Making the value of technology clear
[post_title] => How Operations Software Can Drive Financial Results and Improve Plantwide Visibility
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What is keeping manufacturing operations from leveraging all of their rich data to optimize and use AI effectively? Often, it’s the lack of a structured, coherent, well-managed way to ensure everyone can leverage all the right data at the right time. This Industrial Intelligence Platform Buyer’s Guide discusses what to consider and the many facets of such a system to support operational excellence.
Please enjoy a summary of our findings below.* For the full research, please visit our sponsor, Velotic.
Considerations in Buying
We have identified six major categories of issues crucial to making a sound investment decision for an Industrial Intelligence Platform. Beyond the software's functionality and technology as part of the infrastructure, the provider also matters. Choosing the best-fit platform can determine time to value, the agility of intelligence to match operations, long-term costs, scalability, and operational adoption.
Functionality
Capabilities for ingesting, harmonizing, managing, contextualizing, analyzing, and creating applications with a wide range of industrial data are fundamental. Many functions are needed to leverage diverse, fast-moving, and granular industrial data at the performance levels needed to support manufacturing, maintenance, or service operations.
Technology
The platform must be open to connect with diverse data sources, yet be fully protected by cybersecurity measures. Industrial settings also require high performance to ensure timely alerts and action while operations are in progress. Industrial Intelligence Platforms must also be able to orchestrate not only data streams, workflows, and UI mashups, but also AI agents.
Industrial Infrastructure
This platform needs to blend into the data infrastructure over the long term. This includes many hosting options, a robust edge, and intelligence for every employee as needed. Ideally, it streamlines licensing by including AI in the platform.
AI Capabilities
An Industrial Intelligence Platform must appropriately incorporate and manage AI. Per the definition, it is added into the analysis capabilities to improve the ability to convert data into actionable insights and intelligence. Agentic AI assistants for specific domains, such as quality or service, can improve the value of software developed.
Provider & Service
The company providing the Industrial Intelligence Platform is ideally ready to support your company in many ways over the long haul. They need to have deep experience in industrial settings and, ideally, have customers with use cases similar to those your company has in manufacturing and service operations. An ecosystem of partners can also be useful.
Enterprise Considerations
Industrial Intelligence Platforms come in many forms, and not all are ready to scale and deliver the reliable performance larger companies need to standardize across their manufacturing and/or service operations. Scalability, reuse, and proven applicability in all of the areas your company wants to orchestrate now and in the future are crucial.
How can better information, automation, and AI improve design engineering decisions?
Design engineers face growing pressure to bring products to market faster, control costs, improve quality, and
manage increasing product complexity. Yet important information that impacts decisions may be difficult to obtain, arri
ve too late, or require time-consuming manual work.
At the same time, AI is creating new possibilities for product design and engineering, but engineers need accurate, actionable, and trustworthy guidance for it to be useful.
Tech-Clarity is conducting research to understand how manufacturers support design engineering decisions today and what engineers need from future AI-enabled applications. We are exploring questions such as:
If you work in product design, engineering, product development, manufacturing engineering, or manage these teams, we invite you to participate and share your perspective. As a thank you, you’ll receive a complimentary copy of the research report summarizing the findings.
Qualified respondents will also be entered into a drawing for one of twenty $25 Amazon gift cards.*`
Individual responses will be kept confidential and will only be reported in aggregate. Please feel free to share this survey with colleagues who have experience in product development or manufacturing.
Thank you for your support. Pease check out our Active Research page for additional Tech-Clarity research opportunities.
*See survey for eligibility requirements.
[post_title] => Design Engineering and AI Survey: What Do Engineers Need?
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[post_modified] => 2026-09-24 02:18:27
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[post_date] => 2026-09-22 08:56:22
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What does it take for manufacturers to achieve a competitive advantage in new product development? Our recent survey identified product innovation, product quality, and new product development (NPD) speed as the three most important drivers of that advantage. How do companies overcome the operational challenges standing in the way of improving all three?
Please enjoy an overview of our findings below.* For the full research, please visit our sponsor, SOLIDWORKS.
Late Design Changes
It’s harder to achieve a competitive edge when engineers face inefficiencies. Well over half of respondents cite late-stage design changes as a reason for missing product development dates. Late changes are often a symptom of broader collaboration issues not only across design and engineering teams, but also between engineering and manufacturing, where misalignment and limited visibility lead to unnecessary rework and downstream disruption.
Wasting Time on Non-Value-Added Tasks
Close to half of the respondents report that wasting time on non-value-added tasks contributes to delays. Time spent on these activities takes time away from design work and meeting delivery dates.
Working with Outdated Information
Over 40% point to outdated information and rework from incorrect BOMs as other major sources of delay. Engineers then need to spend time identifying the source of the errors and making design changes to address them. An inaccurate BOM can also drive poor design decisions and quality issues, problems that often surface only in manufacturing, causing even greater delays.
Waiting on Suppliers
Waiting for supplier data is another cause of delay, cited by 41% of respondents. Engineers need supplier data to complete designs and testing, and when engineers must translate data between CAD systems, the extra step adds time and can introduce quality issues that further slow development.
Validating Regulatory Compliance
Close to a third lose valuable time validating compliance, likely due to hunting for documentation or piecing together information across multiple systems. Without easy access to traceable design data, meeting regulatory requirements takes longer, delaying launches and missing market opportunities.
Missed Launch Dates
Product development challenges directly impact business performance. More than half of respondents miss launch dates, creating a ripple effect across the business. Sales slip, companies miss market windows, and competitors can launch first and capture market share. And when design teams stay on a project longer than expected, the next projects are delayed too.
Higher Development Costs
Close to half of the respondents say these challenges drive higher development costs, and over 40% see higher product costs. That pressures profitability and competitiveness. These challenges affect not only the business but also individual engineers. When costs run higher than expected, less money is available for engineering tools, additional hires, wage increases, and other employee benefits.
Less Innovation
The impact extends beyond schedules and costs. Nearly half of manufacturers report having less time to iterate and innovate. This is a sign that teams are consumed by rework, issue resolution, and non-value-added work. Less time for design iterations means less time for product optimization and performance improvements. Ultimately, when innovation suffers, so does revenue growth, competitiveness, and long-term resilience in demanding markets.
