The digital transformation of engineering workflows can get complex. An obvious example is when a manufacturer’s legacy technology must be integrated within an organization’s new cloud-based engineering platform. These systems may not be compatible out of the gate. There are other hurdles; for example, consider an aerospace company with government contracts. For national security, they may be restricted from storing data on the cloud. Meanwhile, another company’s in-house software may be unable to function with data on the cloud.

Cloud-based engineering platforms exist, and most offer the ability to transfer whole product lifecycle workflows — from design, to manufacturing and beyond — into a single ecosystem. For many organizations, these platforms offer a great method to implement a digital transformation — but they are not for every company.

“We need to meet our customers where they are,” says Philip Lord, business strategy manager at Autodesk. “We are building a complete platform for design and make, but we don’t expect our customers to adopt it all at once. We’re providing flexibility to connect systems they’re using to bring them along with future platform capabilities. One of the biggest barriers of digital transformation is connecting data across different silos and workflows.  That’s why we’ve released Vault APIs, to help customers bridge those different data silos.”

The end goal of Autodesk Vault APIs is to provide every user with the data and information they need to succeed — but within their tools and systems of choice. This makes the implementation of that data — and using it to make better, informed decisions — more natural.

Creating a tailored data management and communication system

Irvin Hayes, Jr., Vault product manager at Autodesk explains that most of the day-to-day data engineers interact with are catered to their specific function. Consider a CAD file; many engineers working in various tools will need much of the information within the geometry file — but they may not need all the information. Meanwhile, not everyone in a product development cycle has a technical background. For example, marketers may lack the need or authorization to access the CAD files. However, they are sure to need some of the information inside the file.

“The challenge,” Hayes proposes, “is how do I get this information out to those users to make some really good decisions outside of engineering?” He notes the importance of providing the right data, in the right way, to these users for several reasons. First, it reduces the chance of overloading users with information. Second, users gain a proper understanding of the project’s status, reducing errors and rework. Third, it eliminates the chance of users making decisions based on outdated information. Next, it ensures users have most of the information they need as they need it. And finally, if the user still needs more information, it is only a search away.

Using Autodesk Vault APIs, a company can build this proposed, tailored data management and communication system to fit its specific needs. Consider an organization using Autodesk Vault to store confidential CAD data locally. However, that organization may also use Autodesk Platform Services to perform other tasks, such as project management.

“We need to make sure that those who still need an on-premise solution — for various reasons — can still connect to that platform to get the availability of other services we offer,” says Hayes. “That’s what we’re looking for here with the APIs because we still have those customers. They love what we’re doing on our platform services, but they still need Vault’s on-premise solution.”

Once again, consider a CAD file located in Autodesk Vault. Cloud tools don’t need access to the full 3D model to track when a task is completed. By using Autodesk Vault API, the two systems can communicate bi-directionally. This enables a single source of truth and seamless integration, all while keeping IP behind firewalls.

“Vault helps you keep all this data in one location for a particular set of users, groups, and tools,” says Hayes. “It helps to make sure that data is reused and, if possible, limits the amount of outdated information … Since you can see that information, you also hope to decrease the number of reworked designs and duplicated data.”

The possibilities unlocked with Autodesk Vault APIs

While on the topic of data accessibility, it is essential to note that for many organizations engineering data and product geometry is notoriously hard to find. However, these challenges can also be solved using Vault APIs.

“The APIs have login and search capabilities that pull data for reading purposes,” says Hayes. “The APIs are designed to help the developer or user input what they want to find … and then retrieve the right information.”

Another use case for Vault API is that it can help non-technical decision-makers access information that would be hard for them to interact with — unless they underwent significant training. This is important when these individuals are required for the review process but lack the technical training to access engineering data.

“Using the APIs, you could expose the review process to, let’s say, Microsoft Teams as an example,” says Hayes. People on “Microsoft Teams would get a view of the information or the model that’s being proposed. They can markup that information and send it back to the designer. Suppose there are things to change if a design is approved, and various other information can be shared this way. The reviewer doesn’t need to be a Vault user or have knowledge of Vault. They can stay in the tool they are familiar with — in this case, Microsoft Teams.”

APIs can also be used by an organization to produce in-house software tools, automation and workflows. Lord notes that this means that third-party companies, Autodesk users, and Autodesk partners can create customized applications that run alongside their Autodesk tools.

Using Vault APIs, companies can also add legacy tools and software to a digital transformation. This is because the APIs connect data across systems to form a digital thread. Most organizations that undergo a digital transformation do so in stages and inevitably end up with disparate systems from various developers. It’s unlikely that companies will be able to let go of all those systems on day one in favor of a whole platform. In this scenario, it’s reasonable to implement digital threads via APIs to ensure that all these disparate systems can communicate bi-directionally and produce a single source of truth.

“It’s more of a middle ground,” notes Lord. “Our strategy is to really bring a lot of disciplines of designing and manufacturing into one platform. But we can’t bring an entire company’s functions and operations into one platform. We recognize we’re going to exist with other platforms … in a multi-platform ecosystem within a customer’s architecture. This is why connectivity and extensibility of the platform are so important to us. So, Vault with APIs, for example, plays a part in connecting to other platforms.”

