a maintenance or reliability manager to complete. The full version, run collaboratively, spans one to two weeks: most of the time is spent cross-checking answers between maintenance, operations and industrial IT.
Do we need to have already launched a project to be assessed?
No, and this is often the best time to do it. The assessment measures readiness, meaning what exists before any model: instrumentation, history, CMMS data quality, organisation. The results indicate what needs to be consolidated before investing in analytics.
Does the framework adapt to our sector and asset base?
Yes. You can modify the questions, levels and themes, or start from a blank base. The AI generates a version adapted to a continuous process, a vehicle fleet or distributed infrastructure, based on your maintenance policy and asset register.
Can several sites be compared with each other?
This is the main use case in a multisite group. Each site is assessed against the same framework, scores by theme can be compared, and a cross-site roadmap consolidates the assessments to avoid duplicating actions common to the whole scope.
Does the assessment cover industrial network security?
It addresses this from a feasibility angle: connecting controllers and feeding OT data into an analytics platform requires a well-controlled architecture. For an in-depth treatment of the topic, a dedicated industrial cybersecurity assessment is still needed.
Where is the data hosted?
In France, with OVH, with backup at Scaleway. No transfer outside the European Union. The AI models used can be selected, including from European solutions.