What should engineers consider as they select AI solutions to realize AI’s business benefits, often as part of their digital transformation, and exercise some AI oversight?

Companies often digitally transform operations, production or logistics when implementing AI applications. Through advances in generative AI, machine learning, big data analytics, and automation, companies benefit from efficiency, safety, and environmental sustainability.

Most companies will be better off rolling out vendor AI solutions as they become available rather than designing and implementing an entirely new and exceedingly ambitious custom AI application.

Practical AI applications in most industries are not new systems. Instead, AI functionality adds value to existing applications through:

• More confident or accurate results.
• Reduced elapsed time to achieve the results.
• A significant reduction in staff effort to achieve the results.
• Increased scope or scale in factors such as the number of customers, products, inventory items or larger geographic coverage.
• Additional functions that couldn’t be achieved without AI.

More specific and detailed selection criteria are more likely to satisfactorily select an AI solution that will satisfy the scope of the planned AI project. More high-level, general and conceptual selection criteria will make differentiating the available AI software difficult. This situation will lead to selecting AI software based on appearances, such as marketing presentation quality, website appeal or impressions from the AI vendor demo, rather than capability.

The primary selection criteria engineers should consider when selecting AI solutions for their digital transformation projects are these.

Functional requirements

Functionality selection criteria consider the AI software’s fit against the planned digital transformation application’s requirements.

AI application control

When selecting an AI solution, deciding on a Software-as-a-Service (SaaS) or On-premises solution is the first consideration. These two alternatives vary significantly in the amount of control your company will have over the AI application.

A Software-as-a-Service (SaaS) solution offers these advantages:

• Pay-as-you-go on a monthly or annual basis.
• There is no computing infrastructure to operate.
• The AI vendor handles all software and data updates.

An On-premises solution offers these advantages:

• Total control over software, data, performance and application availability.
• Complete data privacy with no opportunity for the AI vendor to use your company’s data for model training or to sell it to others.
• Opportunity to build a smaller, focused, custom AI model for more accurate results, fewer hallucinations and faster performance.

Currently, most companies choose a SaaS AI solution because they don’t have the AI skills and computing infrastructure to build and operate an on-premises AI solution. However, this situation will likely change as companies build AI experience and want to tightly control their AI model and proprietary data for competitive advantage.

Problem fit

Engineers should consider whether the AI software can generate the desired output, typically text, images or audio, that aligns with their specific application requirements. Understanding the capabilities and limitations of proposed AI solutions in relation to the problem at hand is crucial for successful adoption.

Define specific problems or opportunities the AI solution will address as test cases. Perform the test cases for each proposed AI solution to differentiate them.

Accuracy

As engineers assess the accuracy of AI model outputs, they should consider related criteria such as the following:

• Quality of the generated outputs.
• Ability to generalize to different inputs or scenarios.
• Consistency of results.
• Frequency of hallucinations or inaccurate or misleading results.

Specify the desired level of accuracy for the AI solution’s output and test to establish whether or not the candidate AI software can achieve this level.

Non-functional requirements

Non-functional selection criteria consider the AI software’s quality attributes, such as security, usability, and scalability.

Performance

Engineers evaluate the performance of AI software to ensure that it meets their desired response time.

AI software accesses a large language model (LLM) to formulate the required response to prompts. That process can take time. If the desired response time cannot be achieved, the actions to consider include:

• Rerun the test with similar but revised prompts to see if the response time changes materially.
• Rerun the test with another AI software offering.
• Build a smaller LLM that is more focused on the problem space.
• Confirm that there are no network bottlenecks between the workstation and the AI solution.

Scalability

An increasing number of end-users will likely access the AI application over time. Also, the number of prompts a given end-user will issue will increase over time.

To handle that growth, the AI application must scale well. Scalability criteria include:

• Performance remains the same even though the number of prompts increases.
• The quality of outputs does not deteriorate.
• The associated cost increase is acceptable.

