A Practical Digital Maturity Model for Manufacturers

Despite years of digital investment, many manufacturers still struggle to scale beyond pilots. From disconnected apps to inconsistent rollouts across sites, it's clear that most teams are digitizing but few are maturing.

Without a structured approach, even strong initiatives stall out. That’s the situation Terex was facing. As a global manufacturer with operations across three continents, their teams were experimenting, building apps, and testing new ideas, but in silos. The Genie division, in particular, saw how uneven digital maturity across factories was holding back momentum.

At Ops Calling 2025, two senior IT leaders from Terex, David Judgitz, Chief Digital Officer, and Ken Macintosh, Senior Director of Digital Manufacturing, shared how they tackled this challenge head-on.

Here, we’ll break down how Terex used a practical digital maturity model to bring structure to their rollout, helping them assess readiness across factories, track app performance, and scale what actually works. Whether you're exploring AI, citizen development, or just trying to unify your digital operations, there's a lot to take away.

A digital maturity model is a framework for assessing how prepared your organization is to adopt, scale, and sustain digital capabilities.

It gives teams a structured way to understand their current state, identify gaps, and focus resources on the areas that will deliver the greatest impact. Instead of guessing which projects are ready to scale, leaders can use the model to guide decisions with more clarity and less risk.

A maturity model helps everyone understand where they stand, but real progress only happens when the whole organization moves in the same direction. You need leadership setting the course and the people on the floor shaping how the work actually gets done.

Top-down:
Leadership sets priorities, commits budget, and connects digital work with business goals. In practice, this means senior teams step in early to pick use cases, clear roadblocks, and agree on rollout plans.

Bottom-up:
Engineers, operators, and frontline teams bring ideas, build apps, and give steady feedback. They’re not just using what’s handed to them, they’re helping shape it.

To keep things moving at Terex, teams met regularly. Quick check-ins, office hours, and reviews of new apps kept everyone connected. It gave people visibility into what was working, what wasn’t, and what needed attention. It also helped IT, engineering, and operations stay aligned as new tools rolled out.

"...Empowering them with tools…both from my side on the IT and digital manufacturing side and citizen development with our engineers, we get together a couple times a week, whether it's through Tulip-hosted office hours events or through our own internal meetings. It's become fun. It's clever..." Ken Macintosh

Without a clear way to measure readiness, digital initiatives often stall or stay siloed. Maturity models help teams step back, assess where each site or app stands, and focus on what’s actually ready to scale.

Here’s how manufacturers put them to work.

Step 1: Assess Factory-Level Maturity

This helps you evaluate how ready each site is to take on new digital tools. Most teams look across a few core dimensions:

MES or execution system adoption, tracking materials and material movements, capturing data for compliance or traceability, automation of data capture, and real-time visibility.

Some manufacturers build internal frameworks. Others start with third-party maturity assessments to benchmark capabilities and highlight gaps.

The goal is an honest view of where you're ready to scale and where foundational work is still needed, so leaders can prioritize factories based on readiness, not guesswork.

Step 2: Prioritize Use Cases by Value and Effort

Once readiness is clear, the next step is figuring out what to focus on. Most teams use a simple filter:

High impact + low effort → Act quickly
High impact + high effort → Plan and resource
Low impact or unclear value → Park for later

Some visualize this in a Pareto chart, a quick way to spot which 20% of use cases will drive 80% of the value. It helps turn scattered ideas into a focused roadmap.

Step 3: Track App-Level Maturity Over Time

If your teams are building apps (especially through no-code platforms or citizen development), it helps to track where each app is in its lifecycle:

Level 1 – Build: App is solving a local problem and being iterated
Level 2 – Use: Stable and used across lines or zones
Level 3 – Scale: Standardized, documented, and ready for rollout across sites

This lets you avoid spreading unproven apps too widely and focus support on what’s already working.

Step 4: Align on What to Scale

With both factory and app maturity mapped out, teams can make better decisions about what to scale, where to invest, and what to hold. Instead of chasing every idea, you’re building a focused, staged roadmap that reflects real operational readiness.

“I think you start with a vision… you need global buy-in… so having that buy-in from leadership on what the strategy is going to be, how you’re going to execute, and then setting up key metrics that you can identify. And I think it’s okay to have failure in the development life cycle, it’s actually good. When you see what’s really working, really double down and influence that.” - David Judgitz

As digital maturity improves, AI gets a lot easier to put to work. Not because the technology changes, but because the basics like data, structure, and stable workflows, are finally in good shape.

Here are a few of the use cases Terex explored during their rollout:

AI-generated work instructions
Simple videos of manual tasks were enough. AI turned those clips into step-by-step instructions that helped with training and reduced the need to start new SOPs from scratch.

App review copilots
Before new apps went live, AI tools scanned them for logic gaps and messy data handling. It gave builders a clearer sense of where things might break.

"One of our goals was really around productivity…We set some pretty clear targets and KPIs… We can clearly see that we are harvesting those hours and it's allowing us to do more with less."- David Judgitz

These examples aren’t edge cases. They only work because the digital foundation underneath them is steady. Once that foundation exists, AI isn’t a separate program, it becomes something that fits naturally into everyday work

Digital maturity isn’t the finish line. The real measure is the impact you see on the floor. When the right tools land in the right places, the gains start to show up quickly. Things like:

• Higher uptime

• Labor savings

• Fewer hours per unit

• Shorter product cycle times

• Better, faster data capture

These aren’t guesses. They’re tracked in the same systems that maturity helps put in place. Teams can compare performance across workstations, lines, or sites and use real-time data to guide decisions.

"...Now we can measure the time that it was logged, the time that I accepted it, who's got it. It's really helped us understand how to solve and where we need to apply pressure to solve." - Ken Macintosh

As the data gets cleaner and more complete, bottlenecks become easier to spot. Processes get more consistent. Improvements compound. And the organization starts to see digital maturity turn into actual operational change.

Tulip gives teams a practical way to digitize their work and roll out improvements without slowing the operation down.

• A no-code builder that lets teams create and adjust frontline apps without waiting on long development cycles

• A library of templates so common workflows look and behave the same across lines and sites

• Governance and version control built in, making citizen development easier to guide and maintain

• Real-time data capture and dashboards that show what’s happening on machines, lines, or whole shifts

• AI tools that help generate work instructions, check app logic, and support deployments

• Digital instructions and SOPs that make training simpler and help new operators get up to speed faster