We’ve all been swept up in the AI wave. We have tried all manner of large language models (LLMs, including the media favorite, ChatGPT) and found them all lacking in one way or another. One —Claude — claimed to be good at math but wasn’t. Most seem to be good at answering college-level English literature questions, but incapable of solving freshman physics problems. We have used them for help when researching articles (especially Perplexity). When we gave ChatGPT several engineering-type questions, it always had an answer — but not always correct ones.

Perhaps we should not expect LLMs to be know-it-alls. How could they be? They’re trained on data troves of information scraped from all who would supply it as well as publicly available information such as Wikipedia. While the collective size of the data at their disposal is staggering, the quality, accuracy and depth of their answers is all too often lacking. Plus, the data that may be most valuable to engineers — their own — is off limits to LLMs.

Any organization of a good size and long history will have a tremendous amount of information. Therin is a valuable history with documents, drawings, models, revisions, PLM databases … all those which compromise tribal knowledge.

If this was a class on the failures of LLMs for engineers and the need for AIs to train on their own data, Jeff Platon, head of marketing at Accuris, would be in the front row and emphatically raising his hand.

“We totally got this,” he would say.

Accuris has a product called Goldfire, a semantic search application made to search an organization’s data.

Semantic search is the type of search that can sense context and the user’s meaning, as opposed to keyword search, which looks for exact word matches. By way of example, semantic search will find a different “best football player” in the U.S. than the UK, recognizing the correct sport in each location.

In the U.S., who bigger or with more history than the Navy? Who more to benefit from sematic search through vast repositories of data? Who is less likely to put their data on the cloud for training of civilian LLMs for their training? Instead, the military carefully guards its information from prying eyes, keeping it encrypted on secure servers.

Platon opens with a slide of an aircraft carrier. He comes from a Navy family. With the single biggest weapon system on display, a marvel of technology and an exemplar of operations in the most devastating environment possible — war — he has the attention.

Mining a company’s own data is what Goldfire is all about. Accuris does this by combing through all of it, indexing, linking … in short organizing and hyperlinking in a way to make information findable and usable.

The aircraft carrier was not just for show. The U.S. Navy is an Accuris customer and uses Accuris to help make design decisions.

NASA is another customer. Platon tells a story of helping NASA find information to avoid astronauts returning to Earth, splashing down in the ocean, but their capsule unable to right itself leading to a potentially “very bad outcome.” NASA scoured their information troves for information on inflatable bags used to successfully right reentry capsules for the Apollo mission. They found nothing. They called retired engineers to see if they had kept any information. The search went on for a year until finally they called in Accuris.

“Within 20 minutes of implementing the AI infrastructure, they found 249 documents that solved their problem, say Platon. They were able to fix the engineering problem. NASA saved $2 to $3M dollars and one to two more years of sorting through the data and organizing it.”

“We helped NASA find a solution to astronaut recovery,” says Platon.

Accuris works with text documents and databases. It aims for a wholistic approach to all of an organization’s needs. By analyzing everything, it’s able to connect data islands of CAD and PLM to PLM, ERP, SQL databases as well as manufacturing and upstream operations such as procurement systems. Along with other solutions such as automated BOM reporting from a database of 1.2B parts and Supply Chain Intelligence, the ESDU knowledge base containing engineering design data, Engineering Workbench, an AI platform for standards, codes, and regulations management.

Putting it all together, Accuris should theoretically enable an organization to answer operational questions that span departments and disciplines. For example, if a part fails on a production line, how long it would take for the supply chain to replace it?

This is vital for China Plus One strategies, says Platon.

Accuris’ AI technology is already available for this purpose and in use by 900,000 design engineers, including many large companies, branches of the armed forces and defense contractors.

It may be Accuris’ worst kept secret.

“We don’t make a big deal of it,” says Platon modestly.

Lest we think Accuris is only military, Platon steers us towards green hydrogen, an energy source of such potential that people have stated that it will save the Earth, a technology in which Accuris is engaging.

Accuris may be better known as IHS (its previous owner) and as a publisher of millions of standards of which ANSI, SAE and AS are the best known. The company also has access to seven million technical articles and books and 108 million patents and patent applications.

That, all by itself, would be something for a neural network to feast on.

The post Forget ChatGPT, the answer is in your own data appeared first on Engineering.com.

It might be time to reframe digital transformation discussions and implementations, with a dramatic shift in the focus of digital transformation away from inputs (“new and improved” and “faster, better, cheaper”) into measurable outcomes that fit smoothly into an enterprise’s business plans. This is nothing less than a reorientation of digital transformation and its enablement throughout an organization’s product lifecycle—from concept through end-of-life.

Nearly all industrial companies take digital transformation’s opportunities and implementation challenges very seriously. Management and leadership in a few laggard enterprises, however, still see digital transformation—or digitalization—as just a buzzword, even as they fall further behind their competitors.

A digital transformation tutorial

While it is clear to all within the PLM community that PLM is foundational for a meaningful digitalization strategy, senior leadership does not always understand this truth, leading to a paradox. The investment level in digitalization indicated by my organization’s research seems appropriate, yet success is in jeopardy.

Weak digitalization plans often reflect a need for top executives to be more aware of how much digitalization will potentially impact the jobs and responsibilities of everyone in the organization. Hence, well-thought-out integration and implementation strategies are mandatory. Also needed is the realization that digitalization is dependent on a strong and comprehensive PLM strategy, just as PLM is greatly enabled by digitalization. This synergy enhances the value of the resulting digital transformation initiative.

Fundamentally, digitalization is the next logical step in the ongoing revolution of representing anything and everything in 1s and 0s. This is to say that digitalization is moving from a fuzzy concept to a data-driven derailment of the status quo, including:

• Transforming products from physical goods into tangible services, the product-as-a-service (PaaS) business model that renders the “product” into data.
• As information’s importance continues to grow, products and services are increasingly bought and installed for the data they generate or collect.
• New sources of information are speeding up innovation and product development, adding urgency to digitalization.

It is important to remember that today’s PLM professionals were busy with applications and implementations decades ago when digitalization really was a fuzzy concept—PLM was here first, so to speak. The digital tools that were used to support the product lifecycle, which have now been in use for a few decades, were the beginnings of digitalization. What those tools can now achieve is essential to meeting today’s broad-based enterprise-level digitalization objectives.

What does digitalization mean? Digitalization transforms business models to generate new revenue and value opportunities; it is far more than an analog-to-digital change. If leadership struggles with this, explain it in 1990s terms as “knowledge management.” And make sure digitalization is not seen as merely scanning and digitizing paper documents into images. Images are losing their value and rationale as containers for today’s massive information flows. With the shift from documents to data well underway, containers, in any format, are falling short of the enterprise’s real needs.

Moreover, digitalization is not a one-and-done transformation, which can be demonstrated with a look at the history of recorded music. Analog music formats evolved from 45 RPM records with a single song on each side to vinyl long-playing (LP) media with several songs on each side, followed by cassette tape players, the Sony Walkman, and CDs. Music digitalization arrived as MP3 players with vastly improved sound and hundreds of songs. Now digitalization has taken music online to reach everyone through streaming services.

Likewise, digitalization is the next step in lifecycle management in leveraging existing and future technologies—not the starting point. Enabling digitalization requires end-to-end connectivity, end-to-end lifecycle optimization and sometimes deep changes in the organization and its work culture.

Why points of view matter

The fundamental task in getting anyone to “see” anything is to understand that person’s point of view and how they developed that way of thinking. The challenge with PLM-enabled digitalization comes when senior leaders perceive digitalization as different from and more strategic than PLM, and then allow independent, disconnected implementations. These initiatives are plagued by:

• Implementation gaps and overlaps
• Information resource duplications
• Extensions and integrations that leave some information requirements unsupported
• Independent and incompatible solution architectures

The results should surprise no one: Time-to-value and ROI are substantially diminished.

Points of view matter. To understand why, it helps to see the digital transformation-centric point of view as outside-in and the PLM-centric point of view as inside-out.

The outside-in view sees digitalization as strategically reconfiguring business functions and business entities. Relationships with and between external entities become opportunities for new value propositions, especially in predictive service delivery, sales disintermediation (reducing the number of intermediaries between producers and consumers),and supply chain optimization. With these implementations, the likeliest solution platform is enterprise resource planning (ERP), the go-to toolset for purchasing, supplier relationships, cost management and profit forecasts, among other resource-intensive activities.