Lower Product Quality
At the same time, more than 40% of respondents say these challenges lead to product quality issues, which carry multiple consequences. Customer satisfaction and loyalty may decline, opening the door to competitors. Warranty claims and returns drive up costs, especially if quality issues lead to major recalls. In regulated industries, poor quality can mean greater regulatory scrutiny, fines or penalties, delayed approvals, and increased liability and risk.
Individually or together, these findings show that product development challenges are not isolated operational issues; they directly affect business performance, including revenue, profitability, and competitiveness.
Better Processes
The survey results show PLM delivers improvements that closely align with the challenges manufacturers identified, evidence that PLM can play a significant role in addressing them and improving business performance.
Higher Productivity
A large majority report improvements in the process areas that drive engineering productivity: more current and accurate product data (61%), accurate BOMs (58%), faster engineering change cycles (51%), less design rework (47%), and less time spent on non-value-added tasks (43%). With PLM in place, engineers spend less time searching for information, reworking incorrect or outdated data, and facing late engineering changes. With less time lost to non-value-added tasks, they can run more design iterations, innovate more, and better meet quality targets.
Efficient Manufacturing Handoffs
Half of respondents cite a more efficient design-to-manufacturing handoff as a benefit of PLM. When engineering and manufacturing work together earlier and share accurate product information, issues surface sooner, reducing costly late-stage changes.
Better Data Security
More than a third of respondents cite better protection of product data and intellectual property as a key benefit of PLM. When engineers have common and secure ways to share information, they collaborate more readily with team members and suppliers, leading to better ideas and designs. Stronger security also reduces business risk by protecting valuable IP from unauthorized access or loss.
PLM Enhances Data Management Value
Collectively, the findings show that PLM’s value goes beyond better data management. Its operational improvements address engineering challenges such as late design changes, data inconsistencies, poor collaboration, and program delays. By creating a more structured, connected product development environment, PLM helps organizations improve operational efficiency.
How far have manufacturers progressed in connecting the digital thread across engineering and manufacturing? Earlier this year we surveyed over 200 manufacturers and found that they are pursuing PLM-MES integration for two primary reasons; improving time-to-market and product quality. The research showed other goals as well, including supporting AI initiatives. But the data also uncovered a wide variety of PLM-MES integration maturity and showed that most manufacturers, even larger more advanced ones, have room for improvement in integrating their digital threads.
Recently, we had the opportunity to sit down with two experienced Kalypso consultants to discuss the results and learn from their experience. In the webinar, Jim Brown shares his research and leads a discussion with Kalypso’s Howard Schmillor and Cameron Carr.
Watch the webinar replay now or visit the related Kalypso research page for the webinar, Tech-Clarity report, and more information about PLM-MES integration.
[post_title] => PLM-MES Integration: Connecting the Digital Thread
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[post_date] => 2026-09-16 09:48:58
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Engineering data is valuable across the enterprise, but not everybody gets the information they need. We researched the need for 3D engineering data and the applications that create, access, and share it. We found the need is growing and investigated how companies are filling it. It’s not just the typical build-or-buy scenario.
Please enjoy the summary below. For the full research, please visit Tech Soft 3D (registration required).
Designing in 3D has transformed product development, unlocking new value by supporting design and simulation techniques that reflect the real world. But the value of 3D data goes beyond making engineering decisions. It’s become the language to share and collaborate on product designs across the value chain. 3D engineering data has become a strategic business asset, powering downstream functions like manufacturing and service and serving as the backbone for high-value digital twins and AI initiatives.
Demand for 3D is Expanding
We recently interviewed 15 large manufacturing companies to better understand their need for applications that leverage 3D engineering data, the challenges they face, and how they source these solutions. The participants are quoted throughout this research.
More than two-thirds of respondents said the need to access and leverage 3D data across their company is growing, and the rest said it has stayed the same. Not a single company said that the need for 3D data had decreased.
Why is demand increasing? Respondents shared the following reasons:
The need for applications that create, access, and share 3D data is expanding. It’s important to recognize that not every need will be met with commercially available solutions. To meet their needs effectively, companies must be open to both building and buying solutions as needed. Companies must consider speed and cost when they choose when to build and when to buy, of course. But they shouldn’t forget the often-overlooked cost of scaling. Further, they must be careful with needs that could reveal IP or sensitive data. Lastly, companies should recognize that AI may make building more accessible and more attractive, which may lead to a change in the current COTS-heavy balance of when to build and when to buy.
Don’t Start from Scratch
The expertise required to accurately interpret and exchange 3D engineering data is specialized, and resources are scarce. Engineering data, including 3D CAD and simulation data, is complex. Whether building or buying, it’s important not to start from scratch and reinvent the wheel.
When building solutions, companies should look for the opportunity to leverage components, SDKs, and building blocks. They should favor solutions that incorporate an understanding of how CAD works, including the complex mathematical representations behind the geometry and the rich metadata in the files. With the rise of AI vibe coding, less experienced developers may be looking for ways to access and share 3D data as well. These components may make this feasible. It may also make it feasible for them to create 3D data, but that is more complicated and less likely in the near term, although everything with AI is changing rapidly.
Choose a Strategic Partner
Look for trusted suppliers for both build and buy approaches. Look for proven, scalable apps with APIs to access multiformat CAD and simulation data. If creating 3D data, be careful to create the data as the designer or software provider would in order to ensure data quality.
Be Prepared to Change
Lastly, engineering and IT are always changing, and AI is accelerating the process. Recognize that the current process for deciding when to build and when to buy may need to evolve.
*This summary is an abbreviated version of the ebook and does not contain the full content. For the full research, please visit Tech Soft 3D (registration required).
If you have difficulty obtaining a copy of the research, please contact us.
[post_title] => Meeting the Demand for 3D Engineering Data
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[post_date] => 2026-09-15 10:42:02
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Where should A&D manufacturing companies focus their Model-Based Enterprise (MBE) efforts?
To achieve MBE goals, manufacturers must track parts and processes from design engineering through manufacturing to the sustainment of the finished product in the field. The challenge is not transferring information among the business, design, and manufacturing systems; it is the complexity of creating and managing the digital thread across a global supply chain, through multiple manufacturing environments, and over decades of field maintenance. This eBook explains what Model-Based Process Authoring is, why it is critically important, how it functions within an MBE environment, and the technology required to support it.
Please enjoy a summary of our research below.* For the full research, visit our sponsor iBase-t.