Finally, consider one system receiving a software update from its vendor. The company using that system may need to update its digital threads to ensure the new update is compatible with the rest of their tailored ecosystem. Autodesk provides many ways to simplify this maintenance via Vault API. One example is that Vault API is based on Rest API, which is optimized for cloud connectivity and data gathering. As a result, Hayes explains that it will need less maintenance than other manually maintained digital threads systems. Additionally, many of Autodesk’s tools have built-in methods to communicate bi-directionally without the use of API.

When tailored data management systems are necessary

The attractive nature of a single design and make platform is that organizations hope to ‘hit-the-easy-button’ and walk away with a full digital transformation of their workflows, processes and products. But as Lord and Hayes point out above, the adoption of a whole platform is more of a journey.

Though Vault API addresses many of the challenges associated with digital transformation, the journey to implement it is a phased process. Organizations have invested significantly in in-house programming and IT expertise; the phased approach allows them to gradually transition to a complete platform and update in-house systems as needed. Additionally, it enables companies to connect data and processes that must remain outside of a cloud-based platform — for those organizations, tools like Vault API will always be a necessity.

Finally, Autodesk offers various tools to help organizations build digital threads using APIs. Hayes suggests that organizations look at sample code and documentation offered by Autodesk, its community of third-party coders and internal development supports.

To learn more about Vault Data APIs, visit the Autodesk website.

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Chairman and CEO Vimal Kapur on February 6 announced the plan to pursue a full separation of Automation and Aerospace Technologies, adding to the previously announced plan to spin-off Advanced Materials,

The move will result in three publicly listed companies with distinct strategies and growth drivers. The company said in a press release that the separation is intended to be completed in the second half of 2026 and will be done in a manner that is tax-free to Honeywell shareholders.

“The formation of three independent, industry-leading companies builds on the powerful foundation we have created, positioning each to pursue tailored growth strategies, and unlock significant value for shareholders and customers,” said Vimal Kapur, Chairman and CEO of Honeywell. “Our simplification of Honeywell has rapidly advanced over the past year, and we will continue to shape our portfolio to create further shareholder value. We have a rich pipeline of strategic bolt-on acquisition targets, and we plan to continue deploying capital to further enhance each business as we prepare them to become leading, independent public companies.”

Honeywell says the planned separations of automation, aerospace and advanced materials will deliver a slew of benefits, including simplified strategic focus and greater financial flexibility to pursue distinct organic growth opportunities through investment.

Honeywell Automation will create the buildings and industrial infrastructure of the future, leveraging process technology, software, and AI-enabled, autonomous solutions, said Kapur. “As a standalone company with a simplified operating structure and enhanced focus, Honeywell Automation will be better able to capitalize on the global megatrends underpinning its business, from energy security and sustainability to digitalization and artificial intelligence.”

Honeywell says it’s aerospace company will see unprecedented demand in the years ahead from commercial and defense markets, making it the right time for the business to operate as a standalone, public company. “Today’s announcement is the culmination of more than a century of innovation and investment in leading technologies from Honeywell Aerospace that have revolutionized the aviation industry several times over. This next step will further enable the business to continue to lead the future of aviation.”

Here’s a look at how each of the three new companies will operate:

Honeywell Automation: Positioned for the industrial world’s transition from automation to autonomy, with a comprehensive portfolio of technologies, solutions, and software to drive customers’ productivity. Honeywell Automation will maintain its global scale, with 2024 revenue of $18 billion. Honeywell Automation will connect assets, people and processes to push digital transformation.

Honeywell Aerospace: Its technology and solutions are used on virtually every commercial and defense aircraft platform worldwide and include aircraft propulsion, cockpit and navigation systems, and auxiliary power systems. With $15 billion in annual revenue in 2024 and a large, global installed base, Honeywell Aerospace will be one of the largest publicly traded, pure play aerospace suppliers.

Advanced Materials: This business will be a sustainability-focused specialty chemicals and materials company with a focus on fluorine products, electronic materials, industrial grade fibers, and healthcare packaging. With nearly $4 billion in revenue last year, Advanced Materials offers leading technologies with premier brands, including its low global warming Solstice hydrofluoro-olefin (HFO) technology.

Honeywell says it remains on pace to exceed its commitment to deploy at least $25 billion toward high-return capital expenditures, dividends, opportunistic share purchases and accretive acquisitions through 2025. The company says it will continue its portfolio transformation efforts during the separation planning process.

Since December 2023, Honeywell has announced a number of strategic actions with about $9 billion of accretive acquisitions, including the Access Solutions business from Carrier Global, Civitanavi Systems, CAES Systems, and the liquefied natural gas (LNG) business from Air Products. Honeywell will continue with its planned divestment of its Personal Protective Equipment business, which is expected to close in the first half of 2025.

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I expect effective, enterprise-spanning collaboration to be front and center. Without that, no new product (or service or system) will succeed in the marketplace and/or with its users … or even get to market in the first place.  This points to the criticality of people in digital transformation and why people are cited as a major cause of many digital transformation failures.

These failures, estimated at three-fourths of all digital transformation projects, are inevitable.  As long as users and managers don’t understand Digital Transformation, they will not accept it and may even actively resist it. In what I see as a corollary to Murphy’s Law (the idea that “anything that can go wrong will”) when digital transformation efforts fail too often, they will stoke all-too-real fears that lead people to thwart further efforts.

