EPD 2026: What We Learned About the Future of Digital Engineering

Engineering Process Days 2026 made one thing clear: digital engineering is no longer a narrow engineering process conversation.

Hosted by :em AG in Darmstadt, Germany, the 18th Engineering Process Days brought together manufacturers, technology leaders, and digital engineering practitioners to explore how digitalization is changing engineering and manufacturing. The event’s theme, “Digitalization meets Engineering & Manufacturing,” was fitting. Across two days, the agenda covered PLM, MBSE, requirements, simulation data management, configuration, digital twins, software development, IT architecture, AI, supplier collaboration, and production-connected digital transformation. The organizers and presenters brought that wide range of topics together into an effective day-and-a-half thought leadership experience.

The strongest takeaway from the event was that engineering, manufacturing, quality, software, suppliers, and business systems are becoming more connected, more interdependent, and well positioned to benefit from AI-enabled workflows.

For Anark, that made Engineering Process Days 2026 especially relevant. The event reinforced many of the same themes we see every day with complex manufacturers: product complexity is increasing, engineering decisions are becoming more cross-functional, and the digital thread is only valuable when people across the product lifecycle can actually use it.

Digital engineering is now a lifecycle conversation

One of the clearest patterns across the event was how broad the digital engineering conversation has become.

PLM was still central, but it was no longer discussed in isolation. The sessions repeatedly connected PLM to MBSE, software development, requirements, simulation, ERP, MES/MOM, SAP, configuration management, digital manufacturing, supplier collaboration, and AI.

That reflects the reality inside large manufacturing organizations. Product data does not live in one place. Product decisions do not happen in one function. And product outcomes are shaped by many stakeholders who often work across different systems, different processes, and different levels of technical depth.

Several presentations highlighted this enterprise complexity directly. The modern digital thread is more than technical integration between systems. It connects engineering intent, product structure, process context, manufacturing requirements, quality expectations, supplier inputs, and lifecycle decisions.

That is a much harder challenge than digitizing an artifact or implementing a single enterprise platform. And it delivers much more value on a different scale.

Complexity is the business problem underneath the technology

Another major theme was complexity. 

Manufacturers are dealing with more product variation, more configuration pressure, more software-defined functionality, more supplier dependency, and faster expectations from the business. That complexity shows up in engineering capacity, product development speed, quality risk, and the ability to scale without adding unnecessary overhead.

The AWS keynote framed this well by connecting product complexity, disruption, and the move toward more adaptive products: Manufacturers are being asked to move faster while the products themselves are becoming more configurable, more connected, and more dynamic over time.

Belimo’s presentation made the business impact especially tangible. Their digital transformation story connected platform strategy, MBSE, PLM, digital thread foundations, process discipline, and business scalability. The point was not simply to become more digital. It was to improve productivity, support growth, maintain quality, and create the operational discipline required for future automation and AI.

"We need to find the next innovation to drive growth, but we can't compromise on quality. This requires a lot of discipline." -Elena Cortona, Belimo.

That is an important distinction. Digital engineering initiatives succeed when they are tied to business outcomes. Speed, quality, productivity, resilience, and growth are the real measures of progress.

Real companies live in ecosystems, not single systems

A repeated theme throughout the event was that manufacturers are no longer striving to operate from one clean, centralized single source of truth. In fact, it was just the opposite. They are operating across ecosystems, assembling best-in-class technologies for various areas of the business.

Hilti’s discussion captured this reality with a practical point: large enterprises will not have one solution for everything. They will continue to operate across multiple systems, domains, processes, and stakeholder groups. Organizational change management requires a lot of time and effort, so leaders are being careful where to choose the wisest investments.

"We will never have [only] one solution. We need to deliver products with speed, and quality, to drive new sales." -Christine Kramer, Head of PLM, Hilti

That point showed up across the event. Companies are working across PLM, ERP, CAD, requirements, simulation, software, manufacturing, quality, supplier systems, test data, documentation, and collaboration tools. Each system plays a role. Each function has its own needs. Each domain has its own language.