Tech-Clarity is pleased to announce that we are expanding our research team and coverage to include the full range of supply chain functions that sit under the Chief Supply Chain Officer including advanced planning and scheduling, visibility, risk, procurement, and order management. With an extensive background as a Gartner analyst, supply chain vendor executive, and practitioner, Amber Salley joins Tech-Clarity as Vice President of Research for Supply Chain. Amber brings 25 years of experience spanning supply chain planning, S&OP/IBP, inventory management, materials planning and service parts planning with a strong focus on connecting technology decisions to financial outcomes. Please visit Amber's Bio Page for more on her background.
Extending our coverage to supply chain comes at a strategic time. Planning, procurement, and execution decisions are converging as AI and agentic systems move from pilot projects into core decision-making, while continued disruption from tariffs, geopolitical risk, and shifting sourcing strategies keeps risk and resilience on the boardroom agenda. Software vendors are moving down-market, giving midsize organizations access to planning, visibility, risk, and procurement technology once reserved for the enterprise — but often without the internal expertise to evaluate it. Stakeholders, from the Chief Supply Chain Officer to the CFO to private equity operating partners, are recognizing the need for independent, vendor-neutral guidance that separates real capability from hype and ties supply chain investment to business value including cash, margin, and service outcomes. Leading vendors across planning, visibility, risk, and procurement are advancing their offerings to meet this moment, while new entrants continue to emerge to fill the gaps legacy suites leave behind. Supply chain is shifting away from siloed, function-by-function decisions toward integrated, outcome-driven strategy — and buyers need a trusted, independent source to help them navigate it.
“The time is right to extend our coverage to supply chain,” explains Jim Brown, President and Founder of Tech-Clarity. “Supply chain management decisions are increasingly interconnected to Tech-Clarity’s core coverage areas, and portfolio owners, operating executives, and PE operating partners alike are looking for independent guidance on the value of technology across the full supply chain. Amber is uniquely qualified to offer that perspective, with a rare combination of practitioner, consulting, analyst, and vendor-executive experience. We're excited about how her depth and credibility will help further our mission of making the business value of technology clear.”
"I'm excited to be joining Tech-Clarity because their independent, vendor-neutral approach to research is exactly the perspective supply chain leaders need right now,” shares Amber Salley. “Tech-Clarity's reputation in manufacturing and product development creates a natural extension into supply chain, and I'm looking forward to helping buyers cut through vendor noise and make decisions that actually move their business forward."
Amber's research focus will include supply chain planning (demand, supply, inventory, and S&OP/IBP), supply chain visibility, supply chain risk and resilience, procurement (including direct spend), and order management.
Please follow Tech-Clarity on LinkedIn and join our mailing list to read Amber’s research. For more information or to schedule a briefing please feel free to contact us.
[post_title] => Tech-Clarity Expands Coverage to Supply Chain with Veteran Analyst Amber Salley
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[post_date] => 2026-09-04 19:34:06
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Digital transformation in manufacturing is entering a new phase, as solution providers move beyond traditional PLM offerings toward AI-driven "digital twins" and connected data foundations that promise to make generative AI trustworthy for mission-critical engineering decisions. In this interview, Stephane Declee, CEO of the ENOVIA brand at Dassault Systèmes, discusses how the company is applying decades of industrial knowledge to its platform strategy. Centered on 3D UNIVERSES, the strategy gives manufacturers an industry-specific environment built for a future in which AI plays a central role in product development.
Howie Markson
Hi Stephane. Will you please introduce yourself and tell us a bit about Dassault Systèmes and ENOVIA?
Stephane Declee
I'm Stephane Declee, CEO of the ENOVIA brand at Dassault Systèmes. ENOVIA is the product lifecycle management brand within the 3DEXPERIENCE platform, the place where engineers, project managers, procurement teams, and business stakeholders come together to develop products from concept through production.
Dassault Systèmes has been working with manufacturers for more than 40 years. What's made us different isn't just the technology, it's that we build on deep industrial expertise. We don't just provide software. We bring scientific models, domain knowledge, and decades of real-world manufacturing experience to every engagement.
Today, we're focused on something bigger than traditional PLM. We're helping manufacturers build the foundation they need to operate in what we call the generative economy, where AI-driven design, simulation, and decision-making become practical, trusted tools rather than experimental ideas. That's the mission behind 3D UNIVERSES, and it's what gets me up in the morning.
Howie Markson
We've been hearing a lot about 3DEXPERIENCE and 3D UNIVERSES lately. Can you describe what 3D UNIVERSES actually is? If someone is running product development at an auto or aerospace manufacturer, what's in it for them?
Stephane Declee
3D UNIVERSES is a strategic direction, not a single product you buy off the shelf. It's how we're evolving the 3DEXPERIENCE platform to serve specific industries in a much more targeted, intelligent way.
For a manufacturer in the automotive or aerospace industry, it means three things in practice.
First, it gives you a living virtual twin of your product, one that connects engineering, manufacturing, operations, and supply chain in a single environment. You're not working with disconnected files or systems. You're working with one shared model of truth.
Second, it brings industrial AI into your product development workflows in a way that's grounded in your company's actual data, not a generic assistant trained on the public internet. The AI understands your context: your regulations, your design rules, your history.
Third, it helps your people work smarter. Project managers automate the scheduling. Engineers spend more time on design and less on administration. Teams across departments collaborate on the same platform, building and reusing knowledge with every project.
If you're a vehicle program director or a systems engineer at an aerospace OEM, 3D UNIVERSES is about closing the gap between where product development is today and where it needs to be.
Howie Markson
Most people in the industry refer to digital twins. But you use the term "Virtual Twin" instead. Is that your branding, or is there something fundamentally different between the two? What are manufacturers getting with a Virtual Twin that they're not getting elsewhere?
Stephane Declee
It's a fundamental difference, not a branding choice.
A digital twin gives you a snapshot. It captures the current state of a product or asset at a point in time. That's useful, but it's largely descriptive; it tells you what exists.
A virtual twin is a living model. It connects every dimension of your product, the geometry, materials, system behavior, manufacturing constraints, and operational data, inside a single platform. It doesn't just reflect reality; it simulates it. You can test a design change, predict how a system will perform under stress, or anticipate a production bottleneck before anything is physically built.
