What is a digital maturity level?

It is a stepped scale describing how systematically a plant collects its data, how quickly it can use that data, and how far its decisions rest on evidence rather than recollection. It can be read in five levels, from paper records to predictive monitoring.

How do I tell which level my factory is on?

The most practical test: how long does it take to learn which machine stopped most on yesterday's second shift, and why? If the answer is 'I'd have to ask someone' you are on level 1; 'a few hours' is level 2; 'a few minutes' is level 3.

Can you skip a level?

Skipping usually fails. An AI layer will not work without a regular, trustworthy flow of data underneath it. Collect the data first, make it visible second, and move to prediction last.

Does digital maturity require new hardware?

In most plants, no. If the machines already produce data, the existing PLC or SCADA infrastructure is usually enough to reach level 3. Extra sensors are only needed when something you want to monitor is not measured at all.

What Is Digital Maturity Level?

Most of the projects we discuss under the heading “digital transformation” come down to a single question: where does this factory actually stand today? Because the same investment pays for itself in two months at one plant and never works at another — and the difference is usually not the product, it’s the starting point.

The five steps below are not an academic model. They are a simplified version of what we keep finding on the floor.

1. Paper and memory

The data exists, but it lives inside people. The shift supervisor knows the stop reason, the operator remembers which die causes trouble, output goes into a notebook.

The tell: answering “what happened on the second shift yesterday?” requires phoning someone.

This level is less rare than people assume, and it isn’t a failure in itself — it works for a long time in a small plant. The problem appears as the plant grows: memory doesn’t scale.

Next step: standardise how things are recorded. Not software yet — a shared list of stop reasons.

2. Spreadsheets and retrospective reporting

Data is collected, but by hand and after the fact. Forms are filled in at the end of a shift, someone types them into a sheet, a report appears at month end.

The tell: the report is accurate but late. By the time you notice a problem, it happened three weeks ago.

There’s a second tell: if two people calculate the same month’s OEE differently, the definition isn’t standard yet. How OEE is actually calculated is the thing to settle at this level.

Next step: take the data out of human hands. Every manually entered field is both a delay and an error source.

3. Connected monitoring

Machines are connected, status and counters flow automatically. OEE is visible as it happens, stops appear on screen while they’re happening.

The tell: you can answer “which line is down right now?” by looking at a screen instead of asking anyone.

Reaching this level is easier than most expect, for one reason: the signals needed are usually already produced by the existing PLC or SCADA infrastructure. Where factory data comes from is more often a question of reading what exists than of buying something new.

Next step: give the data context. Seeing a number and knowing what it means are not the same thing.

4. Digital twin and operational intelligence

The data no longer just flows; it lands on a model of the factory. Which machine, on which line, on which shift, producing which part, is losing you the most — all in one place.

The tell: meetings no longer argue about which line is the problem. Everyone looks at the same screen and the discussion moves straight to the fix.

This level usually goes by the name operational intelligence: the layer that turns a raw signal into a decision. A digital twin is what carries it — the model is what gives the number its context.

Next step: move from explaining the past to pointing at the future.

5. Predictive monitoring and decision intelligence

There are two sides here, and they answer different questions.

Predictive monitoring looks at the floor you have: this machine won’t finish the shift at this rate, this stop reason is going to repeat. The warning arrives before the event. Its measure is equally concrete — you should be able to see how many of those warnings actually came true. If you can’t, it isn’t prediction, it’s an impression of prediction.

Decision intelligence looks at what doesn’t exist yet: knowing a new line’s capacity before you build it, a new robot’s cycle time before you buy it. The tool here isn’t live data, it’s factory simulation — putting a number behind the decision before any steel is cut.

The tell: in investment meetings, sentences that start with “I think” get replaced by scenario comparisons.

What skipping a step costs

The most common mistake we see is a plant on level 2 trying to buy level 5 outright. An AI layer will not work without a regular, trustworthy flow of data beneath it — a prediction fed on junk isn’t a prediction.

The reverse is also a cost: getting stuck on level 3. Screens go up and nobody looks at them, because numbers on their own say very little until they sit in context.

Where to start

You don’t have to move the whole factory at once. In practice the best starting point is the single line where the problem is most visible: one whose data already flows, whose bottleneck is known, and whose improvement can be measured. Climbing one step and seeing it work there is both faster and cheaper than trying to cover everything from the outset.

If you’re not sure which step you’re on, the quickest route is a conversation: tell us about your current setup and we’ll work out where to start together. You can write to us from the contact page.