There is nothing mysterious about digital transformation failures.  They are caused by the same factors and lack of oversight that often leads to every other technology implementation failure.  Any list of such failures will suffice, so I see no point in reiterating them here. 

Assuring the successful implementation and adoption of any new technology (or system or process, for that matter) requires that users get comfortable with it and see how its use in their everyday jobs provides value.  It’s that simple … and that complex!

The myriad changes and disruptions surging through the global economy make successful digital transformations increasingly urgent, CIMdata’s clients tell us.  As the leading authority on Product Lifecycle Management (PLM) and its digital transformation, CIMdata provides research, education, and strategic consulting to clients around the world.  This requires us to have our ears to the ground.

Before we get into what I see as being 2025’s key digital transformation drivers, we must be clear: What is Digital Transformation?  While there are many definitions of digital transformation, CIMdata prefers Gartner’s: “…the use of digital technologies to change a business model and provide new revenue and value-producing opportunities; it is the process of moving to a digital business.”

At the end of this article, we will take a more comprehensive look at digital transformation and its impacts.

Now, on to 2025’s drivers.

The ongoing maturation of PLM technologies is Driver No. 1.  By continually expanding capabilities for product design, development, analysis, and connectivity, PLM solution providers are helping pull operations technology (OT) and engineering technology (ET) closer together with powerful new tools that support and enhance an organization’s overall information technology (IT) landscape.

Enhancing PLM’s new capabilities are artificial intelligence (AI) as both Generative AI (GenAI) and Retrieval Augmented Generative (RAG AI) running on the powerful new computers they require.  AI and this hardware are rapidly spreading through the global economy, and every day brings new announcements.

The growth of PLM and the new surge of interest in digital transformation have the same drivers.  Every organization in the industrial and commercial world is under growing pressure to create products, develop services, and enhance and/or build/rebuild these systems on which success depends. 

CIMdata clients, among the world’s largest companies, tell us they are subject to the same drivers.  This is especially true of the huge aerospace, defense, and transportation corporations, and the builders of the production systems on which they depend.  They pioneered the use of PLM-enabling technology in the mid-to-late 1980s to support complex products and systems with service lives measured in decades.

Industrial consolidations and acquisitions are Driver No. 2.  They combine multiple PLM implementations that differ widely in source, age, and extent of use.  They must be made to work together (i.e., they must be “harmonized”), usually with extensive modifications, many of which result in PLM implementation revisions and enhancements that are part of its maturation.

Accompanying and often driving these consolidations and acquisitions are staffing shortages, adding to the pressure to simplify, speed up, and broaden access to data.  This access fosters more effective collaboration, which is essential for developing a steady stream of competitive and profitable offerings in every marketplace.

In addition, digital transformation “upskills” every job and task it touches.  The need for new skills must be clearly understood and acted upon.

Industrial consolidations and expansions may accelerate yet again as the U.S. Department of Defense continues to drive its Digital Engineering initiatives, adding pressure for digital transformation throughout its vast supply chains.  Additionally, new domestic policies may step up the “reshoring” of manufacturing, which is already underway in many other countries.  As a result, companies that have failed with digital transformation may be incentivized to try again.

Fortunately, as PLM matures, the time lags between advances in PLM capabilities and their comprehension and adoption are shrinking.  However, while this is Driver No. 3, I still don’t see comprehension and adoption growing fast enough to keep up with Driver No. 2.  

Still needed is a better understanding of processes as they are actually used, a deeper appreciation of the benefits of newly available technologies, and redoubled efforts to get “people” to look up from their day-to-day tasks and see what is happening all around them.  Ultimately, they must recognize the growing knowledge gaps in their part of the business.

Knowledge gaps mean people: users, implementers, managers, and leadership. Digital transformation will fail again unless “people” are:

•  Relieved of fears of retribution if they point out difficulties

•  Eased out of any temptation to disrupt the transformation

•  Made to see that user buy-in is essential, not just for success but for survival.

This employee buy-in and its corresponding user empowerment are also strategic to Digital Transformation.  Hopefully, this surprises no one.

Talk to the “people” singly and in groups about what they like and dislike about progress and personal expectations.  Find out why they feel the way they do, then help them to see how one more failure, even a small one, will hurt everyone. 

For 2025 and Beyond

From my perspective, it seems that everything in the digital world has become “strategic,” i.e., every change is critical and fosters more change.  Looking at this objectively, I see that:

•  Digital transformation is strategic for organizations to strive and then prosper.

•  PLM is increasingly viewed as a core enabler of Digital Transformation for most

   companies that design and deliver products and/or services to the market.

•  A solid grasp of People, Processes, and Technologies is strategic to PLM.

Digital transformation presents a dramatic informational vision of an industrial and economic future that is accelerating right in front of us.  Powerful new technologies, including enhanced PLM, are raising expectations across the entire organization.  Common sense tells us these expectations should be consolidated into Key Performance Indicators (KPIs) and measurable return on investment (ROI) calculations that align with the organization’s business processes and its most promising expectations.