Here's a concrete example. A carmaker using a digital twin might capture a static picture of its production line. With a virtual twin on the 3DEXPERIENCE platform, that same manufacturer can simulate the entire production sequence, test different assembly scenarios, identify where bottlenecks will occur, and evaluate design changes all virtually, before committing a single resource.
The other critical distinction is continuous learning. Every simulation, every design iteration, every change feeds back into the virtual twin. The model gets smarter with each cycle. That's not something a digital twin does. It's static by design. The virtual twin is dynamic by design.
For auto and aerospace manufacturers working on complex, long-cycle programs, that difference matters enormously.
Howie Markson
Our research on The State of Product Development found that Top Performers are embracing AI in product development. How are you thinking about AI with 3D UNIVERSES? What problems are you solving with it?
Stephane Declee
I want to be honest here, because I think it's important. When I look at what's actually deployed at manufacturers today, including large OEMs in automotive, aerospace, and industrial machinery, the real maturity level for AI in product development is still early. There are pilots, there are experiments, but very few companies have put industrial AI to work at scale. The momentum is real, but the realization isn't there yet for most.
That's not a criticism. It's a recognition that the prerequisites are hard.
AI, in a general sense, including natural language interfaces, automation of repetitive tasks, is accessible today. But industrial AI, the kind that actually drives product development decisions, requires something more. It requires trusted data. It requires a model-based, data-driven foundation where the AI understands the engineering context, the regulatory environment, and the company's own rules.
What we're building with 3D UNIVERSES is exactly that foundation. We're not offering a generic chatbot layered on top of your PLM system. We're enabling generative experiences, AI that operates within your specific industrial context, that can run design scenarios, propose configurations, assess project health, and flag compliance risks with results you can actually act on and trace.
The word I keep coming back to is trustable. Industrial AI at scale has to be trustworthy. That means knowing under what conditions a result was generated, what data fed it, and whether the regulatory environment it was trained on is still current. Without that, you don't have industrial AI. You have a very capable tool that you can't rely on when the stakes are high.
Howie Markson
Companies have product data everywhere, in CAD files here, PLM data there, and purchasing data somewhere else. How do you make AI work when all that data is speaking different languages? How does 3D UNIVERSES pull that together?
Stephane Declee
This is exactly the right question to ask, because it's where most AI initiatives hit a wall.
The first step isn't technology, it's ontology. You need a structured model that recognizes your data across systems, aligns definitions, and establishes that a component record in one system is the same entity as a reference in another. Without that, the AI is working with noise.
What 3D UNIVERSES enables is the creation of that data ontology across your enterprise without forcing you to move all your data into a single repository. You're not doing a massive migration. You're building a semantic layer that connects your existing assets in an organized, structured way, so AI engines can work across them reliably.
But the real power comes from what you do next. Once your real-world data is properly connected and contextualized, you can combine it with synthetic data generated through simulation, running virtual scenarios that create new data points your physical world hasn't produced yet. That combination of real and simulated data, projected onto your product and process models, is what makes generative experiences possible.
A designer or engineer isn't just querying data. They're running scenarios: what happens if I change this material? What's the impact on weight, cost, and manufacturing leadtime? The model gives you an answer grounded in your company's actual knowledge and history, not a generic industry average.
That's the difference between AI as a search tool and AI as a genuine engineering partner.
Howie Markson
You've introduced this concept called IPLM, Intellectual Property Lifecycle Management. Is this replacing PLM, or is this additive to PLM? What's the main difference for someone managing products day-to-day?
Stephane Declee
IPLM doesn't replace PLM. It extends it in a direction that PLM was never designed to handle.
Traditional PLM manages a product from conception through to end of life. It tracks what was built, how it changed, and who approved what. That remains essential. It's not going away.
But here's what PLM doesn't manage: the intellectual property, including the design rules, the engineering know-how, the regulatory constraints, and the simulation models used to create that product, which can be reused, recombined, and evolved to create the next one.
With generative AI now capable of producing design configurations, generating product structures, or proposing process changes based on a set of rules, you need a system that manages the lifecycle of those rules and that knowledge, just as rigorously as PLM manages a physical product. That's IPLM.
A practical example: your AI system generates a structural component design based on current aerospace regulations and your internal stress-testing standards. Six months later, a regulation changes. Without IPLM, you have no reliable way to identify which AI-generated designs are now out of compliance or to re-run those generative experiences with the updated rules.
With IPLM, you can trace every generative output back to the IP that produced it, manage the lifecycle of that IP as regulations evolve, and ensure that the knowledge your teams build today remains a trusted, compounding asset and not an unchecked output you can't stand behind.
Howie Markson
Change management, BOM management, configuration management, and release processes are core aspects of PLM. What changes occur when manufacturers adopt 3D UNIVERSES? Are they doing these tasks differently, or are they just being integrated into a new system?
Stephane Declee
They're doing these tasks differently, and that distinction matters.
The 3DEXPERIENCE platform already provides robust capabilities for change, BOM, configuration, and release management. What 3D UNIVERSES adds is a new way of working within those processes: one that combines platform-level collaboration, knowledge reuse, and AI assistance to make each step faster and more intelligent.
Take change management. Today, a change engineer identifies an issue, manually assesses impact, notifies the right people, and works through an approval process that often spans disconnected systems. With 3D UNIVERSES, the same change process is embedded in a collaborative platform where all stakeholders are already working. AI assistance can suggest the right people to include, identify downstream impacts on the BOM or schedule, and help initiate the change workflow automatically.
The same principle applies to configuration management and release. The AI understands the context of your product, its virtual twin, its project status, and its dependencies, so it doesn't just automate low-value steps. It brings the right knowledge to the surface at the right moment.
What manufacturers are really getting is a platform where the collective knowledge of every project, every change, and every release decision compounds over time. Teams don't just execute processes in a new system. They work smarter because the system learns alongside them.
Howie Markson
Our soon-to-be-released research shows manufacturers miss project launch dates 45% of the time, and our previous research, How to Reduce Non-Value-Added Work in Engineering, shows that only 54% of an engineer's time is spent on innovation and actual design work. What are you doing in 3D UNIVERSES that gets engineers back to doing what they love?
Stephane Declee
The numbers are consistent with what we see across our customer base, and they point to the same root cause: too much time spent on coordination, administration, and hunting for information and not enough time on the work that actually creates value.