The future of digital engineering will depend on how well organizations can connect those systems and people without losing context. Without a concerted effort to bridge the gaps, hidden costs will continue to eat away at profitability and time to market. 

"Inconsistencies in the way teams collaborate is work that your customers will never pay for." -Marcus Zimmermann, Rolls-Royce Power Systems AG

When teams have to recreate product context in screenshots, emails, spreadsheets, slide decks, file shares, and disconnected meetings, the enterprise pays for the same work multiple times.

AI depends on better digital thread foundations

AI was present throughout the event, but the most useful conversations did not treat AI as a magic layer added on top of disconnected data.

Instead, several sessions connected AI readiness to digital thread maturity, semantic context, domain standards, data governance, ontology, and knowledge graph approaches. 

AI across the engineering lifecycle will only be as useful as the product context it can access, understand, and trust. If engineering knowledge is trapped in disconnected files, undocumented decisions, isolated systems, and informal collaboration channels, AI will struggle to deliver meaningful results.

Rolls-Royce Power Systems’ presentation was especially aligned with this theme. Their discussion of what agentic AI requires, including proprietary data, domain standards, ontology, and governance, underscored a critical point: future AI agents will need structured, connected, and governed product context.

Marcus Zimmermann’s discussion of digital threads and knowledge graphs reinforced the same idea. The term “digital thread” can mean many things, but its future value depends on making relationships between data, decisions, systems, and stakeholders more explicit. At that point, the digital thread will go beyond traceability and become a foundation for AI-enabled engineering.

Adoption still comes down to people and process

Despite the advanced technology discussed throughout the event, one of the strongest recurring messages was human centered.

Digital transformation depends on people, process discipline, and adoption. Several presenters emphasized the need to find champions, build champions, and focus on real process change rather than tool deployment alone. As one presenter put it:

"This is not about the tool. It is about enabling new ways to collaborate through a new tool."

That is a critical lesson for digital engineering leaders. Technology can create the foundation, but people still have to trust it, use it, and change how decisions are made.

This is especially true when digital engineering expands beyond engineering teams. Manufacturing, quality, suppliers, procurement, service, and business stakeholders need access to product context in ways they can understand and act on. They do not all work in CAD. They do not all live in PLM. But they all influence product outcomes.

The companies that succeed will be the ones that make digital engineering useful across the full product lifecycle.

From Engineering Process Days to Engineering & Production Days

Looking ahead to next year, Marcus Krastel, CEO of :em engineering methods AG, closed the event by announcing that the 2027 event will be renamed to Engineering & Production Days.

The name change reflects what was already visible throughout this year’s event: digital engineering is expanding beyond engineering process improvement into the broader enterprise systems, production workflows, and collaboration models required to move faster across the full product lifecycle.

Engineering and production are becoming more tightly connected. Product development decisions affect manufacturing readiness. Manufacturing feedback affects engineering priorities. Supplier collaboration affects speed and quality.  AI readiness increasingly depends on connected lifecycle context, including the semantic relationships and knowledge graph approaches that can help make that context usable. And digital thread strategy increasingly needs to serve engineering intent, production execution and quality inspection.

The 2027 name change reflects the direction the industry is already moving.

Looking ahead

Engineering Process Days 2026 was valuable because it reflected the real state of digital engineering. The event broadened the discussion beyond a simplistic vision of one system, one tool, or one transformation path. It showed the complexity manufacturers are actually navigating: more systems, more data, more stakeholders, more pressure to move faster, and more urgency around AI readiness. 

For Anark, the event reinforced a central belief: the next phase of digital engineering value will be enabled by model-based collaboration across the product lifecycle.

The digital thread only creates value when people can use it. AI becomes most valuable when it can reason over connected product context and human-in-the-loop interactions. And enterprise transformation only works when engineering, manufacturing, quality, suppliers, and business teams operate from a shared understanding of product intent.

EPD 2026 raised two deeper questions we will explore in follow-up posts:

What kind of digital thread foundation is required for AI-ready engineering?

And:

How should manufacturers collaborate when product truth lives across a distributed ecosystem of systems, suppliers, and people?

Those questions sit at the center of where digital engineering is going next.