Manufacturers have long discussed digital continuity, but most are still struggling with brittle workflows, data silos, and fragmented tools. A new generation of platforms is rewriting the rulebook, with edge collaboration, orchestration, and intelligence at its core.
For decades, manufacturers have invested in digital tools with the promise of seamless integration, spanning design, production, and inspection. But those promises remain elusive. Fragmentation persists. File formats remain incompatible. And even simple engineering changes can take days to ripple across siloed systems.
Stephen Graham, Executive Vice President and General Manager of Nexus at Hexagon Manufacturing Intelligence, sees this problem up close. “We have spent years watching engineers and operators try to force legacy systems into collaboration,” he says. “The truth is, the old model of disconnected point solutions stitched together with brute-force integration no longer serves a manufacturing world that moves at digital speed.”
This is not merely an IT headache. It is an operational liability. When CAM data does not reflect the latest design tolerances or inspection results are siloed from the simulation model, production slows down, errors creep in, and confidence erodes. Graham is clear about the scale of the challenge: “You cannot deliver a digital thread with fragmented architecture,” he explains. “You need a fundamentally different approach—one that assumes integration, collaboration and intelligence from the outset.”
Edge collaboration and the new flow of work
One of the most profound shifts in this new architecture is the move from centralised processing to edge-native collaboration. In traditional systems, decisions were deferred to a central server or cloud environment. But in modern manufacturing, latency is unacceptable, and context is everything.
“Bringing compute to the edge of the factory floor allows teams to make better decisions faster,” Graham adds. “It is not just about speed; it is about context. When you co-locate intelligence with execution, you reduce miscommunication, you shorten feedback loops, and you empower the people closest to the process.”
This philosophy underpins Hexagon’s Nexus platform, and especially its recent release, ESPRIT EDGE. But for Graham, the bigger picture is more important than any individual tool. “The edge and cloud are not competing architectures,” he adds. “They are complementary. The edge delivers responsiveness and specificity. The cloud provides scalability and orchestration. You need both.”
For many manufacturers, the obstacle to transformation is not the absence of advanced tools; it is the weight of existing systems. Legacy MES, PLCs, and even file-based CAM systems have grown rigid over time. Innovation stalls not because ideas are lacking but because the infrastructure cannot support them.
Graham believes the answer is not a rip-and-replace approach. “You cannot tear everything down and start again,” he continues. “What you can do is create a connective layer, a platform that orchestrates intelligence between your existing systems. We think about it as composability. You expose functionality as services. You let workflows evolve organically.” This composability represents a significant departure from traditional monolithic software stacks. Instead of siloed, vertically integrated applications, platforms like Nexus aim to offer modular services that can be reused, recomposed, and reorchestrated, whether that is geometry manipulation, tolerance management, or toolpath simulation.
“What really drives innovation,” Graham says, “is not more software; it is better orchestration. When systems talk to each other natively, engineers stop wasting time translating data and start using it to make better decisions.”
The case for openness and the complexity behind it
In theory, every vendor claims to support openness. But in practice, building an open platform that works across partners, users, and even competitors is exceptionally difficult. It requires not just APIs but governance. Not just interoperability, but trust. “We do not believe openness is just about file compatibility,” Graham says. “It is about creating shared frameworks for collaboration across teams, across tools, and across companies. That takes deliberate engineering and constant curation.”
This is especially important in manufacturing ecosystems, where partners and suppliers must collaborate without surrendering intellectual property or compromising security. “The biggest challenge in open platforms is not technical; it is trust,” Graham adds. “You have to give users confidence that when they plug into your system, they will not lose control, fidelity or performance.”
One of the great ironies of digital transformation is that it often produces more data fragmentation, not less. Every machine, tool and software system generates data, but much of it remains trapped. Centralising all of it into a single source of truth is not only unrealistic but also counterproductive.
Graham advocates a federated model instead. “Federation means accessing data where it lives, whether that is in the cloud, on the shop floor, or in a supplier’s database,” he explains. “You do not need to move the data. You need to make it accessible, contextual and connected.”
This approach also enables more meaningful use of artificial intelligence. “AI needs structure, context and scale,” Graham says. “A federated architecture gives you that. It allows you to embed intelligence directly into the workflow, so the system learns, adapts and improves without breaking continuity.” This shift is fundamental to unlocking the next wave of manufacturing performance, not through automation alone, but through embedded intelligence that augments decision-making at every level.
The changing role of the engineer
This wave of platform-enabled intelligence has implications not just for systems but for people. In particular, it is reshaping what it means to be an engineer in a modern manufacturing environment. “Too often, engineers spend more time debugging files and translating between systems than actually solving problems,” Graham explains. “When you remove those frictions, you elevate the role of engineering. It becomes about creativity, orchestration and continuous improvement.”
By encoding domain knowledge into modular workflows, organisations can also retain and scale best practices. “Expertise becomes embedded, reusable, and accessible,” Graham adds. “That is powerful. It means you can onboard new talent faster, preserve institutional knowledge, and continuously refine your processes.”
For many years, automation has been the benchmark of digital transformation. But Graham believes the industry is entering a new phase, where the goal is not just to automate but to enable autonomous decision-making. “Autonomy is not about removing people; it is about giving systems the ability to sense, learn and adapt,” he says. “When your workflows are instrumented and intelligent, you can do things like self-adjust toolpaths based on real-time inspection or automatically reroute jobs based on resource availability.”
This kind of dynamic responsiveness is only possible when platforms can coordinate across disciplines in real time. “You need a unified fabric where design, simulation, production and inspection are connected by default,” Graham explains. “That is when your factory stops being a series of isolated steps and becomes an adaptive system.”
The ecosystem challenge
As compelling as this vision is, it comes with a paradox. Manufacturers want openness, but they also want reliability. They want modularity, but they do not wish to become systems integrators. Graham is candid about this tension. “There is no perfect solution,” he says. “What matters is how you balance it. You need to curate the ecosystem. You must make deliberate choices about what you build, what you integrate, and what you leave open to partners.”
It is a constant negotiation between flexibility and control, innovation and governance. But for Graham, the prize is worth the complexity. “A platform that enables co-creation, not just consumption, becomes a force multiplier. It lets every player in the ecosystem contribute to continuous improvement.”
Ultimately, the goal is not simply digitisation. It is transformation. It is the creation of factories that are not just connected but adaptive, able to flex with market demand, absorb supply chain shocks, and evolve with new materials, processes and products. “A truly intelligent factory is not one that just runs faster,” says Graham. “It understands its own context, anticipates change, and improves over time. That is the shift from execution to cognition.”
This kind of agility is no longer optional. In a world of volatile demand and fractured supply chains, manufacturers must be able to respond to uncertainty rather than merely absorb it. And that demands infrastructure that is not only digital but dynamic.
For organisations still stuck in proof-of-concept purgatory, Graham has a clear message. “Do not start with tools. Start with workflows. Understand where the friction is, where the knowledge gaps are, and how your teams collaborate,” he advises. “Digital transformation is not a technology project. It is a cultural reset. You need to design for speed, learning and resilience, not just compliance.”
This means asking hard questions about architecture, incentives, and mindset. But it also means embracing a new model, one where intelligence is not an add-on but an organising principle. As Graham concludes, “The winners in the next phase of manufacturing will not be those with the most technology. They will be the ones that can orchestrate the fastest because their systems, their people and their data are all pulling in the same direction.”