From the PLM-centric, inside-out point of view, PLM is the platform for integrating external entities into lifecycle processes. In PLM, external entities become collaborative add-ons to internal lifecycle process flows. In this view, digitalization exploits the myriad application architectures used in product development and throughout the product lifecycle to enable new, high-value business models.

The take-away’s here are two-fold:

The digitalization community must recognize the power and necessity of a fully functional PLM platform—specifically that end-to-end connectivity and optimizing business functions and entities throughout the lifecycle are foundational to realizing their business and digital objectives.

The PLM community must appreciate the digitalization community’s view that strategically reconfiguring business functions and entities (not just “integrating” them) can result in major new business value propositions.

Both communities must be able to see the significant opportunities that lie at the intersection of their two perspectives.

PLM’s place in the enterprise’s digital landscape

PLM-enabled digitalization requires a deeper understanding of PLM’s role in the enterprise’s digital landscape. If a PLM-enabling product innovation platform is a viable way to enable the digital transformation of the lifecycle, it is then fair to ask: What is PLM?

After 40 years of working in and around PLM-enabling technologies, solutions and toolsets, PLM should be viewed as an end-to-end strategic business approach—a highly developed set of business solutions that are both internally and externally consistent and used for:

• The collaborative creation, use, management and dissemination of product-related intellectual assets. Assets here means all product/plant definition information, i.e.: the virtual product.
• All product/plant process definitions, including virtual planning, designing, producing, operating, supporting, decommissioning and recycling/disposal.

Understood this way, PLM is far more than an engineering-oriented technology for product development. It’s an innovation platform that supports the extended enterprise and all of its needs from concept through end-of-life.

And bear in mind that innovation takes place not only during the enterprise’s countless transformation processes but also in the very definitions of the organization’s intellectual assets.

Next, we should clearly understand what intellectual assets are: All the components of the enterprise’s product and process definitions. This means all mechanical, electronic, software, formulas, recipes, specifications and documentation components, plus all the business, manufacturing and support process definitions that fall within the scope of the end-to-end lifecycle.

What is meant by “product”? Another sweeping definition: discrete manufactured products, of course—aircraft, cars and trucks, computers and software and medical equipment, as well as mundane things like pills, furniture, hats and coats, shoes and socks, foods and soda pop (canned or otherwise) and on and on.

Fast forward to the 21st century, where companies and their leaders can no longer afford to focus solely on traditional discrete products. Today, the demand spans a wide array of projects and assets, including:

• Construction projects: Buildings, hospitals, bridges and highways.
• Processing plants: Oil refineries and offshore drilling platforms.
• Infrastructure assets and facilities: Airports, railways, distribution systems and their associated equipment.
• And much more: Spacecraft, weaponry, ships and beyond.

These “products” defined in all their endless variety and adaptations as the organization’s intellectual assets in the endless, i.e., circular, product lifecycles of development, production, marketing and sales and usage, support, upgrades and maintenance plus research for everything that comes next. Within these lifecycles, PLM is tightly linked (and usually integrated) with ERP, SCM, CRM and other enterprise solutions, as well as a myriad of product-definition and creation tools (e.g., computer-aided design, engineering, and so on).

Considering the above, it’s no surprise that information technology (IT) has been divided into two readily distinguishable parts. One part supports deliverable assets in the form of physical products managed with ERP and other similar solutions. The other part supports intellectual assets in the form of virtual products managed with PLM.

In turn, this IT division is leading to the formation of three distinct domains within digital transformation—PLM and ERP, of course, but increasingly execution. Execution is an all-embracing term for getting more competitive and innovative products, systems and assets (physical or virtual) into customers’ hands sooner while lowering overall costs.

And I see refocusing digital transformation efforts toward execution as a way for enterprises to stop punishing their capital structures.

Thus, digital transformation—digitalization—can yield a truly integrated collaborative environment at the heart of every organization that works to solve all common problems. This, too, is an inflection point.

The post Where and how PLM fits into a digital transformation initiative appeared first on Engineering.com.

Don’t it always seem to go
That you don’t know what you’ve got ‘til it’s gone?
 — Joni Mitchell, Big Yellow Taxi, 1970

There once was a Golden Age for value-added resellers (VARs). In the business of distributing much-sought-after CAD software, all they had to do was wait for it to be sold nearby and they would get their cut. Then, every year afterward, they would collect maintenance fees. With CAD software being hard to learn, they could also cash in on selling training.

Oh, those were the days.

These days, most designers and engineers have CAD. If they need more licenses, they can bypass the VAR and get those licenses directly from the vendor. If they don’t know how to use a feature, they can Google it or watch a YouTube video — again bypassing the VAR. With VARs no longer guaranteed a cut of every nearby sale, it’s a whole new ball game.

“Anybody can sell to anyone now,” says Cameron Carson, SVP of Engineering at Hawk Ridge Systems, at the inaugural one-day Hawk Ridge Systems Partner Summit recently held in San Francisco. Carson has experienced tectonic shifts in the VAR landscape firsthand. Over the years, the disappearance of sales territories has been bad news for some and good news for others. Some VARs will hang on to the seat they have always enjoyed. Others will gladly remove their seat belts and feel free to move about the cabin. Being one of the bigger VARs, and getting bigger with each acquisition, Hawk Ridge Systems is well positioned to benefit from that freedom.

Not your father’s Hawk Ridge

The Hawk Ridge Systems Partner Summit combines the best of SolidWorks’ annual 3DEXPERIENCE World with the intimacy of a local user group. Instead of dozens of user stories, there’s a curated few with ample time between presentations and a format conducive to getting to know other users.

Attendees come from a diverse number of Silicon Valley firms. Some from big firms “my mother would recognize,” and also small firms doing engineering to order or reverse-engineering work.

It’s unusual for VARs to hold partner summits and invite the media, but Hawk Ridge Systems isn’t your usual VAR. They have taken the traditional role of the VAR and expanded upon it, building upon design and manufacturing services to try to understand design engineers’ business issues.

“We’re not the old Hawk Ridge. We’re not just selling seats of SolidWorks or doing simple PDM implementations,” says Carson, leading off the Summit. We’re looking to see how we can adapt and thrive in this environment alongside you. With the solutions provided we can arrive at that single source of truth. It was 20 years ago when I was in industry and hearing model-based information was right around the corner. We would move from paper to PDFs with a giant vault and BOMs. But we’re still not there yet, and today, engineers and designers are being asked to do more. Software is getting more sophisticated and capable while the IT departments are shrinking.”

Increasingly sophisticated and varied software presents a challenge for design and manufacturing teams. Even for companies as expert in software use as Knapheide (maker of truck bodies with their own presentation), some software is either too infrequently used or comes with such a steep learning curve that it’s best left to specialists.

Knapheide may have expertise in all things SolidWorks, but when it came time to program a custom solution for stacking their truck bodies, they called upon Hawk Ridge Systems. Hawk Ridge Systems’ stacking solution is neatly integrated into the SolidWorks interface and other customizations.

“We have expertise in specialties the design engineers don’t,” says Cameron.

Slow to solve  — and why that’s a good thing

Next up is Andrew Parkhurst, who conducts a master class in patience and listening.

“If I had an hour to solve a problem, I would spend 55 minutes studying the problem and 5 minutes solving it,” Parkhurst attributes this quote to Albert Einstein.

Studying a problem is more than just listening, nodding and taking notes. Parkhurst recommends a letter of understanding (LOU) to capture critical business issues.

“If we don’t take the time to delineate the problem and don’t know what we’re trying to solve, how do we know we’ve solved it?”

Hawk Ridge Systems offers the full breadth of the SolidWorks and Dassault Systèmes portfolio as well as third-party solutions, including HCL CAMWORKS, DriveWorks, 3D printing hardware from FormLabs, HP and Markforged and HP, laser scanners from Artec3D and Creaform, and more. Yet, Parkhurst’s discipline is rock solid. He keeps from blurting out an obvious solution to a business issue, as I would have.

For example, DriveWorks’ design engineers have gone wild with customization requests. Surely, Hawk Ridge Systems will offer them DriveWorks.

Instead, he suggests we help each other.

“Sometimes the solutions can be right in the room,” he says and encourages users to share their issues with each other.

