Industrial IoT and connected industry maturity
Your connected industry measured by theme, then turned into a prioritised roadmap.
10 themes, 177 questions, 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.
Industrial IoT and connected industry maturity
10 themes, 177 questions, 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 Industrial IoT maturity framework (non-certifying), built on a 5-level 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.
It is this mechanism (a level, a level above, and the action linking the two) that turns an observation into a trajectory.
Is the data collected from equipment used in day-to-day production decisions?
- N1
Data stays within controllers or machine historians. No usable feed outside of technical interventions.
- N2
A few pieces of equipment feed dashboards, mostly consulted during one-off analyses or after an incident.
- N3
Data from a defined scope feeds indicators reviewed during production routines. Teams use it to identify stoppages and losses.
- N4
Data-driven indicators are the shared reference for production, maintenance and quality. Deviations trigger tracked actions, with an owner and a deadline.
- N5
Uses are reviewed periodically, indicators and models adjusted according to results, and the base is replicated on other lines or sites with documented lessons learned.
Action to move from L2 to L3
Choose a pilot line, define three indicators from machine data on it (availability, causes of stoppage, actual rate), display them at the workstation, and put them on the agenda of the daily production meeting with a named owner for deviations.
« 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 measurementon IoT.
What this framework covers
Industrial IoT covers all the capabilities that allow a piece of production equipment to produce useful data, and that data to flow through to a decision. This spans machine and utility instrumentation, collection protocols and gateways, convergence between operational technology and information systems, storage and modelling of time series, business uses (condition-based maintenance, yield tracking, traceability, energy) and the security of shop floor networks. This is an industrial transformation programme, not a requirement to satisfy.
In practice, it is hard to steer because maturity varies widely from one site to another and one shop floor to another. How many of your assets are truly instrumented, and on which parameters? Is the data collected actually used by production teams, or does it stop at a dashboard nobody opens? Who owns the shop floor network, from production through to IT? An industrial director often has numerous pilot projects and no consolidated view of the level reached.
One common confusion is worth clearing up: connecting is not the same as steering. Many organisations count their sensors and gateways, which measures equipment, not a capability. Maturity is read elsewhere: in the quality and contextualisation of data, in the standardisation of a base that can be replicated from site to site, in the ability to scale up a pilot rather than multiply it. The rise of unified namespace type architectures and growing network segmentation requirements are shifting the centre of gravity from the sensor to the architecture.
The maturity assessment answers a different question to a technical inventory. The inventory says what is installed. The assessment places each theme on a progressive scale and points to the precise action that moves it up a level. The gap between the score and the target generates the action plan, which the AI groups into a prioritised roadmap. Scores can be compared across business units and over time, which makes it possible to track a site’s trajectory objectively rather than argue over impressions.
The framework is ready to use and adaptable. You adjust themes, questions and levels to your sector, discrete or process, single site or multi-site. The AI refines level wording according to the CMMI method, or builds a variant from your own documents: industrial architecture diagrams, site standards, factory master plan.
Reference standard: Industrial IoT maturity framework (non-certifying), built on a 5-level CMMI scale
The themes assessed
Strategy and governance of connected industry
Existence of a formalised trajectory, alignment with the industrial plan, roles between production, methods and IT, project funding model.
Instrumentation of equipment and utilities
Coverage of machines and utilities, parameters measured, sensor and retrofit standards, inclusion of instrumentation in machine purchase specifications.
Connectivity and shop floor networks
Network architecture, wired and wireless coverage, industrial protocols and gateways, management of offline equipment, availability and redundancy.
OT and IT convergence
Boundaries and responsibilities between operational technology and information systems, interfaces with MES and ERP, lifecycle and update management, architecture trade-offs.
Data collection, quality and contextualisation
Data models and tag naming, timestamping, time series management, completeness and reliability of flows, linkage to work orders and equipment.
Operation and industrial use cases
Performance and OEE tracking, condition-based and predictive maintenance, product traceability, energy monitoring, quality control, adoption by shop floor teams.
Security of connected industrial systems
Equipment inventory, segmentation and isolation, management of remote access and contractors, anomaly detection, continuity plans in case of unavailability.
Platforms, edge and cloud
Distribution of processing between edge and centre, hosting choice, interoperability and reversibility, management of storage and compute costs.
Skills and organisation
In-house skills in automation, data and industrial cybersecurity, operator training, dependence on integrators, cross-site coordination arrangements.
Scaling up and measuring value
Standardisation of a replicable base, per-site deployment kit, measurement of expected and actual gains, decision to scale up or stop a pilot.
A short version of the framework, with 34 questions, is available for the online self-assessment. The full version covers 10 themes and 177 questions.
Frequently asked questions
Does this framework lead to certification?
No. Industrial IoT maturity is not a certifiable field: no body issues an attestation on the subject. The assessment measures the level reached by your practices and produces the trajectory for improvement, site by site.
How is this different from a technical inventory of our connected equipment?
The inventory says what is installed. The assessment places each theme on a progressive scale, from network base to shop floor use, and links each gap to an action that moves it up a level. A heavily instrumented site can still show low maturity if the data is not used.
How long does the assessment take?
The short version takes 20 to 30 minutes to complete for a manager who knows the scope. In collaborative mode across a site, with methods, maintenance, industrial IT and production involved, allow one to two weeks, most of the time going into data collection.
Can the framework be adapted to our sector?
Yes. Themes, questions and levels can be modified, and you can add your own axes, for example the constraints of a process or regulated environment. The AI adjusts the wording according to the CMMI method or builds a variant from your architecture documents and site standards.
How can several factories be compared without pitting them against each other?
The same framework is applied to each site, making scores comparable by theme. The benchmark is used to identify where one site has already solved a problem others face, and to reuse its solution. A cross-site roadmap then consolidates the assessments into a group-wide plan.
Is the action plan costed?
Each gap between the score and the target generates actions, to which the service catalogue attaches a solution with its cost, timeframe and expected impact on the score. You decide based on orders of magnitude, not intentions.
Does answering require technical expertise?
The questions concern practices and organisation, not controller configurations. Some require input from an automation engineer or a network manager: collaborative mode lets you assign these questions to the right person.
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.