Ethical considerations

To responsibly adopt AI solutions, engineers must consider ethical implications. They should consider factors such as:

• Data privacy protection.
• Fairness, as opposed to bias, in the output.
• The risk of potentially harmful or unethical uses.

Ethical considerations can be revealed by testing the candidate AI solutions.

Security and compliance

As engineers assess the various AI solutions, they should evaluate each potential AI solution’s security measures and compliance features.

Vendor criteria

Vendor selection criteria consider the capability and likely performance of the AI solution vendor. Vendor evaluations for AI solutions for digital transformation are risky because:

• Predicting the likelihood of AI vendors being absorbed by a merger or failing is difficult.
• Most vendors are so new that they have little track record with customers.

Vendor expertise

Engineers assess each AI vendor’s experience and capabilities in the relevant AI domain for their planned application.

Vendor support and maintenance

Evaluate each AI vendor’s support and maintenance services, most likely through customer references.

Customization and flexibility

Determine each AI vendor’s willingness and ability to adapt their solution to specific needs. If the evaluation suggests that customization will be required, that AI solution should be dropped from further consideration.

Costs

The cost selection criteria engineers consider when designing AI solutions include licensing, implementation, and operating costs.

License cost

Compare the license cost of the various on-premises AI solutions.

Implementation cost

Compare the implementation cost of the various AI solutions. The implementation cost includes:

• Computing infrastructure upgrades.
• People change management.
• Revisions to business workflows.

If the AI solution uses your company’s proprietary data, there will be significant data preparation costs.

Operating cost

Compare the operating and maintenance costs of the various on-premises AI solutions.

Compare the monthly usage cost of the various SaaS solutions.

Business case

The business case for the alternative AI digital transformation solutions will vary. The easiest choice is always the alternative with the strongest business case based on tangible benefits. However, there are always intangible benefits to consider, such as customer satisfaction or contribution to the strategic plan. The final AI solution recommendation should consider tangible and intangible benefits.

Allocating more effort to select an AI solution with more specific and detailed selection criteria will reduce risk and position the planned AI digital transformation project for success.

The post Making smarter AI choices appeared first on Engineering.com.

Hexagon AB’s potential spin-off of its Asset Lifecycle Intelligence and Safety, Infrastructure & Geospatial divisions, the company has announced the new business will operate with the business name “Octave.” The company says the new name communicates its intent to increase, accelerate, and optimise customer outcomes.

In addition to Hexagon’s existing Asset Lifecycle Intelligence and Safety, Infrastructure and Geospatial divisions, Octave will also include ETQ (currently operating under the Manufacturing Intelligence division) and Bricsys (currently operating under the Geosystems division).

This new business will be a pureplay software and SaaS company focused on helping customers make smarter, more data-driven decisions. Octave’s portfolio will help customers design, build, operate and protect assets more effectively, enabling clearer insights and better incident response.

“As we prepare for the potential separation from Hexagon AB, Octave will be a powerful identity to reflect the significant growth opportunity,” said Mattias Stenberg, current President of Hexagon’s Asset Lifecycle Intelligence and Safety, Infrastructure & Geospatial divisions and incoming Octave Chief Executive Officer. “As a separate, stand-alone company Octave will have the depth, scale, and expertise necessary to capitalise on software and services opportunities across the industrial and public sector spaces and deliver intelligence at scale.”

Collectively, Octave had approximately 7,200 employees as of the end of 2024, and revenues of approximately $1.654 billion (EUR 1,448 million).

If approved by stakeholders, it is the Hexagon Board’s current expectation that the separation and listing process will be completed in the first half of 2026.

“The good thing about Octave is that it’s fully integrated. It’s one company with one go-to-market strategy, one sales force. It’s not just a collection of companies. That’s the 1+1=5 effect,” said Stenberg at the press conference. “We’re already bringing together ALI and ETQ. We’re well on our way there. That will take maybe six months to a year. Bricsys is more of a standalone unit for now. It’s very much an online-sold product and operates a little differently, so that will take more time. It’s a bigger business.”