And they do. Company A (a household name we can’t divulge) has an Excel infestation. Company G (another Silicon Valley giant) needs help with version control. Company T initially sourced most of its components but is now bringing its manufacturing in-house. All of them have problems moving data between data islands.

The room has been transformed into a lively discussion of a type not seen at big annual user meetings.

Parkhurst stays in listening mode, the Cheshire Cat that knows the secrets of Wonderland and will share them when the time is right. He sympathizes, repeating the engineers’ problems to assure them he understands.

“With CAD, PDM, PLM, CAM, CRM, ERP … engineers are drinking alphabet soup,” he says.

HI not AI

“How many have undergone an ERP implementation?” Cameron asks later. There’s a shudder in the room. It was not one of the city’s earthquakes.

Selling design and manufacturing software across industries should give Hawk Ridge Systems a unique perspective and insight into the broader world of a company’s business. They would’ve been able to observe what works and what doesn’t, especially among divisions of a company, each with its disparate systems and data silos.

Hawk Ridge Systems, privy to a vast pool of customer data, with a history of observing problems and offering solutions, is HI (human intelligence) over AI, but the concept is the same. Resellers have been deep learning on data, the same as AI does with neural networks. But resellers’ HI has one big advantage. They have seen business bottlenecks and blasted through them with software solutions, either off-the-shelf or custom-made, in a way AI still must learn.

Growing too fast for its own good

“How many companies have growth targets?” asks Parkhurst. All nod. After all, isn’t growth good?

Not necessarily, says Parkhurst. Can a company handle the growth? Are its users sufficiently trained? Can manufacturing scale? The more business systems and processes they have, the better off they are, right?

Not necessarily. Systems and processes can be suffocating. Parkhurst gives an example of a company that was acquired and forced to use the big company’s systems. They had a difficult time of it, Parkhurst states. It hindered their creativity and productivity.

An experts’ expert

The changing market for VARs has led to consolidation. Hawk Ridge Systems has been an active acquirer. “We must have had seven acquisitions,” says Cameron. It’s true. Since 2017, Hawk Ridge Systems has acquired:

1. Symmetry Solutions
2. Cimtronics Midwest
3. Parson Technology
4. Quest Integration
5. CAS
6. Design Point
7. Access Manufacturing Systems.

No longer confined to Silicon Valley (where it supports notable tech giants Amazon, Tesla and Google) Hawk Ridge Systems has created a VAR empire that stretches from coast to coast and North to South, with 26 offices in the US and Canada serving 33,000 SolidWorks users.

The post Your business is my business, says SolidWorks reseller appeared first on Engineering.com.

In this article, I am returning to fundamental questions that often baffle technology users—many times to the surprise of those who earn a living from technology. In an earlier article, I addressed some basic queries: What does PLM actually mean, how do I know if I need it and what questions should be asked to figure that out?

I want to start by taking a step back. These questions may seem inane and answering them may seem unhelpful. Still, they address challenges in comprehensive PLM implementations—the complexity of end-to-end connectivity and their digital threads, webs and networks—whatever they are labeled.

It’s a distressing fact that very few PLM implementations ever reach their ultimate projected goal. The reason is no mystery: implementers often do not push the project through to completion. They don’t “stay the course.”

A big part of the remedy is preventing key people from being peeled away to other major projects.

Big-picture context

The effort to ensure funding and resources for the three or more years needed to implement any major enterprise-class transformation must never be overlooked. The cost of a major transformation could add up to several million dollars—sometimes much more. The usual celebrations of incremental small wins persuade no one unless those wins are placed in a big-picture context.

As I have witnessed again and again, failure to keep everyone focused is the top reason why PLM implementations don’t scale up from the project and the business unit.

Maintaining staff and project management focus is also essential in bringing information technology (IT), operational technology (OT) and engineering technology (ET) together with PLM. While bringing IT, OT and ET together can also be done with enterprise resource planning (ERP) solutions, it’s not recommended, even if the enterprise is supply-chain intensive.

True to its predecessor, material resource planning (MRP), ERP focuses on enterprise resources—primarily money, people, orders, supplies and facilities. But as PLM users know, the enterprise (or a product or an asset) is much more than the sum of its resources and inputs. Digital twins of the enterprise must represent its structure—how its dozens (or hundreds) of moving parts are sustained, enhanced and kept in synch. Thus, a PLM representation of the enterprise focuses on the organizations It encompasses and the activities that support each line of business along with their relationships, internal stresses and all the frictions among them.

Thus, the digital twins of the enterprise are about much more than inputs and resources. For starters, the digital threads of enterprise-scale digital twins must connect to anything and everything that enhances competitiveness and profitability—or that threatens how they work together. Ensuring long-term sustainability in this way requires that any focus on factors external to the digital twins must be matched by a focus on the internals.

As for IT, OT and ET, PLM has increasingly accessible capabilities. These are now truly powerful business platforms that clarify and simplify complex challenges and, most importantly, ensure the timely delivery of world-class products to the marketplace.

This I see as Right-to-Market—the right product, to the right market, at the right time, with the right capabilities, at the right price. Enabled by PLM, Right-to-Market builds enterprise sustainability by ensuring that:

• Even the most competitive rivals can be outperformed.

• Marketplace presence is strengthened with collaboration and innovation that span the enterprise.

• Keeping customers happy is a central focus.

• Builds in long-term profitability for the enterprise—the justification for implementing every large-scale technology.

Right-to-Market is in sync with enterprise-class PLM. This means expanding the implementation of PLM until its digital twins represent the entire enterprise, not just its products and assets. PLM is already in common and profitable use to manage all the individual assets and systems that, along with people, make up the enterprise.

Right-to-Market means completing digital transformation and much more. This ensures that collaboration and innovation work closely with and build on each other and that they mesh smoothly with engineering, production, marketing and service, as well as connecting the enterprise’s IT, OT and ET process and technology environments.

Enterprise-class PLM is the only viable way to achieve this top-level integration, which is bringing IT, OT and ET together. No other solution or technology has the capabilities and resources necessary to support enterprise re-creation on this scale. And only in this way can marketplace rivals be outperformed.

Enterprise-class PLM and the transformations inherent in Right-to-Market lead to smarter evaluations, quicker interpretations of data and information and fewer errors. Enterprise-class PLM helps analysts and decision-makers see the big picture the marketplace presents, to reconcile conflicting viewpoints and to overcome resistance to change—all while silos of information are opened, connected and integrated.  

This brings me to the next question: How do we determine whether operations are big enough, complex enough, or if the company’s products and/or services are sufficiently complex to benefit from PLM?  

Essentially, this means taking an inventory of product, asset and system complexities and their viability. Here are a few useful indicators:

• The enterprise has sufficiently sophisticated products and assets to meet foreseeable customer needs.

• The connectedness and effectiveness of the information processing embedded in products with built-in electronics and supporting software.

• The growing need for products, assets and systems to accommodate change that is abrupt and all-pervasive.

This last point includes new physical, mechanical and materials capabilities; new production and service processes; built-in electronics and embedded information processing; artificial and augmented intelligence; evolving customer expectations and demands; a changing cast of aggressive competitors; and new opportunities in core markets and adjacent segments.

Following this effort, a concerted push may be needed to measure the sophistication of the enterprise’s assets and systems in terms of their competitiveness, profitability and sustainability. Bearing in mind that sophistication and complexity are inseparable, the focus here is:

• Determining and enhancing long-term sustainability and profitability of assets.

• Verifying the soundness of product and asset service lives in terms of marketplace shifts, changes in demand and competitive initiatives.         

• Quickly identifying and exploiting new marketplace opportunities.

• Identifying assets suitable for Product-as-a-Service (PaaS) business models.

• Finding profitable uses for under-utilized capabilities.

• Identifying and disposing of obsolete assets, ending the use of outdated processes and avoiding creeping obsolescence with its potential to ambush business plans.

If these analyses and inventories are done conscientiously, almost every organization will quickly see that it will benefit from implementing PLM or broadening its use.

When digital twins grow to support the enterprise, digital threads link them to everything in its marketplace, imposing huge demands on the breadth and depth of connectivity. On the other hand, less granularity is probably needed than what is customary for a product or asset, keeping the appropriate enterprise digital twins within manageable and usable proportions.

Moreover, digital twins, digital threads and their connectivity change endlessly, which points to a reality of PLM in its ultimate configuration—its need for ongoing support similar to the support invested in IT, ET, or OT.