To conclude, the three drivers described above and mentioned below are the most prominent positive developments:

1. The ongoing maturation of PLM’s capabilities
2. Industrial consolidations and acquisitions with the resulting harmonization of multiple digital implementations
3. Shrinkage of the digital knowledge gap

For 2025, taken together, these offer—and require—better insights into the new technologies being evaluated, providing a better grasp of new processes being implemented and making it easier for people to trust in the long-term benefits of Digital Transformation … ultimately with collaboration front and center.

So we ask again, what is digital transformation?  Every firm in any digital business has its own definition.  Here are three:

•  Siemens Digital Industrial Software: “digital transformation is taking advantage of high-end computer systems and software to revolutionize the way products are developed, produced and optimized.  Foundational to such a transformation are digital twins, virtual models that let engineers test and evaluate products and systems before building them, and digital threads, virtual connections between tasks and processes throughout the product lifecycle.”

•  IBM: “Digital Transformation is a business strategy initiative that incorporates digital technology across all areas of an organization.  It evaluates and modernizes an organization’s processes, products, operations and technology stack to enable continual, rapid, customer-driven innovation.”

•  McKinsey & Co.: “Digital Transformation is the rewiring of an organization, with the goal of creating value by continuously deploying tech at scale. A clear digital transformation strategy focused on specific domains and enabled by a set of specific capabilities is critical for organizations to not only compete but survive. Digital transformation is not a one-and-done project; most executives will be on this journey for the rest of their careers.”

At the end of the day, I am convinced that every aspect of these definitions will accelerate in 2025 and the coming years. 

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Energy company Repsol has extended its co-innovation partnership with Dublin-based professional services firm Accenture to accelerate the use of generative AI across the company, through the introduction and deployment of AI agent systems. This “agentification” will help to improve the efficiency of processes as they are scaled across all company businesses.

Introducing AI agents is part of the evolution of Repsol’s digital program, an extension of the work carried out for more than two years in the energy firm’s Generative AI competence Center, which has laid the foundations for analyzing and understanding the advantages of generative AI and defined a strategy to extend it throughout the company.

“With the extension of our collaboration with Accenture, we continue to drive our digitalization and AI push through the introduction of generative AI agents,” said Josu Jon Imaz, CEO of Repsol. “We aspire to be one of the pioneering companies in the energy sector in the use of these technologies. Since we launched our Digital Program more than six years ago, Accenture has been providing us with tools to improve our efficiency and competitiveness, in our effort to transform the company through technology.”

The deal means Accenture will help build and deploy customized, autonomous AI agents, powered by components of the Accenture AI Refinery platform and the Nvidia AI platform, including Nvidia accelerated computing and Nvidia AI enterprise software.

In a press release, Repsol says these agents will help “reinvent and streamline processes into more dynamic and less complex workflows to boost productivity, ranging from planning and forecasting to application maintenance and incident resolution,” enabling Repsol employees to work faster, simpler and more efficiently.

The two companies will also explore the use of AI agents and Nvidia Omniverse for digital twins and robotic solutions to perform maintenance and other activities in its industrial and logistics centers more efficiently.

“We are excited to help Repsol achieve a new level of performance by working together to create tailored AI agents with the Accenture AI Refinery and the NVIDIA AI platform. Accelerating the use of agentic AI will enhance efficiency and productivity at speed, better serve customers with personalized experiences, and ultimately help Repsol gain competitive advantage,” said Julie Sweet, chair and CEO, Accenture.

On the customer side, these technologies will deliver personalized offers with greater accuracy and speed.

As part of this agreement, Repsol will also expand training for its employees.

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Download your free copy of the 77-page e-book by filling in the form to the right.

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It’s no secret that a new regime of massive tariffs is set to roil the North American economy. Indeed, what was once a continent made of economic partners now appears to have become something completely different.

White House press secretary Karoline Leavitt at a press briefing on January 31 brushed off reports that a plan to impose 25% tariffs on both Canada and Mexico has been pushed back to March 1, confirming that the tariffs will go ahead on Feb. 1.

**UPDATE: On February 3, 2025 the Trump Administration announced that it would indeed push tariff implementation back at least 30 days to March 1.**

For their part, Canadian Prime Minister Justin Trudeau and Mexican President Claudia Sheinbaum have both promised retaliation if the tariffs go ahead.

Tariff jitters have already started to leave their mark, as BNN Bloomberg has reported that several steelmakers based in Canada and Mexico have “paused” the processing of new orders from US customers until they have a better understanding of the impact of any new tariffs.

How to measure tariff risk in a manufacturing company

No doubt, the risk created by 25% tariffs will be hard to predict for each region and any specific company. However, when considering tariff risk in a manufacturing company, there are several Key Performance Indicators (KPIs) that can help assess and manage the impact of tariffs on operations, costs and profitability.

If your digital transformation game is on point, these data will be at your fingertips. If your company is still in the early stages of digitalization, you will need to compile these from different sources. While every company will have its own specific metrics, here are some basic relevant KPIs that can help you quantify any potential tariff impacts:

Cost of goods sold (COGS)

Why it’s relevant: Tariffs can directly impact the cost of raw materials, components, and goods imported from other countries. Tracking COGS allows the company to monitor how tariff increases affect the overall cost structure and profitability.

What to track: Compare pre- and post-tariff costs for critical materials or products and assess the impact on overall COGS.