The separation, spin-off and listing remain subject to this ongoing process and final approval of the Hexagon Board and shareholders, as well as being subject to other conditions, consents and regulatory approvals.

Hexagon leadership cautioned that there can be no assurances that a separation, spin-off or listing will occur.

— With files from Ian Wright

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Throughout my career, I have had exposure to several types of legacy software in different companies and industries. Organizations deal with the idea of transforming to a digital platform in one of three ways: they view the legacy software as crucial and must be integrated (high resistance), they keep using it in parallel to a digital approach regardless of the high cost, or they transition completely to a digital system, which is surprisingly the least common approach.

During my time working for a global automotive company, I encountered a semi-collaborative approach to data sharing and working together. The main problem for engineering was working together in a private ecosystem. This was caused by several factors— different rules and regulations at each location, legacy software and a legacy mentality driven by several acquisitions that were never fully integrated. Our north star became the migration to a digital platform that bridged the divide and got the locations to work together. This approach was ultimately successful, and members of the organization could easily work on their projects from any location.

 Of course, issues appeared with the transition to a digital thread, including numbering schemes and streamlining or adapting processes to local needs. I learned that it is very easy to fall down the rabbit hole if you entertain every little detail. Instead, your main drive should always be to the agreed scope. During this transition, I had to quell a lot of debates on minor things that could have derailed the scope of our projects, and there were a lot of projects in the initial phase, such as our desired outcomes from moving to a complete digital thread, which software to migrate or discontinue, vendor selection and many others.

Indeed, it’s worth taking time at the beginning to design your solution as thoroughly as possible—it saves a lot of headaches down the road and most importantly, saves money. The role of an engineer in this specific spot is to balance out the budget with features. First and foremost, in this role you must bridge the divide between design and manufacturing, this was one of the first things that I learned as I was cutting my teeth in my first engineering job. You can design a product or a solution as neat as possible, but at the end you must produce it and to produce a product is a whole other beast than just drawing it on your computer. Understanding both the design component and having a surface understanding of how the product is manufactured gave me enough credit with the shopfloor people that I became the go to person for the head of manufacturing to present their topics and work with them to be able to incorporate them in the implementation process.

One of the most important but frequently ignored topics is user acceptance. People who are working with a specific software are usually SME (subject matter experts) and know the software in detail. Because of this, it can be tough to gain buy-in, but they are your most important asset in a legacy software to digital thread transformation. They have depth of knowledge that is critical to a successful migration or transition. Who knows LS outputs? Who knows how the processes were designed? Who knows which person down the process needs to be informed? The subject matter expert will make your life a thousandfold easier, so include them as early as possible, align on scope and have them help you build it.

If I were to choose one thing to avoid at all costs during a digital transformation, it would be ignoring parts of the organization. My success in this project was a result of the frequent consultation with the people handling day-to-day business of the organization. Since we started with several locations during ramp up, we ended up working very closely with people from all over the production process. This resulted in rapid feedback on anything that we did—especially on what we did wrong. That feedback is crucial, as we could incorporate it and adapt from sprint to sprint.

Legacy software is still present in many companies, but it should not be seen as malign pieces of a process that would kill a project before it starts. Rather, it was an important piece of the puzzle that fit the organization at a specific time in its existence, and as organizations mature and digital becomes the new norm, legacy software should be considered an important aspect of a migration scenario, even if it will ultimately be replaced.

Andrei Lucian Rosca is an engineer with a bachelor’s in mechanical engineering focusing on CAD software with more than 10 years of experience in Digital Transformation projects in several industries, from automotive to consumer goods. I am currently exploring innovative solutions (e.g. IoT, AI) and how to include them in future projects.

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What is an Automated Building Management System (BMS)?

An Automated Building Management System (BMS) is a centralized system that manages and controls various building systems and operations in a manufacturing facility. These systems typically manage areas like heating, ventilation, and air conditioning (HVAC), lighting, energy consumption, security, water management, and access control. BMS integrates these functions into a cohesive platform, allowing real-time monitoring, control, and optimization.