3D UNIVERSES is built on seven foundations. Without going into all of them, I want to focus on three that directly address productivity for engineers and project teams.
The first is Virtual Companions. These are AI assistants embedded in the platform with full context of your product, your project, and your company's knowledge. An engineer doesn't need to search across systems to find a previous design or a simulation result. They ask. The companion understands the context of the virtual twin, surfaces the relevant IP, and can suggest a starting point rooted in what your organization already knows. The engineer takes over from there, refining, shaping, and applying their creative judgment. That's a very different experience from starting from scratch or copying from an old document.
The second is Generative Experiences. These are structured AI-driven workflows that guide a user through a sequence of intelligent steps. We built an example around project health assessment: today, a project manager copies comments and status updates into an external LLM, runs a sentiment analysis, extracts risks manually, and then creates the risk entries in the PLM system. With 3D UNIVERSES, that entire sequence runs inside the platform in seconds. The companion identifies the risks, proposes the next action, and waits for the project manager to confirm.
The third is Knowledge and Know-How. Every simulation, every design decision, and every change is captured and made available as reusable knowledge. A new engineer on a program doesn't start with a blank page. They start with the accumulated expertise of everyone who worked on that product before them.
Howie Markson
One thing we hear about 3D UNIVERSES is using virtual twins for products. But what about the organization itself? Can a manufacturer apply these same concepts to optimize its operations?
Stephane Declee
Absolutely, and this is one of the most underappreciated parts of 3D UNIVERSES.
The virtual twin concept doesn't stop at the product. You can apply exactly the same modeling and simulation approach to the organization itself, including its processes, its methods, its workflows, and its decision-making rituals.
Think about what that means. In traditional operations improvement, you analyze a process, identify inefficiencies, implement a change, and then wait to see the results. With a virtual twin of the organization, you can simulate a process change before you make it. You run the scenarios virtually, identify where the bottlenecks will move, test the new workflow, and then deploy with confidence.
For a manufacturer, this could mean modeling the entire product development process from requirements gathering through engineering, validation, and production launch inside the platform. Every project then runs against that virtual model, learns from what worked and what didn't, and improves the template for the next program.
It also means that the knowledge your organization builds, such as the way your teams work, the processes that consistently deliver on time, and the practices that don't, becomes a structured, reusable asset rather than something that lives in someone's head or in a slide deck. That's a meaningful competitive advantage, especially as experienced engineers retire and new talent needs to get up to speed quickly.
Howie Markson
Can you give me a real example? A customer who's using 3D UNIVERSES and seeing results, can you tell us what they were struggling with, and what changed?
Stephane Declee
Automation Express is a great example, and it's one I find myself returning to often because it shows the full picture.
They're a small industrial machinery company. They develop around 10 custom machines per year, fully engineered to order, meaning every machine starts with a client conversation rather than a standard catalog. That's a complex, resource-intensive model for a small team.
Their challenge was scaling. Every new project required significant engineering effort just to define scope, configure requirements, and align sales with production. They didn't have the bandwidth to grow without either hiring aggressively or fundamentally changing how they work.
What they built with 3D UNIVERSES was a virtual twin of their organization. Every time a new project begins, they don't start from a blank slate. They replicate a project template, including processes, documents, and requirements structures, that captures everything they've learned from every previous machine. Engineering and sales collaborate from day one on the same platform, which dramatically accelerated their time to market and reduced costly misalignments between what was sold and what was manufacturable.
Over time, they also built a rich virtual twin of their products, including all the documentation, supplier data, and maintenance knowledge around each machine. Now they're preparing to deliver that virtual twin to their customers alongside the physical product. It's a new value stream they didn't have before.
The bonus is what comes next: they're moving toward configure-to-order, using 3D tools that let their sales team configure a machine in front of a client in 3D, potentially in augmented reality on the factory floor, without requiring engineering resources for every configuration. That's how a small company scales without losing its engineering-to-order identity.
Howie Markson
What do you think people misunderstand about 3D UNIVERSES? What's the biggest misconception you hear?
Stephane Declee
The biggest misconception is that 3D UNIVERSES is a product, something you buy, deploy, and switch on. It isn't.
3D UNIVERSES is a strategic direction. It's how Dassault Systèmes is evolving the 3DEXPERIENCE platform to serve specific industries such as automotive, aerospace, industrial machinery, and others, with the depth of knowledge, AI capability, and integration that those industries need to compete in the next decade.
The second misconception, which follows from the first, is that it's all-or-nothing. Manufacturers sometimes hear about virtual twins, industrial AI, generative experiences, and IPLM in the same conversation and conclude that this must be a massive, multi-year transformation before they see any value. That's not how it works.
You start where your pain is greatest. If your engineers are spending too much time on administrative work, you start with virtual companions. If your project teams are missing launch dates, you start with AI-assisted project management. If you're struggling to scale your engineering-to-order model, you start with configure-to-order tooling.
Each capability builds on the same platform foundation, so the investments compound. The knowledge you capture in one project informs the next. The data ontology you build for one process extends to others. You're not doing a big bang replacement of everything you have. You're adding real, practical capabilities in the areas where they deliver the fastest return, and you're building toward something much more powerful over time.
That's the approach, and it's grounded in 40 years of working with the world's most demanding manufacturers.
Thank you, Stephane, for the interview. At its core, 3D UNIVERSES rests on a simple premise. Manufacturers won't get trustworthy AI in product development until their engineering, manufacturing, and operations data are on a single, connected, continuously updated model rather than scattered across systems. Building a shared ontology and grounding AI in a company's own contextual data, rather than generic training data, is a good starting point. The small manufacturer example is an early proof point, showing real gains in speed and alignment from a shared platform.
Thank you, Kyle Herring, for arranging the interview. It was an informative discussion that should interest manufacturers looking at PLM solution providers and their AI strategies.
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Amber Salley is the Vice President of Research for Supply Chain at Tech-Clarity, covering supply chain management, including advanced planning and scheduling, S&OP/IBP, supply chain visibility, risk and resilience, procurement, and order management.
Amber has over 25 years of experience in supply chain. She has worked as a practitioner with the IBM Integrated Supply Chain, a consultant at Booz & Company (now Strategy&, part of PWC) and Accenture, an analyst at Gartner, and an executive at a supply chain software company — a combination that gives her a unique perspective on the industry and shapes how she approaches it. Along the way, she's covered a broad range of industries, giving her a wide-angle view of how supply chains actually operate. She has a degree in Information Technology and Supply Chain Management from Duquesne University and an MBA from the University of Chicago Booth School of Business.