Supply chain lead time

Why it’s relevant: Tariffs may disrupt supply chains by delaying deliveries due to customs processes, new suppliers or changing routes. Monitoring lead times helps evaluate whether tariffs are increasing time-to-delivery for materials or finished goods.

What to track: Track delays in the arrival of materials and products due to tariff-related issues (such as port congestion or customs clearance) and adjust production schedules accordingly.

Inventory turnover

Why it’s relevant: Changes in tariffs can lead to shifts in inventory needs—either in response to price changes or disruptions in supply. Tracking inventory turnover can help manufacturers understand if they are holding too much or too little inventory due to tariff impacts.

What to track: Measure how tariffs influence inventory levels, and whether adjustments in inventory turnover rates are needed due to price fluctuations or supply chain delays.

Gross margin

Why it’s relevant: Gross margin is an important indicator of profitability, and tariffs can eat into profits if they increase costs without the ability to pass those costs onto customers. Monitoring gross margin provides insight into how well the business is absorbing tariff-related cost increases.

What to track: Compare margin changes before and after tariffs are applied to determine their impact on profitability.

Product cost variance

Why it’s relevant: Tariffs can alter the cost of production by raising prices for materials or components. Monitoring product cost variance helps manufacturers determine if tariffs are affecting specific products or lines disproportionately.

What to track: Measure the difference between expected and actual product costs and determine how tariff changes contribute to these discrepancies.

Supplier performance and reliability

Why it’s relevant: Tariffs can affect supplier reliability, especially if they cause delays in receiving materials or goods. Tracking supplier performance ensures that any tariff-related disruptions are identified early and can be addressed by sourcing alternatives.

What to track: Evaluate lead times, quality issues, and delivery reliability from suppliers in light of tariff changes.

Cost of imported goods

Why it’s relevant: The price of imported goods is one of the most direct effects of tariffs. Tracking this KPI allows manufacturers to assess how much more expensive their imported goods or materials have become because of tariffs.

What to track: Monitor changes in the cost of raw materials, components, or products that are imported, and assess whether this increase impacts product pricing or margins.

Customer pricing and profitability

Why it’s relevant: If tariffs increase costs, manufacturers may need to adjust their pricing strategies. This KPI helps assess whether customers are absorbing price increases or if the company is forced to take a hit on profit margins.

What to track: Track any pricing changes made in response to tariffs, and measure customer response (e.g., sales volume or customer retention) to determine if pricing adjustments are successful.

Market share

Why it’s relevant: Tariffs can affect a company’s ability to compete on price, especially in global markets. Monitoring market share helps assess whether tariff-related price increases are affecting the company’s competitiveness in the market.

What to track: Monitor changes in market share relative to competitors that may be more or less impacted by tariffs, or who have moved their production to regions with lower tariffs.

Cash flow

Why it’s relevant: Tariffs may impact cash flow due to increased costs of materials, potential price hikes, or delayed shipments. Cash flow KPIs allow businesses to ensure they have enough liquidity to manage tariff-related expenses.

What to track: Track working capital and cash flow from operations to see if tariffs are causing cash crunches, particularly if tariffs affect the timing of payments or the availability of materials.

Production efficiency and overall equipment efficiency

Why it’s relevant: Tariff-related supply chain disruptions can affect production schedules, which in turn impacts production efficiency. Monitoring this KPI helps assess whether the production line is experiencing inefficiencies due to material shortages or delays.

What to track: Track OEE metrics, including availability, performance, and quality, to see if tariff impacts are affecting production rates or quality.

Risk exposure by country or region

Why it’s relevant: Tariffs are often country- or region-specific, and some manufacturers may rely heavily on suppliers from regions that are subject to high tariffs. Monitoring this KPI helps companies assess their exposure to specific trade regions and diversify their supply chains accordingly.

What to track: Track the percentage of materials or components sourced from countries or regions that are likely to face tariffs and adjust sourcing strategies if needed.

Regulatory compliance and tariff changes

Why it’s relevant: Keeping track of changes in tariff rates and compliance requirements is essential for avoiding penalties and ensuring smooth operations. This KPI helps manufacturers stay on top of tariff updates and implement necessary changes in business practices.

What to track: Measure how quickly the company can adjust to regulatory and tariff changes, and track compliance with new tariff rules to avoid fines or legal issues.

By watching these KPIs and others, manufacturers can understand how tariff risk is affecting its cost structures, supply chains, cash flow, and overall competitiveness and proactively adapt, optimize their operations and make more informed decisions to mitigate tariff-related risks.

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The push for digital transformation in product lifecycle management (PLM) comes from our everyday experiences, where nearly every aspect of our personal and professional lives is mediated through digital tools.

For businesses, this shift toward digitalization raises a crucial question: How can all these tools, applications and processes coordinate seamlessly to deliver value and innovation? Just a few years ago this was limited to focusing on specific tasks or processes, but now the demand is to have up-to-date information on every tool or system you use.

We live in a hyper-connected era where systems must communicate and adapt in real time. Businesses face the challenge of managing increasingly complex ecosystems of applications, each with their own data and processes. This complexity demands a shift from traditional, siloed thinking to a more integrated, data-driven approach.

Navigating this transformation requires rethinking how data and applications interact, enabling businesses to achieve greater flexibility, efficiency and innovation.