Strategic business approach

Think about it: while the lifecycle of the enterprise is infinite, everyday product and asset models can quickly mushroom to intimidating proportions, requiring many digital threads and a great variety of connectivity. Hence, the meanings of “end-to-end” and “lifecycle” can change daily.

As every project manager knows, justifications can morph into expectations and then into benchmarks for progress and gauges of success, often with little leeway. Support inevitably wanes without meeting these gauges and measurements or at least acknowledging the constant updates and modifications. As anyone working in technology realizes, the passage of time requires more funding support and more staff resources, not less. This may seem obvious, but I find that the implications are often overlooked.

Change has become abrupt and all-pervasive—new physical, mechanical and materials capabilities; built-in electronics and embedded information processing; artificial and augmented intelligence; evolving customer expectations and demands; a changing cast of aggressive competitors; and new opportunities in core markets and adjacent segments.

As a long-time definer and observer of PLM, I do not doubt the viability of digital twins that are sufficiently robust to accommodate the entire enterprise. This is what I mean by Right-to-Market—ensuring the enterprise is optimally configured to enable and sustain long-term success in its marketplace(s). This ultimately means that the enterprise consistently maximizes its return on investment.

And so, the inevitable technology user’s question: can PLM solution providers’ tools support Right-to-Market? Yes, as evidenced by their nonstop development of new capabilities, simultaneous uptake of new technologies and ongoing accommodations to structural changes in marketplaces.

This is aided by using the Cloud in its broadest sense with the introduction of product-focused and technology-focused platforms easily connected to feed ever-larger and evolving digital twins.

Complexity often hamstrings implementations but getting a handle on it and “taming it” is why we take the risks that always accompany new technologies. Failure to implement exposes us to risks that, over time, can only worsen. And in today’s global marketplaces, basing decisions on “what has always happened” is riskier than ever.

This is why every organization needs to embrace PLM as the strategic business approach it is.

The post Why Every Enterprise Needs Its Own Digital Twins appeared first on Engineering.com.

Rivian and Volkswagen Group (VW) have announced a joint venture to develop next-generation software-defined vehicle (SDV) platforms for their future electric vehicles, with VW investing up to $5 billion, starting with an initial $1 billion. This collaboration aims to leverage Rivian’s advanced electrical architecture and software expertise to create a superior SDV technology platform. The venture will accelerate software development, enhance scalability, and reduce costs for both companies.

Through this partnership, both companies envision launching vehicles equipped with the new technology by the latter half of the decade, and VW will use Rivian’s existing platform in the short term. The joint venture is expected to finalize in the fourth quarter of 2024, pending regulatory approvals.

By pooling their expertise and resources, OEMs can accelerate collaborative return from innovation. Organizations can join forces in multiple ways. For instance, an established OEM can acquire or invest into a niche start-up. This can be achieved through creating a new legal entity like a joint venture (JV), investing into another organization to access given capabilities or capacity, or acquiring a competitor or a supplier to gain access to specific technologies. In this context, PLM strategies play an important role in realizing value from such co-innovation partnerships.

Let’s explore how PLM and ERP facilitate real-time collaboration, design reviews and iterative testing, ensuring swift integration of innovations into production.

How PLM powers collaboration, expansions and acquisitions

Organizations grow through both organic transformations and inorganic expansions. Inorganically, business acquisitions and venture capital investments present growth opportunities through access to new capabilities, markets and technologies. The initial challenges from acquisition are multifold and can be summarized in five key PLM questions:

1. How to leverage or scale one organization’s capabilities to drive value across one another?
2. How to drive synergies across organizations without damaging competitive advantage?
3. How to protect and expand on each organization’s intellectual property (IP)?
4. How to integrate PLM and ERP capabilities across two partner organizations to foster effective collaboration, building on each other’s strengths?
5. How to harmonize and consolidate business capabilities across parent and child entities, or across partnering entities, to remove duplication and address gaps?

Specific answers to these questions will depend on multiple strategic factors, including commercial agreements between companies. It’s also a matter of defining the relevant strategy to enable cross-organizational collaboration, drive business integration, leverage best practices across organizations, consolidate enterprise capabilities and ultimately seek simplification.

The first PLM capabilities to consolidate typically relate to core product and project data: from BOMs, materials, software, xCAD, quality standards, compliance requirements, business processes, supply chain integration—aligning processes and associated systems of record/engagement repository. Initial steps towards building a common PLM backbone often relate to data exchange alignment based on common formats and processes. Other the other side, ERP foundation includes aligning procurement, product costing, compliance, sustainability, financial and other core enterprise requirements.

How PLM fosters portfolio alignment and data protection

Partnering across organizations seeking to co-innovate implies a robust commercial alignment to capitalize on respective investments and related business commitments. Such partnership is often characterized by driving product portfolio synergies, sharing resources and value assets, sharing benefits and return on investments.

Business acquisitions and associated investments translate in five strategic perspectives:

• Market expansion: Acquiring businesses to expand into new markets or geographical areas to gain a competitive edge.
• Product diversification: Investing in acquisitions to diversify the product portfolio—including adding new product lines, enhancing existing products, co-developing new variants or product lines, or integrating complementary products to meet broader customer needs.
• Technology advancement: Acquiring businesses to gain access to new technologies, IP, or technical expertise—staying at the forefront of innovation and maintaining a competitive advantage in the market.
• Operational synergies: Focusing on acquisitions that offer operational efficiencies and cost savings—streamlining processes, achieving economies of scale, reducing redundancies and improving overall operational effectiveness.
• Strategic partnerships and alliances: Forming strategic partnerships or alliances through acquisitions to strengthen the company’s position in the industry—enhancing collaboration, share resources and drive mutual growth.

In PLM terms, this translates to integration of new market requirements and regulatory standards into the product development process. It involves managing a range of products and variations within a PLM ecosystem with broader access control to ensure cohesive lifecycle management. From a technical standpoint, stronger collaboration requires updating and integrating new technologies and knowledge into existing PLM frameworks to support innovation and product enhancement. Furthermore, it entails harmonizing processes and systems across merged entities to streamline operations and reduce PLM-related costs.

How PLM strategies support mergers and acquisitions

PLM strategies play a critical role in supporting business acquisitions by providing a structured framework for integrating and managing the combined entities’ product development processes. PLM value drivers contribute to business mergers and acquisitions in multiple ways:

1. Unified product data management: PLM systems consolidate product data from both acquiring and acquired companies, ensuring consistency and accessibility. This unified approach reduces data silos and enhances collaboration across teams.
2. Streamlined product development: By integrating the product development processes of both organizations, PLM strategies ensure that best practices are shared and adopted, leading to more efficient and innovative product development cycles.
3. Enhanced compliance and quality control: PLM processes help manage compliance with industry standards and regulations by maintaining comprehensive records of materials, processes, and product specifications. This ensures that all products meet quality and regulatory requirements.
4. Efficient change management: PLM strategies facilitate effective change management by providing tools to track and manage changes in product design, development, and production. This helps in quickly addressing any issues that arise during the integration process.
5. Improved resource utilization: PLM processes enable better resource planning and utilization by providing visibility into the capabilities and capacities of both organizations. This leads to optimized use of resources and improved operational efficiency.
6. Accelerated time-to-market: By harmonizing processes and leveraging system synergies, PLM strategies can significantly reduce the time required to bring new products to market. This is crucial for maintaining a competitive edge in fast-paced industries.
7. Innovation and IP protection: PLM systems ensure that intellectual assets from both organizations is protected and leveraged effectively, from products to data assets, processes, resources, etc. First and foremost, this fosters innovation by providing a secure environment for collaboration and knowledge sharing.
8. Scalable and flexible integration: PLM strategies provide scalable and flexible integration frameworks that can adapt to the evolving needs of the business. This ensures that the integration process is smooth and can accommodate future growth and changes.

By implementing robust PLM strategies, organizations can effectively manage the complexities of business acquisitions, driving value creation, innovation, and long-term success. This involves continuous simplification and consolidation, balancing control and flexibility to ensure cohesive operations while maintaining agility to respond to market changes and new opportunities.

Consultants often debate the challenges of PLM and ERP implementations as they are complex by nature, but these changes present opportunities to learn and break the status quo. This is especially crucial for companies serving multiple customers through hybrid PLM ecosystems or those aiming to enable mergers and acquisitions by building modular, “plug-and-play” processes, data flows and systems.