Amber's current areas of research include how AI-native and agentic technologies are built into supply chain platforms versus bolted on; the convergence of supply chain planning and execution; and how organizations create business value and build resiliency by treating risk and uncertainty as a structural part of supply chain management, rather than an afterthought. Her take: real value comes from higher quality decisions, not a shinier platform.
Outside of work, Amber is a lifetime member of Girl Scouts and an adult volunteer with her kids' Scouting America troop. She also plays on a Chicago trivia team called "The Team That Shall Not Be Named," is an avid reader with a particular soft spot for pop culture and celebrity news, and loves a family movie night.
Does it feel like your investments in machines, automation, and production systems are not delivering their full potential? You can’t make high-volume manufacturing simpler. Yet you can shorten the time between plant activity, actionable insights, and measurable business value. In short, you can maximize your tech spend.
Listen in as an industry analyst and operational experts share a down-to-earth fireside chat on how better production intelligence data can improve operational visibility and deliver real business value. No matter your current digital maturity, you will walk away with strategies to optimize your industrial data.
What if the tablets and scanners supporting your operations never went down during a shift? How much would you save in spares, employee time, and data reliability? DeepCharge has developed an AI-powered platform designed to bring proactive and predictive maintenance to mobile device fleets. They claim it can begin producing device intelligence soon after deployment and becomes more precise as it learns site-specific operating patterns.
Multiple Digital Twins
Value Equation
Product development and manufacturing are evolving rapidly. Organizations are facing increasing product complexity, workforce challenges, rising customer expectations, and growing pressure to improve speed, quality, and innovation. At the same time, emerging technologies such as AI are creating new opportunities, and new questions.
Tech-Clarity is conducting research to better understand the current practices, challenges, and future of product development and manufacturing. We are exploring questions such as:
Qualified respondents will also be entered into a drawing for one of twenty $25 Amazon gift cards.*`
Individual responses will be kept confidential and will only be reported in aggregate. Please feel free to share this survey with colleagues who have experience in product development or manufacturing.
Thank you for your support. Pease check out our Active Research page for additional Tech-Clarity research opportunities.
*See survey for eligibility requirements.
[post_title] => The Future of Product Development and Manufacturing Survey
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Could deep, proven planning, scheduling, and execution deliver higher value if they acted as a closed-loop, real-time system? OK, that’s an obvious yes, particularly for complex discrete industries. While it’s not common in the market, it is the driving vision for Eyelit Technologies (Eyelit). They are leveraging both longstanding Eyelit integration capabilities and more recently developed agentic AI to achieve this. We learned more during a recent update briefing with executives from the four companies that now make up Eyelit.
Media, PA, USA, July 15, 2026 – Results are streaming in: interest in Manufacturing Operations Software (MOS) is very high. Tech-Clarity, Inc. has reached the maximum of six sponsors for the 2026 research program on The Business Value and Evolution of Manufacturing Operations Software in the Age of AI, as Siemens Digital Industries Software is also sponsoring. The other five sponsors are: Critical Manufacturing, Infor, ISE, Parsec Automation LLC, and SAS, demonstrating broad industry support and momentum.
The survey is open for response until July 26, click here to share your perspective.
Responses are coming in from manufacturers across process, batch, discrete, and hybrid production segments and regions worldwide.
Share your experience now. Individual responses are kept confidential. As a thank-you for participating, Tech-Clarity will send the report to all who respond.
This research initiative focuses on a few key topics that are pivotal for manufacturers’ success:
We have been impressed with Makersite and their focus on Product Lifecycle Intelligence (PLI), as we shared in our earlier briefing insight. Makersite is a small company that has big customers, and they’re helping those companies make significant gains on compliance, cost, sustainability, and supply chain risk. Their #PLI approach connects product data across design, supply chain, compliance, cost, and sustainability to create digital models that help manufacturers make better product decisions throughout the lifecycle. It’s a valuable offering to help companies get the data they need for designers to make better, more holistic decisions around components. The company offers more than applications, they provide data from a variety of sources – both internal and external - including over 150 Makersite gathered database.
Now, Makersite focuses on helping manufacturers “source smarter, design greener, and collaborate faster” by leveraging their AI-powered PLI for sustainable product and supply chain decisions at scale. There’s a lot to that, so let’s unpack what it means. Makersite had already gone beyond focusing solely on carbon content to help manufacturers design for sustainability. This is critical to long-term business success, as show in our Executive Strategies for Long-Term Business Success research series. Makersite is continuing down this path and extending their scope, leveraging their structured product model (aka digital twin) capabilities to collect and associate data with other critical impacts of design decisions including cost. Recently, they also announced they have acquired SiGREEN from Siemens Digital Industries Software and rebranded the platform as Mattermaps, strengthening their product data collection and exchange capabilities.
The big news is that Makersite is adding a “should cost” capability. It’s a new feature to evaluate costs at the portfolio level based on the digital twin, for example accounting for which components are going up in price. The solution is intended to help with scenario and what-if planning to help optimize decisions amid market disruption and uncertainty, which our study on long-term success show has been growing steadily over the last 5 years.
The solution complements others on the market because it more focused on purchased components, not for custom components that have to be evaluated at the 3D / CAD level detail. This makes the Makersite should cost approach complementary to feature-centric costing capability covered by other companies that address Product Cost Management (#PCM), such as aPriori or Facton. The Makersite methodology takes advantage of their strength in understanding the supply chain, and focuses across the product portfolio as opposed to a particular part or part family. We see the value of these solutions as additive, acting on different kinds of parts and at different levels of granularity. Makersite has added a very valuable capability to help manufacturers make better decisions that directly impact competitiveness and profitability.
In addition to the should cost, and supporting it, are now agentic AI capabilities. AI is not new to Makersite. AI has been an important part of their solution for some time. As we said before, it’s important to understand the role artificial intelligence (#AI) and machine learning (#ML) play in Makersite’s ability to create a contextually rich digital thread mapping. Makersite refers to their platform capabilities as “AI powered Product Lifecycle Intelligence.” These capabilities were more embedded in the solution and behind the scenes. Now that they are expanding the use of AI Agents. The strength leverages their structured product model and offers mapping agents, modeling agents, and reporting agents. Makersite explains that the results, unlike some AI solutions, are grounded in data and traceable. The approach leverages AI but keeps a human in the loop for verification. It’s a practical approach that leverages their past AI experience to improve the value they deliver.