By using sensors, automation, and data analytics, BMS helps facilities make informed decisions, improve efficiency, and monitor the overall health of building systems. In terms of sustainability, BMS acts as a vital tool for reducing resource consumption and minimizing a facility’s environmental footprint.

The backbone of sustainability

Energy usage is one of the most significant costs in manufacturing, and managing it effectively is crucial for achieving sustainability. A BMS enhances energy efficiency by automating the control of heating, cooling, and lighting systems to ensure that energy is used only when necessary.

Real-time energy monitoring and control

Through continuous monitoring of energy use, BMS systems can detect energy inefficiencies and automatically adjust operations to minimize waste. For example, lighting systems can be automatically dimmed or turned off in unoccupied areas, and HVAC systems can adjust their operations based on the real-time temperature and humidity levels within the facility. These energy-saving measures help ensure that energy consumption is minimized, especially during non-peak hours when the facility may not be fully operational.

Demand-response and peak load management

One of the key features of an advanced BMS is demand-response capabilities, which enable the system to respond dynamically to fluctuations in energy demand. By adjusting the use of HVAC, lighting, and other systems, the BMS can reduce energy consumption during peak hours when the grid is under strain. Manufacturers can use this feature to avoid high energy costs and reduce their environmental impact by using energy more efficiently during critical times.

Load shedding and optimization

BMS also helps to manage peak demand through load shedding—reducing non-essential energy consumption during periods of peak demand. This can significantly reduce the overall energy consumption of the facility while maintaining operational efficiency.

Manufacturing facilities, particularly those that rely heavily on energy-intensive processes, contribute significantly to carbon emissions. In addition to improving energy efficiency, BMS plays an integral role in helping manufacturers reduce their carbon footprint.

Renewable energy integration

Many manufacturing facilities are now incorporating renewable energy sources, such as solar panels or wind energy, into their operations to further reduce their carbon impact. A BMS can play a crucial role in managing the integration of these renewable energy sources. By prioritizing renewable energy usage when available and balancing it with grid electricity, the system ensures that the facility uses as much clean energy as possible, reducing reliance on non-renewable resources.

Water conservation and waste management

Water is another crucial resource that many manufacturing facilities rely on for processes such as cooling, cleaning, and production. As water conservation becomes increasingly important, a BMS helps manage water usage effectively. BMS systems track and monitor water usage in real-time, alerting facility managers to wasteful practices or water leaks. By identifying areas where water is used inefficiently, such as excess cooling or leaky connections, manufacturers can take steps to reduce consumption. Additionally, BMS can provide automated control over water-based systems, ensuring that they are used optimally throughout the day.

For facilities with on-site wastewater treatment systems, a BMS can assist in monitoring and controlling wastewater processes, ensuring they run efficiently. By optimizing water treatment, BMS systems can help ensure that water reuse is maximized, contributing to sustainability efforts and lowering operational costs.

Predictive maintenance and preventing waste and downtime

Manufacturing facilities rely heavily on machinery and equipment to keep operations running smoothly. However, poorly maintained equipment can lead to inefficiencies, increased energy consumption, and even premature failure. BMS plays a critical role in predictive maintenance, which helps avoid these issues and contributes to long-term sustainability.

Using real-time data analysis, BMS systems can identify potential issues with equipment before they become critical, enabling proactive maintenance. This minimizes unplanned downtime, reduces the need for emergency repairs, and extends the lifespan of equipment, reducing the need for replacement and cutting down on waste.

Predictive maintenance also ensures that equipment runs at peak efficiency, avoiding overuse of energy due to malfunctions or inefficiencies. This can significantly reduce energy waste and lower overall energy consumption, directly supporting sustainability efforts.

Compliance with green building standards

As sustainability regulations become more stringent, manufacturers are often required to meet specific green building certifications, such as LEED (Leadership in Energy and Environmental Design) or BREEAM (Building Research Establishment Environmental Assessment Method). A BMS helps facilities stay compliant with these standards by providing valuable data on energy consumption, water usage, and overall environmental impact.

