Smart factory maturity · Industrial assessment framework
The digital maturity of your production sites, measured site by site and turned into an industrial 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.
Smart factory maturity · Industrial assessment framework
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 Smart factory maturity framework, inspired by Industry 4.0 frameworks and the CMMI scale 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.
This mechanism (a level, a level above, and the action that connects the two) is what turns a finding into a trajectory.
How is machine data collected and used to run production?
- N1
No automatic collection. Downtime and quantities produced are recorded by hand on sheets or in an end of shift spreadsheet.
- N2
A few machines report data to a local tool. Definitions vary from one line to another and figures are reworked before being circulated.
- N3
Collection is automatic on critical equipment, indicators are defined the same way across the site and displayed on the shop floor in line with shift rhythms.
- N4
Data feeds into production routines and decisions on stoppages, adjustments and maintenance. Deviations are analysed with the teams and logged.
- N5
Data is comparable across sites, reconciled with management systems, and underpins predictive models whose performance is tracked and reviewed.
Action to move from L2 to L3
Lock down a single definition of OEE and stoppage causes for the site, roll it out on critical equipment already connected, and display the resulting indicators at the daily production meeting rather than in a reworked file.
« 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
The smart factory maturity model assesses an industrial site’s ability to run its production based on its data. It covers equipment connectivity, machine data collection and quality, integration between production systems and management systems, automation and robotics, the use of analytics and machine learning, internal logistics, maintenance, energy performance and team skills. Each dimension sits on a progressive scale, from a site with no instrumentation through to a site that adjusts its production parameters based on predictive models.
In practice, the difficulty is not technological, it is one of governance. Projects get launched site by site, line by line, sometimes team by team, and no one can say what level the organisation as a whole has reached. How many of your machines actually report usable data, rather than a simple counter? Are OEE indicators calculated the same way from one site to another, or rebuilt locally in a spreadsheet? Have use cases that worked on one site been rolled out elsewhere, or redeveloped from scratch?
One confusion comes up often: equipping is not the same as running. A site may have deployed sensors, an IoT platform and shop floor screens while remaining at the same maturity level, because the data feeds into no decision at all. Conversely, a site with modest instrumentation whose teams use a handful of reliable indicators every day progresses faster. The model therefore separates equipment, usage and organisational embedding, and it is this separation that makes comparisons between sites meaningful.
The maturity assessment does not answer the question of compliance. It does not seek to establish whether a requirement is met, but to place each practice on a scale and name the action that moves it up to the next level. The score is not a grade: it is the starting point of an action plan. The gap between the observed level and the target level generates the actions, which the AI groups into a prioritised roadmap, with a cost and a timeframe against each item.
The framework is ready to use in Datamensio and adapts to your industry. The AI adjusts the themes, questions and level wording to the vocabulary of your operations, whether process or discrete manufacturing, or builds a variant from your own documents: industrial master plan, group standards, site assessment reports.
Reference standard: Smart factory maturity framework, inspired by Industry 4.0 frameworks and the CMMI scale
The themes assessed
Industrial strategy and governance
Existence of an industrial master plan, alignment of projects with production objectives, investment arbitration, roles between group and sites, tracking of benefits realised.
Equipment connectivity and the OT layer
Proportion of instrumented machines, protocols used, age of the equipment base, gateways and PLCs, industrial network segmentation, handling of non-communicating equipment.
Production data and quality
Machine data collection, historisation, shared indicator definitions, reliability of OEE and downtime figures, batch traceability, master data governance.
Integration of production systems
MES scope, interplay with ERP and CMMS, scheduling, recipe and routing version management, remaining manual interfaces.
Automation and robotics
Level of automation of operations and controls, robots and cobots deployed, vision systems, changeovers, standardisation of cells across lines and sites.
Advanced analytics and artificial intelligence
Production use cases, predictive maintenance, quality anomaly detection, process parameter optimisation, model industrialisation and performance tracking.
Maintenance and asset management
Share of preventive and condition based maintenance, automatic alert reporting, intervention history, spare parts management, equipment criticality.
Internal logistics and flows
Work in progress tracking, automatic identification of containers, workstation replenishment, automation of internal transport, synchronisation with planning.
Energy and environmental footprint
Metering by workshop or line, linking consumption to production, drift detection, tracking of consumables and waste, consolidated indicators.
Skills and work organisation
Training of operators and technicians in digital tools, data driven routines, the shop floor’s role in choosing solutions, ability to spread a success from one site to another.
A short version of the framework is available for the online self-assessment.
Frequently asked questions
Does this model lead to a certification?
No. The smart factory is not a standard and no body issues recognition on this subject. The model is used to assess a site’s maturity, compare it and build a trajectory. If you are looking for a certifying framework on a related subject, look at asset management or energy.
How does this differ from a conventional industrial audit?
An audit checks control points and concludes with findings. The maturity assessment places each practice on a five level scale and identifies the action that moves it up a level. The result is not a report of findings but a costed, prioritised action plan tracked over time.
How long does the assessment take?
A self assessment run by a plant manager or a methods manager is completed in a single working session. A multi site campaign run collaboratively takes a few weeks, most of the time being spent gathering responses from production, maintenance and industrial IT teams.
Can plants that make different things be compared?
Yes, provided you assess operating practices rather than the technologies in place. That is the principle behind the model. Benchmarking between business units puts sites on the same scale, and comparison with previous campaigns shows each site’s progress.
Can the framework be adapted to our industry?
Yes. You can amend the themes, questions and level wording, or start from your own documents: the AI then builds a variant aligned with your industrial vocabulary and group standards. The framework remains your property.
Do respondents need technical expertise?
The questions concern operating practices, not PLC configurations. A production or methods manager can answer most of them. Collaborative mode allows connectivity, integration or industrial cybersecurity questions to be assigned to the right contact.
How is the action plan costed?
Each gap between the observed level and the target level generates an action. The service catalogue attaches a solution to each item, with its cost, timeframe and expected impact on the score. The roadmap consolidates everything, including across several separately assessed sites.
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.