Robust PLM strategies align product development processes, leveraging the combined strengths of acquiring and acquired companies to unlock new value streams, enhance productivity, and reduce time-to-market. They provide a collaborative platform fostering continuous improvement and creativity, while ensuring access to a unified knowledge base and shared IP. Effective data management, compliance adherence, and quality control within PLM frameworks lay a strong foundation for sustainable growth, maintaining high product quality and meeting regulatory requirements.

The post PLM and ERP foundations to successful mergers, acquisitions, JVs appeared first on Engineering.com.

Jaguar Land Rover (JLR) was acquired by Tata Motors Ltd. (TMC) in 2008 for about US$1.5 billion (£1.15 billion). Following a two-year separation process from Ford Motor Co. (FMC), initiated its new Product Lifecycle Management (PLM) journey in partnership with Dassault Systèmes. The initial vision was for a full scope transition from a very disparate ecosystem inherited from Ford and previous mergers to an integrated PLM architecture with 3DExperience at its foundation.

Perhaps independently of its PLM strategy, JLR reported strong results in the previous financial year. As of end-March 2024, JLR declared record Q4 and FY2024 revenue of $9.9 billion (£7.9 billion) and $36.6 billion (£29.0 billion) respectively, with profit before tax of $836 million (£661 million) and $2.7 billion (£2.2 billion) for the same periods—the highest since 2015.

Building a credible, holistic PLM strategy and associated implementation roadmap from the ground-up is no easy task. It often translates to multi-year business transformations that must be championed at the board level. For JLR, it has been a 15-year journey in the making. It initiated with a greenfield vision in 2010 which gradually evolved into a hybrid bluefield approach to mitigate transition risks while addressing technical readiness and adoption gaps. A bluefield approach implies combining greenfield and brownfield elements.

Contextualizing it to the JLR story, this post discusses why solving complex PLM challenges requires Agile-based problem solving, adaptive change management and realistic strategic realignment to cope with unpredictability and uncertainty.

Reality check: from greenfield to bluefield

Greenfield PLM implementation strategies often initiate from bold/unconstrained ideas, sometimes combined with unqualified wishful thinking and the need for speed (e.g., unrealistically fast deployment ambitions). When left unvalidated, this approach adds challenges in successfully completing digital transformations—particularly in the context of PLM and other enterprise solutions which carry high-level of ambiguity. Such challenges typically relate to a mix of 5 characteristics:

1. Unrealistic expectations: overestimated benefits and underestimated complexity often fueled by senior leadership’s lack of appreciation and ownership of the PLM scope.
2. Inadequate planning and preparation: amplified by insufficient analysis and the lack of a detailed roadmap due to various technical and business unknowns.
3. Unexpected resistance to change: underestimating organizational ability to embrace change, underestimating communication and training needs.
4. Resource misallocation: budget overruns and inadequate skillsets, assuming existing experts can handle new technologies without external hiring or upskilling.
5. Neglecting delivery risk management: failure to monitor and adjust—wrongly assuming the initial plan will work perfectly without the need for ongoing real-time adjustments based on feedback and evolving circumstances.

JLR’s initial PLM strategy was crafted in 2010; it candidly aimed at a perfect alignment, seeking to connect all enterprise capabilities together following a series of greenfield solution deployments. It later realigned its strategy to refocus on an integrated engineering/development toolset—the product data management backbone of innovation—with BOM, CAD, CAE and virtual twins management at its core.

Legacy coexistence and technical debt

Transitioning from the Ford legacy PLM ecosystem was certainly a significant endeavor for JLR. Similar to Volvo Cars, JLR faced several knowledge gaps in managing the complexity and technical debt it inherited from its previous parent company. With hundreds of tools and customized systems, the integration and data migration landscape required selective dual-track solution development and concurrent support to facilitate the transition. The situation was amplified by different practices and core data sets used across two distinct brands, Jaguar and Land Rover.

Progress was at last reported in 2019, by John Kitchingman, who at the time lead EuroNorth at Dassault Systèmes: “Just over 10 years after the project was initiated, the company has finally rolled out a first solution that covers an entire vehicle program: the Defender model. There is still a way to go, but the good news is that this rollout finally happened.” The hard reality is that it took JLR more than twice the time it initially planned to finally roll out a first solution of 3DExperience that covered an entire vehicle program. The journey with Dassault Systèmes continues as JLR now seeks to complete the deployment of 3DExperience across all vehicle programs worldwide. Only then, JLR would be able to initiate the decommissioning of the remaining components of its redundant Ford-based legacy systems.

There is clearly no single quick fix, silver bullet or magic wand that can address all challenges associated with heavily customized and poorly integrated PLM legacy. Limiting old-new solution coexistence and minimizing customization are typical objectives of every PLM initiative. The last mile of JLR’s PLM transformation will consist of scaling its 3DExperience adoption across the entire product portfolio, finally closing the door on years of functional transition and data migration. Subsequently, this will open the door to further opportunities for enterprise capability improvements.

Leveraging digital twins

JLR clearly aims at end-to-end collaboration around its PLM backbone, reaching cross-functionally beyond design and concurrent engineering supply chains. JLR said that “More than 18,000 users across all JLR business areas and suppliers will make use of virtual twins to increase efficiency, improve production management, save time and reduce waste and costs.”

Laurence Montanari, Vice President, Transportation & Mobility Industry, Dassault Systèmes, remains optimistic about hitting the next significant milestone in this 15-year transformation journey: “JLR is utilizing the 3DExperience platform to enhance its virtual twin experience, creating software-defined vehicles that seamlessly integrate both hardware and software development. […] After five years of partnership, we are opening a new era of collaboration beyond engineering and manufacturing through a trusted partnership, where teams from JLR and Dassault Systèmes work closely in short iterations to address JLR and its ecosystem’s challenges.”

The post How Jaguar Land Rover handled 5 major PLM challenges appeared first on Engineering.com.

Paris-based Valeo manufactures mobility technologies, providing transportation electrification, lighting, driving assistance and connected solutions to improve user experience onboard vehicles. The company has been using software solutions from Dassault Systèmes for decades, from Catia for design and CAD engineering, to Envia MatrixOne for its product lifecycle management (PLM) and Delmia for digital manufacturing.

In June 2024, Valeo announced a partnership with Dassault Systèmes to upgrade its legacy PLM systems to the 3DExperience platform. More specifically, “Valeo will rely on Dassault Systèmes’ Global Modular Platform and Smart, Safe & Connected industry solution experiences based on the 3DExperience platform to accelerate the digital transformation of the Group’s research and development activities.” While the benefits of this digital transformation are substantial, it is important to address the potential challenges and risks associated with such a significant technological overhaul.

Upgrading legacy PLM is no small feat

Despite the undeniable advantages of maintaining an evergreen system, the process is riddled with potential pitfalls. A poorly executed major implementation can cripple a company, while even a successful one often spans years and demands substantial resources. This is a critical aspect that frequently escapes the spotlight in discussions about digital transformation.

These upgrades are not only about software, but also about the people involved, including business change, testing and training. Transitions typically involve not just a technical upgrade but are often coupled with a fundamental shift in workflows, processes, data model evolutions and readaptation of configurations and customizations. Legacy systems, deeply entrenched in a company’s operations, come with their own set of complexities and dependencies. Replacing them requires meticulous planning, robust change management strategies and a clear vision to avoid operational disruptions.

Even when migrating to the latest PLM release within a single vendor ecosystem, the process is far from straightforward. Companies might assume that staying within one ecosystem simplifies the transition, but the reality can be more complex. Newer systems often introduce advanced functionalities that necessitate retraining staff, reconfiguring integrations and revisiting data governance policies. This learning curve can be steep, especially for teams accustomed to older systems. Therefore, it is imperative for organizations to approach such migrations with a strategic mindset, ensuring they have the necessary support and expertise to navigate the transition smoothly.

Upgrading from one software version to another typically brings challenges related to legacy data migration, system integration, de-customization, re-customization, change management, user training and cost of ownership. However, the advent of software-as-a-service (SaaS) solutions provides a promising avenue to mitigate these risks. SaaS platforms are designed to minimize these complexities by offering scalable, flexible and continuously updated environments. Key questions remain about the frequency of upgrades, the capacity to realign configurations and integrations and the ability to adapt to change.