Enterprise Applications as Process Enablers

Historically, business systems and enterprise applications were built to support specific functions and processes within a business. These tools were designed to operate within narrowly defined parameters, focusing on individual data sets, including:

• CAD Applications focused on creating and managing design data, enabling engineers to develop and iterate on product designs.
• MRP Systems (Material Requirements Planning) supported resource allocation and production planning to ensure efficient manufacturing processes.
• CRM Tools (Customer Relationship Management) managed customer interactions, sales pipelines, and support workflows.

While effective for their intended purposes, these applications were largely self-contained with minimal connectivity to other systems. Think of a PLM solution, supply chain, document management or production process. Although companies demanded interoperability, most of these projects were about how to “sync” data from design and engineering to material planning/ ERP rather than setting up a collaborative environment.

Data was treated as a byproduct of the process rather than a core asset. Over time, this siloed approach led to fragmented data landscapes, making it difficult to achieve the integration and real-time insights needed for modern business operations.

The result? Legacy systems often struggle to adapt to the demands of a digital-first world, where agility, collaboration and innovation are paramount. As businesses scale and evolve, the limitations of process-centric architectures become increasingly apparent.

The Shift to Data-First Thinking

Digital transformation fundamentally changes the relationship between processes and data. In the traditional model, processes dictated how data was structured, stored and accessed. Digital transformation flips this paradigm, prioritizing data as the foundation of modern business operations. Companies shift their attention on how to trust data, because data lives longer than applications and business tools. The design history goes on for years, but a company can switch CAD and PDM applications. Data records are more important, and here are some key reasons:

• Real-Time Insights: Data provides the foundation for real-time analytics, enabling businesses to make informed decisions quickly and respond to changing conditions.
• Flexibility and Adaptability: Processes are inherently static and limited to predefined scenarios, while data enables dynamic, context-aware responses.
• Collaboration Across Ecosystems: By connecting data through a digital thread, businesses can ensure seamless collaboration across design, manufacturing, and operations.
• Long-Term Value: Unlike processes, which can be reengineered relatively quickly, data accumulates value over time. A robust data foundation supports innovation, automation, and strategic decision-making.

In this new paradigm, data is not merely a byproduct of processes, it’s the central asset that drives innovation, efficiency, and competitive advantage. Businesses that prioritize data management are better equipped to navigate the complexities of digital transformation and achieve sustainable success.

Rethinking the Status Quo

For decades, enterprise applications like PLM (Product Lifecycle Management) systems have been designed to organize engineering processes within organizations. These systems emphasized creating a ‘single source of truth,’ focusing on managing product data records (e.g., design files, engineering documents) and enforcing processes such as data approval, versioning and updates.

PLM was an effective approach when the demand was to store data and gatekeep access. Today, this misalignment of the traditional PLM approach is becoming obvious. PLM tools should become a source of data shared with everyone.

The foundation of the switch is how PLM software adapts, becoming an “agent” that performs specific tasks on the data, such as engineering change order approval. This shift from a process-centric to a data-centric approach requires rethinking foundational concepts such as the single source of truth and adopting new strategies that prioritize collaboration, flexibility and adaptability.

A Single Source of TRUTH Change

In the context of digital transformation, the traditional single source of truth is evolving into a single source of change. This new approach emphasizes:

• Dynamic Data Organization: Data is no longer confined to rigid hierarchies or processes. Instead, it is modeled and organized to reflect real-world relationships and dependencies.
• Collaborative Workspaces: Data becomes a shared resource that enables cross-functional collaboration, regardless of where it originates or is maintained.
• Context-Aware Data Management: Systems must understand not only what the data is but also how it is connected, who can change it, and under what circumstances.

Instead of treating data as a secondary consideration, businesses must prioritize creating flexible, adaptable data models that reflect the complexity and interconnectivity of modern operations.

Practical Strategies for Disconnecting Data from Applications

Switching to a data-first approach can be challenging, especially for companies with old systems deeply tied to specific software. Some companies have data from a dozen ERP systems and several PLM applications including legacy databases. What can those companies do? Here are a few practical approaches:

Rethink Data Models

Traditional data models are often built around specific applications, which makes it hard to use data across different systems. Most traditional data systems use relational SQL databases with inflexible schemas. To solve this, companies should use modern flexible data models that don’t rely on fixed structures and can change as needed.

Graph databases are a great option because they are good at handling complex connections between data. It’s also important to organize data in a way that lets systems understand its meaning and context, making it easier to work with.

Decouple Data from Applications

Data shouldn’t depend on specific software—it should stand alone as a valuable resource. To achieve this, companies can combine data from various systems into one central place for easier access. Using tools like APIs and integration layers helps different applications share data seamlessly. Another option is data federation, which keeps data in its original systems but allows centralized access and visibility.

Knowledge Graph and AI models

This is a modern data modeling approach which is growing. A product knowledge graph organizes all product-related data—design, engineering, and manufacturing details—in one connected system. This ensures data is consistent and accurate across all areas. It also supports better decision-making with advanced analysis tools and makes it easier for different teams to work together by sharing the same information.