One example of how the agents can add value is through a new offering, ChemAI. We were able to see a demo of how it works. ChemAI generates a product model for a chemical when suitable chemical data is limited or can’t be found in existing datasets. It uses available chemical information and AI to infer likely synthesis pathways and raw materials. This provides a foundation to determine sustainability, cost, and compliance where rich chemical datasets are unavailable. Then, the user can review and validate the results, and select the most appropriate synthesis pathway for that chemical. From my understanding, ChemAI helps fill chemical data gaps by inferring a recipe, raw materials, and associated impacts (see demo image).
Makersite ensures they are not just turning to AI and trusting answers. The solution creates a model by working upstream and connecting with existing data where available. It generates a variety of suggestions for how it was produced for the user to pick from. As a byproduct, the process develops a validated model that can be reused for future scenarios.
ChemAI is just one example of Maketsite’s plans, there will be others released, with plastics and metals mentioned as applications we could expect them to deliver at some point in the near future.
One of the things that has always impressed us is the Makersite customer list. They shared two published case studies during the briefing, each showing impressive results, including:
Makersite is expanding on already strong capabilities in their PLI offering. The two case studies reflect Makersite’s strong capabilities in sustainability, and we look forward to seeing more success stories around cost and supply chain risk as they expand their offering and use of AI. We are excited to following their continued success.
Thank you Nicolás Artímez Wetz and Kerrie Kennedy for your time and for sharing your solution plans and customer successes.
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Don’t miss this recorded event replay on achieving product data-driven digital maturity in the age of AI!
Digital maturity is no longer just about adopting new tools. For design and manufacturing organizations, it is about building the strategy, culture, and connected data foundation needed to make better decisions across the product lifecycle — and to prepare for the next wave of AI-enabled transformation.
Watch Jim Brown, President of Tech-Clarity, and Bassanio Peters, Senior Market Development Manager of Design & Manufacturing at Autodesk, on a LinkedIn Live conversation on what it takes to become a more data-driven organization. They discuss how manufacturers can evaluate where they are today, identify the organizational and process gaps holding them back, and create a practical roadmap for advancing digital maturity.
This lively discussion explores why product data is becoming more critical as a business asset, how AI is raising the stakes for data quality and accessibility, and why connected, contextualized information is essential for faster, more confident decision-making. Jim and Bassanio also discuss how leading organizations are improving visibility and alignment across teams as they modernize the way product information moves through the enterprise.
Attendees will come away with a higher-level understanding of how to:
How are manufacturers managing their manufacturing operations?
Tech-Clarity invites you to participate in a research study on “level 3” manufacturing operations software for production facilities. What business value are companies gaining from software at this level? How is MES/MOM/MOS evolving? What impact is AI having on this landscape?
We will also use the results to report on best practices to maximize business value from MOS and AI. Please take 10-15 minutes to fill out this short survey. As a thank you, we will send you a copy of the report summarizing the findings.
In addition, eligible respondents will be entered into a drawing for one of twenty $25 Amazon gift cards. See the survey for eligibility details.
Take the survey now to share your perspective!
Please feel free to forward this survey to others you feel have an opinion about US-based fabs to share. Individual responses will be kept confidential.
Thank you for your support. Please check out our Active Research page for additional Tech-Clarity survey opportunities.
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About Tech-Clarity, Inc.: Tech-Clarity is an independent research firm dedicated to making the business value of technology clear. We analyze how companies improve innovation, product development, design, engineering, manufacturing, and service performance through the use of digital transformation, best practices, software technology, industrial automation, and IT services. Our mission is to help manufacturers learn how to improve business results through the use of PLM, portfolio management, CAD, simulation, MES / MOM, IoT, quality, service, supply chain, analytics, and other solutions. About Eyelit Technologies: Eyelit Technologies is a leading provider of integrated software solutions that optimize factory and multi-factory productivity across industries such as semiconductor, electronics, automotive, industrial, medical device, and aerospace & defense. Its AI-powered suite of planning, scheduling, and execution solutions enables businesses to improve production processes, enhance asset utilization, and streamline scheduling. Eyelit Technologies empowers organizations to drive profitable growth, reduce costs, improve delivery performance, and gain greater visibility, improved decision support, and decision execution. Expanding on this foundation, Eyelit’s purpose-built, industry-specific solutions extend the capabilities of existing platforms to optimize decision-making across supply chain, planning, and execution. These tailored solutions improve outcomes by addressing critical elements such as orders, quality, assets, materials, labor, and suppliers. To learn more, visit: www.eyelit.com. [post_title] => Eyelit First to Sponsor Tech-Clarity 2026-27 Making Manufacturing AI Matter Research Program [post_excerpt] => [post_status] => publish [comment_status] => closed [ping_status] => closed [post_password] => [post_name] => ai-value [to_ping] => [pinged] => [post_modified] => 2026-09-30 09:46:38 [post_modified_gmt] => 2026-09-30 13:46:38 [post_content_filtered] => [post_parent] => 0 [guid] => https://tech-clarity.com/?p=24389 [menu_order] => 0 [post_type] => post [post_mime_type] => [comment_count] => 0 [filter] => raw ) [comment_count] => 0 [current_comment] => -1 [found_posts] => 925 [max_num_pages] => 47 [max_num_comment_pages] => 0 [is_single] => [is_preview] => [is_page] => [is_archive] => [is_date] => [is_year] => [is_month] => [is_day] => [is_time] => [is_author] => [is_category] => [is_tag] => [is_tax] => [is_search] => [is_feed] => [is_comment_feed] => [is_trackback] => [is_home] => 1 [is_privacy_policy] => [is_404] => [is_embed] => [is_paged] => [is_admin] => [is_attachment] => [is_singular] => [is_robots] => [is_favicon] => [is_posts_page] => [is_post_type_archive] => [query_vars_hash:WP_Query:private] => 13ea7d9d1aeecf069f8f0166cdde328f [query_vars_changed:WP_Query:private] => 1 [thumbnails_cached] => [allow_query_attachment_by_filename:protected] => [stopwords:WP_Query:private] => [compat_fields:WP_Query:private] => Array ( [0] => query_vars_hash [1] => query_vars_changed ) [compat_methods:WP_Query:private] => Array ( [0] => init_query_flags [1] => parse_tax_query ) [query_cache_key:WP_Query:private] => wp_query:713149a2bd7d3e9893586664a87cb713 )
