From Dashboards to Predictive Operations: How Volvo Group Is Using Data, AI and Digital Twins to Drive 30% Productivity Improvement

As manufacturers look to unlock greater value from AI, predictive analytics and digital twins, many organizations still face familiar challenges around data quality, user adoption and scaling innovation beyond pilot projects.

We sat down with Ivan Branco, Director of Data & AI within Service Operations & Technology at Volvo Group, to discuss Volvo Group's journey from traditional dashboard reporting to predictive decision-making.

As part of Volvo Group's global aftermarket organization, Ivan and his team are responsible for data management, governance, AI and operational visibility across a worldwide network of warehouses and service operations.

In this interview, you will discover:

• How Volvo Group uses predictive analytics to maximize customer uptime
• Why some digital twin initiatives delivered ROI in just 3-6 months
• How data and AI have already contributed to a 12% productivity improvement
• Why data quality, not technology, is often the biggest barrier to scale
• How Volvo avoids pilot purgatory by focusing on proof of value over proof of concept

What operational challenge were you trying to solve when moving beyond traditional dashboard-based decision-making?

Ivan Branco: The main objective was visibility, transparency and fast access to information.

Historically, if an individual in operations wanted to understand inventory levels or analyze sales performance, they would need to ask an analyst to create a report. The analyst would then have to consolidate, harmonize and validate the data before providing an answer.

Whereas today, because the harmonization and modeling have already been completed, users can ask a question directly and receive an answer almost immediately, with GenAI accelerating this process significantly.

At the same time, users still need to perform due diligence and validate the results. Even though technology helps us move faster, accountability still remains important.

The goal is simple: enable better decisions, made more quickly.

At The Connected Worker Manufacturing Summit 2026, your session will explore the journey from descriptive dashboards to predictive and prescriptive operations. What were the key stages of that transformation?

Ivan Branco: Technology was not the biggest challenge.

We mostly leveraged technologies already available in the market, including AI, GenAI and agentic AI. The real challenge was understanding our business processes and translating that knowledge into data models.

Throughout, our biggest investment was engaging with subject matter experts across the organization. They had to explain how the business operates, what the data means and how different systems relate to one another.

Many organizations assume this work belongs solely to IT. In reality, technical development is often the shortest part of the project. The difficult work is understanding the data, linking systems together and ensuring everyone is working from the same version of the truth.

By doing that, you're also improving data quality, data readiness and AI readiness.

Can you share an example where predictive capabilities enabled better decision-making than traditional reporting?

Ivan Branco: We do this constantly within our aftermarket business. In the transportation industry, success is measured by uptime. Our continuous challenge is to maximize the amount of time customers can keep their vehicles operating. By collecting historical data relating to trucks, repairs, parts usage and inventory levels across our dealer network and warehouses, we can predict with a high level of accuracy when a vehicle is likely to experience a failure.

"The starting point should not be the technology, it should be the problem you're trying to solve."

Using those insights, we proactively contact customers and recommend a service window before a breakdown occurs. We can align maintenance with the vehicle routes and ensure the correct parts are available at the right dealership along the way.

The difference is significant. Instead of dealing with an unexpected breakdown that might take hours or even days to resolve, the issue can often be addressed during a planned service visit. All while we ensure that the dealer inventories are positioned to support any future demand.

How are digital twins being used across your operations today?

Ivan Branco: At Volvo Group, digital twins are used in several ways.

On the product side, our engineering teams use digital replicas of components to understand performance issues and improve future designs. Historical data can be analyzed alongside the digital twin to identify why certain parts fail and how they can be improved.

Within our warehouse operations, we maintain digital replicas of facilities. These environments provide visibility into inventory, equipment movements and operational activity.

We use heat maps and simulation capabilities to understand how the facility is operating, identify bottlenecks and optimize layout decisions. That way, operators can determine where high-moving inventory should be stored and test potential changes before making physical modifications.

Heat maps also help areas that present increased operational or safety risks, giving teams another layer of visibility when optimizing warehouse performance.

The key advantage is that the changes that once required weeks of planning can now be evaluated virtually, in minutes.

