Digital twin and industrial process simulation maturity
Your digital twin practices placed on a maturity scale, and converted into a costed roadmap.
10 themes, a 5-level scale. And the action that moves each level to the next.
The framework’s 10 themes, already written from L1 to L5. One company, one business unit, or 300 at once.
Digital twin and industrial process simulation maturity
10 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 Digital twin and industrial simulation maturity framework (drawing on ISO 23247 representation levels for manufacturing) assessment launched across all three sites at once, from the managers’ interview notes. The framework was already written, its 10 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.
It is this mechanism (one level, a level above, and the action linking the two) that turns a finding into a trajectory.
Is the model recalibrated against real production data after a change to the line?
- N1
No recalibration. The model reflects the state of the line at the time it was originally built.
- N2
Recalibration is done on an ad hoc basis, at the initiative of whoever built the model, without procedure or record.
- N3
A recalibration procedure exists and applies to major changes. Gaps between model and real data are measured and documented.
- N4
Recalibration is systematically triggered by the change management process. Gaps are tracked over time and a threshold triggers a model review.
- N5
The model recalibrates continuously against data flows, drifts are detected automatically and revisions are documented and shared across sites.
Action to move from L2 to L3
Attach model recalibration to the line’s change management process: add an update and gap verification step to the change record, with a named owner and a quantified comparison between model output and production data over a reference week.
« 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
An industrial digital twin is a digital model of a physical asset, a production line or a process, linked to field data and used to simulate, predict and arbitrate. It differs from a CAD mock-up through synchronisation: sensor, MES and CMMS data feed the model, which remains representative over time. It differs from a one-off simulation through persistence: the model lives, recalibrates itself and serves several successive decisions. The spectrum runs from offline flow simulation to the connected twin used for continuous optimisation.
In practice, the subject is hard to steer because initiatives spring up at different sites, with different tools, without a common framework. Three questions keep coming back. Is the model recalibrated against shop floor reality, and how often. Are simulation results used in a real decision, or do they remain a demonstration shown to a steering committee. Is the twin built for a layout project maintained after go live, or abandoned as soon as the line is running. Without answers to these three questions, modelling spend does not turn into operational gain.
The context has shifted on two points. First interoperability: standardisation work on manufacturing, ISO 23247 in particular, describes a reference architecture distinguishing the observable entity, data collection, the twin itself and the applications that use it. Second, data: the spread of industrial IoT architectures and data platforms has made synchronisation accessible, which shifts the difficulty towards model quality and governance. One confusion persists, that of equating digital twin with immersive 3D visualisation. 3D is a reporting mode, not proof of maturity.
The maturity assessment does not ask a compliance question. The point is not whether your twin meets an enforceable requirement, there is none. The point is to place each practice on a progressive scale, from an isolated attempt to an industrialised, measured use, then to identify the precise action that moves it to the next level. The score by theme shows where the effort needs to go: data, model, organisation or usage. Comparison across business units reveals which sites have found a repeatable way of working.
The framework is ready to use in Datamensio and remains editable. The AI adjusts the themes, questions and level wording to your sector, continuous process or discrete manufacturing, or builds a variant from your own documents: simulation specifications, modelling standards, pilot project reports.
Reference standard: Digital twin and industrial simulation maturity framework (drawing on ISO 23247 representation levels for manufacturing)
The themes assessed
Strategy and use cases
Existence of an explicit trajectory, selection of priority use cases, link with production challenges, decision criteria for committing to a new twin.
Industrial data and connectivity
Equipment instrumentation, collection from controllers, MES and CMMS, acquisition frequency, quality and completeness of series, handling of flow interruptions.
Modelling and fidelity
Modelling conventions, scope represented, documented assumptions, model validation against real data, version management.
Synchronisation and recalibration
Frequency of model updates, recalibration procedure after a physical change to the line, detection of gaps between model and reality, responsibility for upkeep.
Simulation and decision support
Simulated scenarios, flow, capacity and layout simulation, use of results in scheduling, sizing or investment decisions.
Prediction and optimisation
Predictive models for drift and failure, process parameter optimisation, learning from history, measurement of prediction performance.
Interoperability and architecture
Alignment with a reference architecture, exchange formats, links with PLM, MES and energy systems, portability between tools and between sites.
Security and IT/OT segregation
Securing flows from the shop floor, network segregation, access control over models, protection of process know-how contained in the twins.
Skills and organisation
Identified roles for modelling and upkeep, upskilling of methods and production teams, in house support or dependency on a supplier.
Value measurement and replication
Expected and observed gain indicators, follow up after go live, capitalisation of models, replication from one site to another, comparison across business units.
A short version of the framework is available for the online self-assessment.
Frequently asked questions
Is the digital twin subject to certification?
No. No body issues digital twin certification. Standardisation work exists, ISO 23247 describes a reference architecture for manufacturing, but these are interoperability benchmarks, not enforceable requirements. The subject belongs to industrial transformation, not compliance.
What is the difference between this assessment and a technical audit of our models?
A technical audit examines a given model and judges its accuracy. The assessment places the whole of your practices on a progressive scale: data, modelling, recalibration, use, organisation. It produces a score by theme and the trajectory for progress, not an expert opinion on one particular model.
How long does the assessment take?
The short version takes 20 to 30 minutes to complete by a methods manager or a programme lead. The full version, run collaboratively, generally spans one to two weeks: most of the time goes into gathering input from production, data and maintenance teams.
Can the framework be adapted to our sector?
Yes. The themes, questions and levels can be changed. The AI adjusts the wording depending on whether you run continuous processes or discrete manufacturing, or builds a variant from your modelling standards. You can also start from a blank base.
Can several sites be compared with each other?
Yes. The same framework applies to each business unit and scores can be compared by theme. A cross site roadmap consolidates assessments from several sites and groups together actions that recur, avoiding paying ten times over for the same level upgrade.
Do you need simulation expertise to answer?
The questions cover practices: where the data comes from, who maintains the model, how results are used. A methods manager or an industrial programme lead can answer them. The collaborative mode allows technical questions to be routed to the right person.
How is the action plan costed?
The gap between the observed score and the target generates the actions. The service catalogue matches a solution to each item, with its cost, timeframe and expected impact on the score. The roadmap is then steered through to delivery.
Where is the data hosted?
In France, with OVH, backed up with Scaleway. No transfer outside the European Union. The AI models used can be selected, including from European solutions.