The imperative of digitalizing R&D

Despite the challenges, the need to digitalize R&D activities is more pressing than ever when driving product innovation. The benefits of a well-executed digital upgrade can be transformative, outweighing the risks associated with implementation. For Valeo, deploying Dassault Systèmes’ 3DExperience platform is a strategic investment aimed at future-proofing its operations in an increasingly competitive automotive landscape.

As an existing user of Catia, Enovia and Delmia, one might expect a straightforward upgrade process within the Dassault Systèmes portfolio. The press release does not specify if Valeo is considering transitioning to a SaaS version of 3DExperience or a more traditional cloud-managed implementation. Nevertheless, it likely involves a pre-configured industry solution tailored for the automotive sector, compatible with ISO 26262, ASPICE and MBSE development.

By connecting thousands of users across various departments into a cohesive virtual ecosystem, Valeo is not merely upgrading its tools but reimagining its approach to innovation. The PLM platform’s ability to capitalize on legacy data to drive intelligent decision-making is a game-changer. It ensures that every step of the R&D process is informed by the most accurate and up-to-date information, thereby accelerating development cycles, driving margin improvement and reducing costs. Companies like Valeo must stay ahead of the curve by continuously innovating and optimizing their R&D processes. As a Tier 1 supplier to the automotive industry, Valeo likely shares data across multiple PLM systems when interfacing with its OEM customers.

Christophe Périllat, CEO of Valeo, emphasized the significance of this partnership, stating, “At Valeo, we are proud to be the key innovation partner of our clients. Our more than 20,000 engineers develop innovative solutions combining hardware and software and leveraging AI to make tomorrow’s mobility safer and more sustainable. Thanks to our partnership with Dassault Systèmes, our teams will have more efficient solutions enabling digital continuity to support our world-leading R&D activities.”

Navigating the path forward

For Valeo, the partnership with Dassault Systèmes represents a critical step towards establishing itself as a tech-driven leader in the automotive industry. The 3DExperience platform is set to streamline Valeo’s R&D activities, offering a robust foundation for developing next-generation mobility solutions.

Pascal Daloz, CEO of Dassault Systèmes, succinctly captured the essence of this transformation, highlighting, “Creating new mobility usages and universes of experiences requires proven capabilities for styling, electrification and software-defined vehicles. Our 3DExperience platform is this differentiator. It leverages the power of generative AI to connect models from science to data from experience, along the full lifecycle of the vehicle.”

Interestingly, Valeo has also been using Teamcenter through its Powertrain Systems business which integrated Valeo Siemens eAutomotive (VSeA) in 2022. This illustrates the complexity and breadth of Valeo’s digital transformation efforts. It also poses several questions regarding the group’s PLM ecosystem:

• Will it transition to a SaaS platform to leverage future upgradeability?
• How much integration and re-customization are expected and how complex will the transition to 3DExperience be?
• Will Valeo use this opportunity to converge its PLM landscape towards a consolidated platform based on 3DExperience?
• Alternatively, will the group continue to use multiple PDM and PLM systems in the foreseeable future?

These questions underscore the challenges and strategic decisions Valeo faces as it embarks on this significant technological overhaul.

The post The promises and pitfalls of modernizing PLM appeared first on Engineering.com.

Product development is a complicated, ever-changing process. Engineers come up with a design, break it and iterate until products are optimized. Product validation, however, doesn’t have to be so tricky. With the right cloud-based platform, engineering leadership can implement workflows to simplify the documentation, communication, verification and validation of a constantly evolving product development.

Engineers and designers need an effective development process to address these challenges. With the 3DEXPERIENCE platform on the cloud, organizations can implement a product review and validation process that increases multidisciplinary collaboration, visibility and traceability while reducing product development costs and time to market.

In this 13-page white paper, we delve into the benefits of collaborative product review and validation via the 3DEXPERIENCE platform. You will learn:

• The challenges to traditional product development and validation.
• Benefits of a cloud-based engineering platform.
• How to achieve product validation success.

Your download is sponsored by Dassault Systèmes.

The post Product Validation Challenges Require a Cloud-based Engineering Platform appeared first on Engineering.com.

In the complex world of engineering and manufacturing software, Bill of Materials (BOM) always stands out as a basic element in information delivery. Regardless of the lifecycle stage (design, engineering, manufacturing, maintenance), BOM always stands for the data that describes the product information. Each element of the product development cycle can be represented by a specific Bill of Materials type. The roots of Bill of Materials are going a long way back to the time when the first MRP/MRPII systems were introduced. Back in those days, BOM was considered a document that contained all the information needed for the product (either digital or physical). As the systems in product development and manufacturing are deeply intertwined, the question about how to organize engineering, manufacturing and supply chain information is triggered more often.

The History Of Multiple BOM Types

The concept of BOM classifications has evolved from various origins. In the early days, when the BOM resided solely within MRP II (Manufacturing Resource Planning), detailing raw materials and components needed, it was simply referred to as a BOM or a parts list in drawings. However, as companies streamlined their processes and adopted different design and data management tools, the necessity for distinct BOM types emerged. PLM (Product Lifecycle Management) systems swiftly embraced EBOM, while ERP (Enterprise Resource Planning) software systems leaned towards MBOM. The “as-x” notation increasingly denoted a product’s stage of maturity. This is how companies end up with a various “BOM type soup” – DBOM, EBOM, MBOM, SBOM, PBOM, xBOM. In addition to that, sometimes BOM management used the prefixes to indicate the source of data such as “electronic” (EBOM), “mechanical” (MBOM), “software” (SBOM) and others, which created an additional level of complexity.

What significance do these names carry? Are they merely labels, or do they carry substantial meaning? On one hand, they are indeed terms, providing flexibility in product data models and reflecting various stages in product development, lifecycle maturity, and organizational processes. Yet, in the complexity of process management across business, engineering and manufacturing realms, achieving terminological coherence is vital.

How Many BOMs Do We Need?

The history of BOM management is still around us and this is a reality of data and BOM management these days in many engineering teams and manufacturing companies. As such you can see places where BOMs still live naturally because it is part of the traditional data management practices.

MRP and ERP solutions

You need an accurate BOM to build a product, buy off the shelf components and order custom parts and assemblies. Therefore BOM information is a critical part of every MRP or inventory management system. It’s a manufacturing bill or sometimes called planned bill of materials to support procurement and production process. It’s a list of all materials and components that are required to manufacture a finished product. It’s very tactical and usually doesn’t contain a good way to trace what happened in the past. A typical manufacturing bill of materials is “effectivity based”, which means you can see how it looks for a specific day. It is tightly integrated with planning and inventory items and functions.

The roots of all BOM management and single bill of materials in every manufacturing organization are coming back to the foundation of MRP systems. A bill of materials (BOM) is a critical component of a manufacturing resource planning (MRP) system. It is a list of all the materials and components that are required to manufacture a finished product. There are several reasons why BOM is important for MRP: Planning and Scheduling, Costing, Quality Control, and Traceability. Therefore MRP BOM is not very useful for engineering functions. Read 5 Books To Start Your BOM Management Education to learn more. Inventory management was a core element of MRP, therefore, it was good for manufacturing BOM and supply chain management, which later became part of ERP solutions.

BOM IN DRAWINGS (Part List or just BOM)

This is another place where BOM lives and has a long history. The drawing part lists existed for as long as drawing and documents were standardized for manufacturing. It is (still) the most widely accepted method to release information from engineering to manufacturing. There are several standards to regulate how to do so. For example, ASME Y14.5 defines the practice of including bill of materials (or part lists). In electronic and electrical engineering, the IPC-2581 standard provides some guidelines for manufacturing data and documentation, including the use of a parts list that specifies the assembly components. Similar requirements can be found in architecture, construction, HVAC, and electrical drawing for construction. All CAD packages provide support to create part lists and bills of materials. For many engineers, it is the first place where they meet BOMs.

BOM IN PDM and PLM (EBOM and multiple BOMs)

Historically, PDM was born to manage CAD files. This is still one of the most frequent use cases for PDM (Product Data Management Systems). However, with the extensions of PDM functions, the question of how to include a good visualizations and list of all components become important. With the introduction of 3D CAD systems, the need to manage 3D components and assemblies triggered the need to keep traceability between component updates and BOM (part list) in the drawing. Most PDM systems do a decent job keeping these lists in sync. PDM systems usually provide a decent mechanism of integrating data between components and drawings. This is probably the mainstream BOM solution for all small and medium size companies. And it is a root cause of a lot of inefficiencies in data management.