Research Firm Tech-Clarity Launches its Research Program on AI Value Middletown, DE, and Holmdel, NJ, USA, Sept 30, 2026 – Independent research firm Tech-Clarity, Inc., is delighted to announce that Eyelit Technologies is the first sponsor for the 2027 iteration of its Making Manufacturing AI Matter research program. This research will review manufacturers’ progress in…
Are leaders making the right operational decisions that correlate to better business outcomes? Do AI and manufacturing software help? Join this panel-style webinar on IndustryWeek October 27, 2026, at Noon Eastern time to learn from a new survey and experienced manufacturing pros from companies of all sizes. Research firm Tech-Clarity’s survey of over 400 professionals…
What is keeping manufacturing operations from leveraging all of their rich data to optimize and use AI effectively? Often, it’s the lack of a structured, coherent, well-managed way to ensure everyone can leverage all the right data at the right time. This Industrial Intelligence Platform Buyer’s Guide discusses what to consider and the many facets…
How can better information, automation, and AI improve design engineering decisions? Design engineers face growing pressure to bring products to market faster, control costs, improve quality, and manage increasing product complexity. Yet important information that impacts decisions may be difficult to obtain, arri ve too late, or require time-consuming manual work. At the same time,…
What does it take for manufacturers to achieve a competitive advantage in new product development? Our recent survey identified product innovation, product quality, and new product development (NPD) speed as the three most important drivers of that advantage. How do companies overcome the operational challenges standing in the way of improving all three? Please enjoy…
How far have manufacturers progressed in connecting the digital thread across engineering and manufacturing? Earlier this year we surveyed over 200 manufacturers and found that they are pursuing PLM-MES integration for two primary reasons; improving time-to-market and product quality. The research showed other goals as well, including supporting AI initiatives. But the data also…
Engineering data is valuable across the enterprise, but not everybody gets the information they need. We researched the need for 3D engineering data and the applications that create, access, and share it. We found the need is growing and investigated how companies are filling it. It’s not just the typical build-or-buy scenario. Please enjoy the…
Where should A&D manufacturing companies focus their Model-Based Enterprise (MBE) efforts? To achieve MBE goals, manufacturers must track parts and processes from design engineering through manufacturing to the sustainment of the finished product in the field. The challenge is not transferring information among the business, design, and manufacturing systems; it is the complexity of creating…
Tech-Clarity is pleased to announce that we are expanding our research team and coverage to include the full range of supply chain functions that sit under the Chief Supply Chain Officer including advanced planning and scheduling, visibility, risk, procurement, and order management. With an extensive background as a Gartner analyst, supply chain vendor executive, and…
Introducing the Topic Digital transformation in manufacturing is entering a new phase, as solution providers move beyond traditional PLM offerings toward AI-driven “digital twins” and connected data foundations that promise to make generative AI trustworthy for mission-critical engineering decisions. In this interview, Stephane Declee, CEO of the ENOVIA brand at Dassault Systèmes, discusses how the…
Amber Salley is the Vice President of Research for Supply Chain at Tech-Clarity, covering supply chain management, including advanced planning and scheduling, S&OP/IBP, supply chain visibility, risk and resilience, procurement, and order management. Amber has over 25 years of experience in supply chain. She has worked as a practitioner with the IBM Integrated Supply Chain,…
Does it feel like your investments in machines, automation, and production systems are not delivering their full potential? You can’t make high-volume manufacturing simpler. Yet you can shorten the time between plant activity, actionable insights, and measurable business value. In short, you can maximize your tech spend. Listen in as an industry analyst and operational…
What if the tablets and scanners supporting your operations never went down during a shift? How much would you save in spares, employee time, and data reliability? DeepCharge has developed an AI-powered platform designed to bring proactive and predictive maintenance to mobile device fleets. They claim it can begin producing device intelligence soon after deployment…
How are product development and manufacturing changing? Product development and manufacturing are evolving rapidly. Organizations are facing increasing product complexity, workforce challenges, rising customer expectations, and growing pressure to improve speed, quality, and innovation. At the same time, emerging technologies such as AI are creating new opportunities, and new questions. Tech-Clarity is conducting research to…
Could deep, proven planning, scheduling, and execution deliver higher value if they acted as a closed-loop, real-time system? OK, that’s an obvious yes, particularly for complex discrete industries. While it’s not common in the market, it is the driving vision for Eyelit Technologies (Eyelit). They are leveraging both longstanding Eyelit integration capabilities and more recently…
Tech-Clarity’s survey reaches the maximum of six sponsors and is actively collecting manufacturers’ responses Media, PA, USA, July 15, 2026 – Results are streaming in: interest in Manufacturing Operations Software (MOS) is very high. Tech-Clarity, Inc. has reached the maximum of six sponsors for the 2026 research program on The Business Value and Evolution of Manufacturing…
Makersite Delivers Product Lifecycle Intelligence (PLI) We have been impressed with Makersite and their focus on Product Lifecycle Intelligence (PLI), as we shared in our earlier briefing insight. Makersite is a small company that has big customers, and they’re helping those companies make significant gains on compliance, cost, sustainability, and supply chain risk. Their #PLI…
Don’t miss this recorded event replay on achieving product data-driven digital maturity in the age of AI! Digital maturity is no longer just about adopting new tools. For design and manufacturing organizations, it is about building the strategy, culture, and connected data foundation needed to make better decisions across the product lifecycle — and to…
MESA and Tech-Clarity Open Survey on The Business Value and Evolution of MES and AI Knowledge Sharing Opportunity: Complete a survey to get the results and learn from other manufacturers Phoenix, AZ, and Media, PA, USA, June 23, 2026 – The Manufacturing Enterprise Solutions Association International (MESA) and Tech-Clarity, Inc. are inviting responses to a…
How are manufacturers managing their manufacturing operations? Tech-Clarity invites you to participate in a research study on “level 3” manufacturing operations software for production facilities. What business value are companies gaining from software at this level? How is MES/MOM/MOS evolving? What impact is AI having on this landscape? We will also use the results to…