With a huge spike in GPT and LLM models, we can see how these models can consume data in a more holistic way disconnected from the applications where the data was originally created. They can also provide quick insights and help find the information you need from large data sets quickly and accurately, to support various chatbots and future analytics and AI agents.

Collaborative Data Management

In a data-first setup, teamwork is key. Companies should enable real-time data sharing so everyone has up-to-date information. Teams across different departments need tools to work together effectively, and clear processes for managing changes are crucial to ensure everyone stays on the same page and changes are tracked.

By following these strategies, businesses can move away from outdated, application-tied data systems and unlock new possibilities for efficiency, innovation, and growth.

Embracing the Future of Data-Driven Manufacturing

We are living through a period of profound transformation in manufacturing and PLM. The traditional model of application-defined data is giving way to a new paradigm of data-defined action. This shift represents a fundamental rethinking of how businesses approach data, applications, and processes.

By prioritizing data as the central asset of their operations, businesses can:

• Achieve greater flexibility and adaptability.
• Drive innovation and efficiency through real-time insights.
• Build resilient, future-ready systems that support long-term success.

The transition to a data-first approach is not without its challenges, but the rewards are immense. By embracing modern data management strategies and technologies, businesses can position themselves at the forefront of digital transformation, unlocking new opportunities for growth, innovation, and competitive advantage.

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Reducing the number of physical prototypes needed also limits unnecessary shipping, packaging and transportation emissions for each prototype iteration and it’s required raw materials. As companies move from traditional trial-and-error prototyping methods to virtual testing, they cut down on the carbon emissions associated with transporting products, tools, and prototypes across long distances.

With tools like simulation and generative design, engineers fine-tune product designs for maximum resource efficiency, reducing material consumption for each unit and often improving product performance. This results in products that use fewer raw materials and are optimized for energy efficiency, which aligns with both cost savings and environmental sustainability.

By testing designs digitally, engineers can predict how products will perform in real-world manufacturing environments. This allows for optimized and more energy-efficient manufacturing processes, reducing waste in energy consumption during production. When designs are optimized for efficiency beforehand, it often leads to more sustainable production methods.

Digital twins and product lifecycle management tools track and manage products from concept to end-of-life. This leads to products designed for longevity, easy maintenance, and recyclability, which supports circular economy principles. Products can be designed to be disassembled and reused, reducing waste and promoting sustainability in the long term.

Using digital prototyping tools, engineers can virtually test the performance of alternative, sustainable materials before committing to them. These informed decisions on material selection could favor eco-friendly materials that meet both product performance and environmental goals. It ensures that manufacturers can adopt sustainable materials without compromising on quality or functionality.

Digital prototyping also helps eliminate inefficiencies in design and manufacturing, which have a direct impact on cost. These savings can be reinvested in sustainable practices such as renewable energy adoption, waste reduction programs, or further innovation in eco-friendly products. The more efficient the process, the lower the overall environmental impact, which usually aligns with corporate sustainability goals.

For manufacturing engineers, digital prototyping provides the ability to design, test, and optimize products more efficiently, leading to lower material waste, reduced energy use, and more sustainable products. For CEOs, it means cost savings, a reduced environmental footprint, and the ability to meet increasingly important sustainability goals while staying competitive in the market. By embracing digital prototyping, manufacturers can lead in both innovation and sustainability while driving value for the company.

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Here’s a breakdown of key tools and software commonly used for digital prototyping:

3D CAD (Computer-Aided Design):

These tools allow designers and engineers to create detailed digital models of products. They support both 2D and 3D modeling and are used to design everything from simple parts to complex assemblies. Parametric and direct modeling capabilities allow for flexibility in making changes and iterations during the design process.

Simulation and Analysis:

Simulation enables users to test how a design will behave in real-world conditions without creating physical prototypes. They use mathematical models to simulate physical properties such as stress, thermal behavior, fluid dynamics, and vibration. They help engineers predict potential performance issues and optimize designs before production.

Visualization/Rendering:

Used for creating and visualizing digital models with a focus on aesthetics and functionality, these tools are important in the early design phase. They help visualize how a product will look and function in its environment, making them useful for concept development, visualization, and basic design adjustments.

Virtual Reality (VR) and Augmented Reality (AR):

Part of the hype cycle five years ago, VR and AR have quietly become a big part of interacting with digital prototypes in immersive, 3D environments. VR tools simulate a fully virtual model, whereas AR tools overlay digital models onto the physical world. Both are used to visualize how prototypes will look and behave in real-world settings, providing an intuitive way to review designs and test user interactions.

Product Lifecycle Management (PLM):

PLM tools manage a product’s entire lifecycle—from initial design to end-of-life. They integrate various design, simulation, and testing stages to allow teams to collaborate efficiently, track revisions, and maintain up-to-date data across all stages of the product development process.

Rapid Prototyping and 3D Printing:

These tools convert 3D models into instructions for additive manufacturing (such as 3D printing), enabling the creation of physical prototypes quickly and cost-effectively. They are commonly used to test form, fit, and function of designs before committing to full-scale production.

Digital Twin Platforms:

A digital twin is a virtual representation of a physical product, system, or process. These platforms collect data from sensors and simulations to provide a real-time, dynamic view of the product’s performance. They are used to monitor the performance of prototypes, track how they evolve over time, and optimize their operations in real-world conditions.