However, as the complexity of products was growing the need to have an independent from 3D representation of product structure emerged. Introduction of systems including mixed mechanical, electronics and software components emerged and so the need to have an EBOM (engineering BOM) that includes all information collected from multiple design teams. This information is needed to review the engineering solution and release it to manufacturing. In this case, EBOM plays the key role in the engineering release process such as ECO.

Future advancement of PLM systems and the need to manage product structure (information) that describes the product in different stages of its lifecycle (development, manufacturing, maintenance, etc), created the need for more than one BOM to represent different product structures and data. Such data structures are also well aligned with the organization of work in manufacturing companies (engineering BOM, manufacturing BOM, maintenance BOM) – each BOM structure belongs to a specific organization and sometimes can be managed by multiple tools.

Creation of multiple BOMs became a continuous place for a battle between multiple enterprise software providers – PLM, ERP, MES, CPQ, etc. Each software provider including engineering and design software, ERP and others, used in the manufacturing process claimed a specific “BOM silo” such as engineering bill, manufacturing bill, service BOM, etc. All together it created a concept of xBOM and the need to keep this information “in sync” (will talk about it later)

One BOM vs Many BOMs?

One of the most frequent debates in the BOM management discipline is around how many BOMs is needed to manage information in a consistent and connected way.

What is behind this conflict? The reality of engineering and manufacturing is to include multiple disciplines and organizations. Each discipline defines its needs to how to structure and present the information. Also, lifecycle and changes are impacting the process. A specific production data (BOM) can be different from a specific revision of Engineering BOM (EBOM). Alternate components required for assembly, but they are not even mentioned in the engineering structure and this information is not even needed for engineers. The level of conflicts is different and depends on the industry, level of complexity, and product development organization. Organizations want to have control of the data. Having multiple BOMs (eg. Design, Engineering, Manufacturing) can be a solution to solve this problem, but at the same time the complexity of the process and potential mistakes on data synchronization forces companies to rethink the approach that is used for each specific organization.

To create multiple BOMs (eg. EBOM and MBOM) is the simplest approach that exists today in the industry. Each system keeps the BOM (sometimes multiple BOMs) and organize a synchronization process between these structures. The names can be different, but essentially means the segmentation of phases (As-Design, As-Planned, As-Built, etc.) or organizational ownership (Engineering, Manufacturing, etc.). In some situations, the segments are caused by the companies, contractors, and suppliers’ boundaries. Each BOM (product structure) is managed as a separate entity. The coordination and change process includes many synchronization and updates to keep consistency between multiple BOMs. It completely solves the problem of ownership and introduces huge complexity of reconciliation, traceability, and validations.

The idea of a single (one) BOM is interesting and worth discussion. One one side, to think that multiple organizations can change their needs and present the information in one way would be too amateur. You can see such efforts sometimes when companies are either small or too centralized and IT and business architectures decide to get a single system to manage all information.

But in a practical sense it is not happening very often. But, if you apply a “data management” thinking, the idea of a single BOM has some merit, because it basically creates a single data structure that is capable of managing all aspects of product information.

The idea of a single BOM that provides a centralized place to manage all product information, connected to all data sources leads to the approach that is called “multi-view” BOM. It is a modified version of a single BOM approach, which mainly focuses on how to segment a specific organizational or process representation from a single BOM. This option is considered a viable alternative to the first option (Multi- BOM) that creates too many difficulties for reconciliation, changes, and synchronizations.

Conceptually, this model is the best because it can reduce the data redundancy and eliminate the data synchronization points. However, this option put a heavy load on the need for the organizations to agree about single data representation and the ways to extract a segmented data representation (View).

From Multiple BOMs to Digital Graph Model

Although usage of multiple BOMs is still the mainstream method to organize product information today, I can see a trend towards creating a holistic digital product model. The main problem with traditional model is heavy orientation on documents (eg. BOM in drawings or Part Lists in MRP) and siloed data representation. In this model, customers are oriented towards documents and perceive product information through the lens of multiple BOMs. This approach, while familiar, has inherent limitations. It often leads to data redundancy, inconsistencies, and challenges in maintaining synchronization across different departments. Although it is still the mainstream model used by most of PLM vendors, it has many disadvantages. The siloed nature of this model hinders the seamless flow of information, thereby impacting efficiency and adaptability.

The idea of xBOM that can integrate multiple data representations and keep them in sync, demands evolution and improvements. The idea of “digital twins” that grows beyond just simulation is the one that I like. While the name “twin” can be debated, the rationale I can see behind it is to rely on a digital model that represents product information including engineering, planning, production, maintenance, etc. in a connected form and can be used by multiple teams and departments.

What can be better than the xBOM model? The adoption of modern data architectures and data management systems is leading the industry towards creating a new holistic digital product model. This model use modern data modeling paradigms (eg. Graph Model) to encapsulate a rich set of information about the product connected together.

Such a model is capable of encapsulating multiple facets of product data (multiple BOM structure) – combining sales, requirements, engineering, manufacturing, and maintenance. A holistic product model will support the creation of a unified view of the product life cycle, enabling better connection and process flow across various lifecycle stages of product development. This approach streamlines processes, reduces errors, and enhances the overall efficiency of product management.

The technological foundation of such a model can be a graph model, which can provide a robust representation of multiple data assets and create a flexible set of relationships. Product knowledge graph is a way to take PLM development and bill of materials data model from siloed engineering bill, manufacturing bill, service and maintenance all together to model engineering and product development process combined with production process and rest of lifecycle stages to present an accurate BOM all way down from requirement to maintenance.

Management of bill of materials has a long history of data management starting from the early days of design using drawings with ink and placing part list on the drawing, using MRPII models to create a single production BOM and advancing towards creation of complex multiple BOM models to support as-design, as-engineering, as-manufactured and as-support models. All together it contributed to the creation of the xBOM model that was dominant for the last decade to support the EBOM/MBOM dual model. At the same time, digital thread is a model that allows the connection of multiple BOMs in the xBOM model in a connected data set to support traceability and coordination of processes. The next step in multi-BOM process development is to establishing digital models (eg. product knowledge graph or digital twin) to manage all aspects of product information and to support integrated business systems development. We go from a single BOM to multiple BOMs to a single digital model and future advantages of BOM copilot for better management of product information.

The post Navigating the Maze of BOM Types: A Glimpse into Future PLM appeared first on Engineering.com.

Digital transformation is increasingly recognized as critical to overcoming the data complexities that frustrate every organization. In my opening remarks I stressed that successful digital transformation requires keeping an eye on the evolving trends and enablers—CIMdata’s Critical Dozen.

I also pointed out that:

• Maximum PLM value will only result from a holistic, end-to-end approach to connectivity, strategies, and tactics provided they appropriately address people, process, and technologies. Nearly all PLM failures result from not following through and not staying the course.
• Organizational change management will be required; it always plays a critical role in maximizing the adoption of new processes and technologies, and delivering bottom-line value.
• Evolving customer demands and market opportunities are motivating investment in comprehensive product lifecycle digitalization.

I outlined the major focal points, including enterprise application architectures, configuration management, extending bills of materials (BOMs) through bills of information (BOI) structures, model-based structures, the Internet of Things (IoT), Big Data and analytics, augmented intelligence, digital skills transformation, organizational change management and, of course, digital twins and digital threads.

Each of this year’s presenters, whether from the private or public sector organizations, are hands-on in some aspect of the digital thread. Several presenters were from CIMdata’s Aerospace and Defense PLM Action Group (AD-PAG) member companies, celebrating its tenth year under the leadership of James Roche, CIMdata’s A&D practice director.

Keynoting was David Genter, director of systems design engineering for Cummins Inc.’s Accelera unit, which was formed to leverage design for sustainability (DfS) by “developing a design optimization culture.” Cummins, based in Columbus, Ind., produces more than 1.5 million engine systems a year and is driving a decarbonization transition as it refocuses from diesel engines to alternative-fuel internal combustion engines; fuel-cell, battery-electric, and hybrid powertrains; and electrolyzers that generate hydrogen and oxygen from water.