How quickly did you realize ROI from your digital twin initiatives?

Ivan Branco: It really depends on the level of complexity of the initiative.

We started with a simple solution that essentially overlaid visual information on top of existing reports. That allowed us to save significant time on activities such as inventory counting and generated a ROI within roughly 3-6 months.

As we've moved into more sophisticated digital twin models, the return period can extend to 1 or 2-years. However, if a simulation identifies a major opportunity, such as optimizing an entire warehouse layout, the value can be realized much more quickly through reduced tied-up capital and improved operational efficiency.

What measurable business outcomes have been achieved so far?

Ivan Branco: The annual investment required to operate the platform represents roughly 10% of the value it has generated for the business. We also set a strategic objective to improve productivity by 30%. While we haven't fully reached our target yet, we've already achieved approximately 12% improvement.

What are the biggest implementation challenges?

Ivan Branco: Change management and user adoption remain the largest hurdles.

We operate warehouses across the globe, which means engaging different teams, cultures and ways of working. To see results, you must explain the benefits repeatedly and ensure people understand why a new approach is better than the old one.

"The biggest investment wasn't technology. It was understanding the business and getting everyone to look at the same picture."

That said, data quality is the foundation for everything. If you don't have quality data, you can't successfully deploy AI, digital twins or even basic reporting.

Data quality is often viewed as a technology issue, but in reality it's usually a process issue. Most problems originate from how data is entered and managed by people.

That's why governance, education and process improvement are so important.

Can you share a real-world example of poor data quality creating operational challenges?

Ivan Branco: Country-of-origin data is a great example.

Every vehicle part has a declared country of origin and inaccuracies can have major customs implications. In some cases, the consequences can be extremely costly, including border delays, fines or other compliance issues.

Historically, individuals sometimes entered the supplier's location rather than the actual manufacturing location of the component. So, we had to educate our procurement teams about why that distinction matters and how the information is used downstream. Alongside that education, we introduced controls and measurements to monitor improvement.

It reinforced an important lesson: improving data quality is usually about changing behaviors rather than changing systems.

Many organizations struggle to move beyond pilots. What's the biggest misconception about scaling these initiatives?

Ivan Branco: In my experience, the process is very straightforward.

A pilot exists to prove value. If the value isn't demonstrated, you will stop. If it is demonstrated, you move into project mode and industrialize the solution.

The mistake organizations repeatedly make is starting with technology rather than the problem they are trying to solve.

At Volvo Group, we first identify a business challenge or opportunity. Then we determine which technology can help address it.

Technology is never the objective. The starting point should not be technology, rather it should be the problem you're trying to solve. We first identify a business challenge or opportunity, then determine which technology is best suited to address it. That's why we focus on proof-of-value (POV) rather than proof-of-concept (POC.)

How do you overcome barriers to frontline adoption?

Ivan Branco: We are involved in the business from the beginning. The teams that will ultimately use the solution participate in the evaluation process, validate the POV and help shape the outcome. Involving the frontline workers in the decisions, from the offset, creates a buy-in early on.

In many cases, our frontline teams are the ones pushing us to move faster. We don't work in silos. We try to break down barriers between departments and make people part of the journey.

Looking ahead to The Connected Worker Manufacturing Summit 2026, what are you most looking forward to?

Ivan Branco: Put simply, I'm looking forward to learning. It's easy to become focused on your own reality and lose sight of what other organizations are doing. In my experience, The Connected Worker Manufacturing Summit 2026 is an opportunity to hear about other challenges, understand different approaches and learn from experiences outside my own organization.

I never attend industry events with specific expectations. Rather, I come with a blank sheet of paper, ready to learn, absorb new ideas and challenge myself. Sometimes the most valuable insights come from sessions you didn't expect to be relevant.

Ivan Branco will be speaking at the Connected Worker Manufacturing Summit, where he'll share more about Volvo Group's journey from dashboards to predictive and prescriptive operations, the role of digital twins in industrial environments and how business-led innovation is helping drive measurable operational value.

Join the conversation in Chicago, 2026, at The Connected Worker Manufacturing Summit and ensure your connected worker strategy is built for the future.