Generative Design:

Artificial intelligence (AI) and cloud computing are combining to automatically generate optimized design solutions based on specific input parameters and constraints (such as weight, material, strength, and cost). They create myriad design options to help designers explore innovative, efficient solutions that might not be immediately obvious in traditional design processes.

Electronic Design Automation (EDA):

These tools are used to design and prototype electronic circuits and printed circuit boards (PCBs). They help engineers create, simulate, and test electronic components and systems in the digital space, ensuring that the circuits are functionally correct before physical assembly. They also help manage the layout and routing of components for optimal electrical performance.

Each tool plays a role in different stages of the digital prototyping process, whether you’re creating a 3D model, simulating real-world conditions, testing functionality, or preparing for physical production. For manufacturing engineers and managers, selecting the right set of tools depends on the specific needs of the product being developed, such as complexity, cost, and how closely the prototype needs to mirror the final product. Tools like SolidWorks, ANSYS, Fusion 360, and Teamcenter are often popular in industrial environments where both design and testing are crucial.

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Faster Product Development Cycle

Digital prototyping allows engineers to create virtual prototypes of products, which can be tested, analyzed, and iterated upon quickly. This leads to faster product development cycles because physical prototypes—often time-consuming and costly to build—are replaced with digital models that can be easily modified and tested in virtual environments.

Multiple teams (design, testing or manufacturing) can work on the same digital prototype simultaneously, allowing for more collaborative efforts and quicker decision-making.

Cost Savings

Traditional prototyping involves building physical models, which can be expensive, especially if several iterations are needed. With digital prototyping, these physical prototypes are eliminated, saving on materials, labor, and production costs.

Problems in the design phase are identified and resolved early, avoiding costly rework during the manufacturing phase. This reduces the likelihood of production delays and scrap due to design flaws.

Simulation tools allow engineers to analyze the material requirements and structure before physical production, leading to optimized use of materials and reducing waste.

Enhanced Design Precision and Quality

Digital prototypes allow engineers to simulate real-world conditions, ensuring that designs are optimized for performance, strength, and durability before moving to production. This leads to higher-quality end products with fewer design flaws.

Engineers can perform complex tests (e.g., stress, thermal, fluid dynamics) on digital prototypes to see how they will behave under various conditions, which might be difficult or impractical to replicate with physical prototypes.

Quick revisions to the digital prototype enable engineers to try out multiple variations of a design, ensuring the best possible version before manufacturing.

Better Collaboration and Communication

Digital prototypes can be shared across teams and even external partners globally, enhancing communication between departments (e.g., design, engineering, manufacturing) and improving alignment on goals, timelines, and requirements.

Managers and stakeholders can easily visualize and interact with the digital prototype, facilitating more effective decision-making and feedback. This improves transparency in the design process and helps prevent misunderstandings.

Streamlined Production Processes

Digital prototypes can be designed with the manufacturing process in mind, allowing engineers to simulate how a product will be built, identify potential manufacturing challenges, and optimize the design for ease of production.

Once a digital prototype is refined, it can be used to create detailed specifications for tooling, machinery, and assembly processes. This helps ensure a smoother transition from design to actual manufacturing with fewer adjustments needed on the factory floor.

Simulating assembly processes in the digital space can help identify potential issues in assembly lines or machinery setups, minimizing costly mistakes and time-consuming adjustments during physical production.

Customization and Flexibility

With digital prototypes, engineers can quickly adapt designs to meet customer-specific needs or adjustments. This is particularly advantageous for industries that require high levels of customization or need to pivot based on market demands.

Once a product design is finalized digitally, scaling up production becomes smoother because the digital model can be used for a variety of manufacturing methods (e.g., additive manufacturing, CNC machining, injection molding).

Integration with Advanced Manufacturing Technologies

Digital prototypes integrate seamlessly with additive manufacturing (3D printing), which enables quick and cost-effective production of physical prototypes or even end-use parts, enhancing flexibility in the prototyping process.

Engineers can optimize designs specifically for additive manufacturing techniques using digital prototyping, which fits into Simulation-Driven Design for Additive Manufacturing (DfAM). This reduces the need for costly adjustments during the actual production process.

Enhanced Innovation and Risk Mitigation

Digital prototyping allows engineers to experiment with different design ideas without the risk or cost of building physical prototypes. This fosters a more innovative environment, as teams can quickly test a variety of concepts and find the best solution.

By identifying design flaws, performance issues, and manufacturing challenges in a digital environment, potential risks are minimized before physical production begins, leading to reduced risks of product recalls or costly revisions during the manufacturing phase.

Sustainability Benefits

The ability to simulate and optimize designs virtually reduces the need for multiple physical prototypes, resulting in less material waste and a more eco-friendly development process.

Through digital modeling, engineers can analyze energy consumption, waste, and emissions, helping to design more energy-efficient products and production processes that align with sustainability goals.

Investing in digital prototyping offers significant advantages in terms of cost savings, faster time to market, and improved product quality. They can use it to test and iterate designs rapidly, streamline manufacturing processes, and encourage more innovative solutions. Ultimately, digital prototyping helps create higher-quality products, reduce risks, and improve the overall efficiency of the manufacturing process, all of which make it a powerful tool for modern manufacturers.

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