Genter stressed “moving analysis to the left,” which means using analysis early and often to engineer sustainability into designs from the start. He cited DfS test cases saving more than $1.4M in the first year by removing non-value-added material in the designs of engine mounting support brackets and exhaust manifolds. When a commitment is made to early analysis for material-use optimization, he noted a typical 10% to 15% material savings, a five-fold return on DfS engineers’ salaries and big improvements from right-first-time design results. 

“Addressing climate change is daunting but DfS is not!” Genter observed. Cummins is committed by 2030 to reduce greenhouse gas emissions by 50%, water use by 30%, waste by 25%, organic compound emissions by 50%, new product Scope 3 emissions (indirect but related) by 25%, and generating circular lifecycle plans for every new part.

Optimizing designs with right-first-time techniques can seem “tough” when “everybody is already overwhelmed with the other work,” Genter said, but Cummins has verified that DfS need not add any organizational burdens or stretch normal design times for new products—quite the opposite.

Several presenters addressed the elimination of paper documents. Robert Rencher, a senior systems engineer and associate technical fellow at Boeing, shared some numbers and described a highly successful solution. In his work with the AD-PAG, he has probed airlines’ continuous checks of aircraft and the U.S. Federal Aviation Administration (FAA) Form 8130-3 Authorized Release Certificate (ARC).

Form 8130-3, the FAA’s airworthiness approval tag, is the airline industry’s good-to-go certificate. It is ubiquitous and mandatory for every aircraft inspection and every new or repaired part.

Rencher’s numbers were eye-openers. He reported that a single FAA 8130-3 may require many as 600 engineering and inspection documents, and documentation for a single bolt can add dozens of pages. One large U.S. airline conducts over 200,000 aircraft checks every year, he said, and:

• 90% of this documentation is still handled on paper.
• The labor cost to file one document was $20 in 2020.
• Between 2% and 5% of these documents “are lost or misfiled.”

In his presentation, which was delivered on behalf of the AD PAG and draws on the experiences of all the members, including Boeing and Airbus, Rencher said Airbus can now electronically generate and verify all the info needed for an 8130-3; this is establishing a digital data exchange for aerospace quality certification. Digitizing this documentation could save the aerospace industry €80 million annually in Europe alone, with over €20 million in annual savings already reported.

Rencher summarized that combining the digital thread with distributed ledger technology has proved to be a success: users of 8130-3 gain the digitization, traceability, provenance and accessibility of part data across the part’s product lifecycle from design to final disposition.

Rencher also covered AD-PAG’s benchmark study on furthering the use of PLM’s digital twins and threads. In this effort’s fifth phase, AD-PAG is working with numerous industry standards bodies, Rencher reported. The intent is to gain consensus and acceptance of results, increase the brainpower in the project, increase AD-PAG’s leverage with PLM software providers, and testing PLM digital twin/thread definitions with more than 20 distinct use cases.

AD-PAG’s other focus is on the Systems Modeling Language (SysML) as an enabler of model-based systems engineering (MBSE). In an update, Chris Watkins, principal engineer, MBE/MBSE at Gulfstream Aerospace Corporation, who is the AD-PAG MBSE project leader, reported on open, standardized application program interfaces (APIs), common ontologies, and new SysML tool providers. He highlighted persistent shortcomings in syntax, notations, and interoperability plus ambiguities and “poor support” for Universally Unique IDs that promise to address many challenges. The AD-PAG is addressing these in a follow-on project phase.

McDermott International Ltd. is actively driving the use of PLM in the engineering, procurement and construction (EPC) industry which regularly joins discrete and process capabilities in its multibillion-dollar energy infrastructure projects; these are a tough challenge for any digital technology. Houston-based McDermott designs and builds on land, at sea, and underwater worldwide.

Jeff Stroh, McDermott’s senior director of digital and information management systems, said every McDermott project has millions of documents and digitalization is increasingly urgent. Many documents are from Microsoft Office, but many more are from other digital tools that are not integrated or only partially so—and they reference many sources.

Challenges abound, Stroh stressed. The re-use of prior document content is limited in EPC, and quality controls are “purely manual,” so change identification and management are difficult. EPC projects rely heavily on offline commenting and mark-ups that must be manually processed, he pointed out. Documents and sources are disconnected, so EPCs have no effective way to “bake in” lessons learned.

As yet there are no effective processes for knowledge management amid nonstop additions, deletions, clarifications and replacements, Stroh added. Nor are there any ways to link content from engineers’ applications to a project’s narrative documents.

Stroh, too, had some eye-opening numbers. In EPC, clients supply thousands of documents but McDermott’s engineering deliverables mushroom to many multiples of that; moreover, each individual document goes through multiple revisions and approval cycles. In one project, the client made nearly 60,000 comments on more than 3,000 documents, he noted, all of which had to be responded to.

Project manhours have ballooned in recent decades, he continued, and the EPC business climate has become very risky, as “cost-plus” contracts are gone and now many contracts are done on a fixed price.

To cope, McDermott engineers need data that is available and usable in their tools and applications; data that reduces the friction in work and finding answers; that drives collaboration and visibility across tools, functions, and disciplines; and that augments the workflow, “not interrupt it.” Legacy approaches, “voluminous narrative documentation and manual processes are not fit for the fast-paced world…”

Finally, Stroh commented that this was McDermott’s second PLM try. The first failed because it focused on “document management instead of reimagining processes based on data and information management.” Success in this new implementation is also tied to adopting the users’ terminology into the processes and tools rather than expecting users to adopt the tools’ terminology.

Mabe, a Mexico City-based white-goods manufacturer, presented its efforts to get new products to market faster, extend and improve product families, reduce business complexity and improve productivity across the organization. With eight factories, Mabe annually produces nearly 10 million refrigerators, ranges and other appliance. General Electric Appliances is Mabe’s largest reseller.

Speaking were Gabriel Vargas, director of engineering systems, and Maria Elena Mata Lopez, leader, technical information and modular architecture.

Through digitalization, automation and a modular product family architecture enabling the automation of product configuration, Mabe’s benefits were dramatic, according to Vargas and Mata Lopez. The resources needed for new product introductions fell by 90%, Mata Lopez reported, and for product maintenance 80%, she added; effort to manage BOMs was reduced by 80%; parts counts for ranges fell by 60% and refrigerators 42%. Cost management was speeded up and made accurate, she added.

Digital transformation leaders from General Electric Aerospace, the Gulfstream Aerospace Corp., and Moog also spoke at the conference.

A persuasive case for using artificial intelligence (AI) in product design was made by Uyiosa Abusomwan, senior global technology manager for digital engineering at the Eaton Corporation. He asked whether AI could be used to optimize product design “the way nature optimizes each new creature?”

Addressing the application of AI to supply chains, Abusomwan pointed out that AI can identify features, extract parameters and put them in 3D CAD formats. As an example, he noted, data can be fed into quality management solutions and into PLM so failures and causes are detected and not repeated; Eaton is already doing this.

For AI to be used to optimize designs, he continued, open minds are needed along with new data management tools, which can cut across design solutions including PLM, and an understanding of the values being sought. Abusomwan predicted that third-party solution providers in PLM and IT will be buying up start-up companies that offer these capabilities.

The conference’s second day focused on the public sector with presentations from or about the U.S. Defense Department’s Research and Engineering, the Defense Acquisition University, the Naval Surface Warfare Center, NASA, and the U.K. Ministry of Defence. All addressed digital transformation issues and their agency’s progress.

In a keynote presentation given by Daniel Hettema, director of digital engineering, modeling, and simulation in the Office of the Undersecretary of Defense for Research and Engineering. His office is “getting into systems engineering big time” and has launched a review of the DoD’s modeling and simulation policies.

The DoD, he noted, has 700,000 employees, over 1.4 million contractors, and 709 policies that address some aspect of digital model creation. Because of its size and complexity, “DoD doesn’t make big moves but…we can move the needle with small wins” such as leveraging digital technologies and using (newly) optimized customer models.

Government agency responsibilities are so much broader—and fundamentally different—than any private-sector organization’s responsibilities. So, the public-sector presenters focused on policies and guidance rather than solutions and processes.

Most presenters at this year’s PLM Road Map & PDT zeroed in on the many different challenges in digital transformation and individual companies’ priorities in tackling them through PLM and some form of digital engineering. They all reported solid successes and rapidly evolving plans to complete their transitions.

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