Pathway to a sustainable future

In the quest for sustainability, manufacturers must leverage technology to optimize resource usage and minimize waste. Automated BMS are an essential tool in achieving these goals, offering real-time monitoring and control of energy, water, and equipment. By automating critical systems, BMS help manufacturers reduce their carbon footprint, lower energy costs, and improve operational efficiency.

Monitoring and managing resources efficiently through BMS gives manufacturers a competitive edge, not only by helping them meet regulatory standards but also by reducing operational costs and contributing to a more sustainable future. As we move toward a greener industrial landscape, BMS will play a pivotal role in driving sustainability efforts and ensuring that manufacturing operations are as efficient and environmentally responsible as possible.

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Let’s explore the last five of the ten most common AI risks and how to mitigate them. To read about the first five, click here.

Inadequate AI algorithm

The AI algorithms available to build AI models vary widely in scope, quality and complexity. Also, project teams often revise the algorithms they’ve acquired. These two facts create a risk of using an inadequate or inappropriate AI algorithm for the digital transformation problem.

Business teams can reduce their risk of using an inadequate AI algorithm by testing algorithms from multiple sources for:

• Desired outputs using well-understood training data.
• Software defects.
• Computational efficiency.
• Ability to work with lower quality or lower volume of data.
• Tendency to drift when new training data is added.
• Explainability.

AI algorithms are a family of mathematical procedures that read the training data to create an AI model.

Inadequate AI model

The risk of an inadequate AI model can result from many factors. The principal ones are an inadequate AI algorithm, problematic rules and insufficient training data.

Business teams can reduce their risk of using an inadequate AI model by testing the model repeatedly using the following techniques:

• Fine-tuning model parameters.
• Functionality testing.
• Integration testing.
• Bias and fairness testing.
• Adversarial testing using malicious or inadvertently harmful input.

The AI model is the object saved after running the AI algorithm by reading the supplied training data. The model consists of the rules, numbers, and any other algorithm-specific data structures required to make predictions when the model uses real-world data for production use.

Insufficient understanding of the data elements

Some data elements or features always impact AI model results more than others. When a project team does not sufficiently understand which data elements influence model results more than others, the situation creates the risk of:

• Inaccurate tuning of the AI algorithm.
• Disappointing or misleading model outputs.

Business teams can reduce their risk of misunderstanding data elements by:

• Testing how dramatically model results change in response to small changes in value or distribution of values of specific data elements.
• Confirming that similarly named data elements across the data sources are identical or not to avoid misunderstanding the data element meanings.
• Ensuring that the data quality of the most critical data elements is the highest.

Data elements are columns in a relational database.

Inadequate team competencies

Given the high demand for AI and data science talent, it’s common for digital transformation project teams not to have all the technical competencies they’d like. Inadequate team competencies create the risk that the quality of AI model results is insufficient, and no one will recognize the problem.

Business teams can reduce the risk of inadequate team competencies by:

• Proactively training team members to boost competencies.
• Assigning enough subject-matter expertise for the various data sources to the project team.
• Engaging external consultants to fill some gaps.

The required project team roles and related competencies are likely to include:

• Business analysts.
• Data scientists.
• Subject-matter experts.
• Machine learning engineers.
• Data engineers and analysts.
• AI architects.
• AI ethicists.
• Software developers.

Insufficient attention to responsible AI

In their enthusiasm for digital transformation project work, the team often neglects responsible AI even though they are not acting unethically. Responsible AI is about ethics. Ethics is an awkward, abstract topic for project teams.

Business teams can reduce the risk of insufficient attention to responsible AI by:

• Scoping of your fairness and bias assessment work based on the sensitivity of the data you will use.
• Investigating the provenance of external data sources.
• Evaluating the compliance and bias of external data.
• Engaging with AI ethicists during design and testing.
• Conducting a fairness and bias assessment of AI model results.
• Designing a process to monitor AI model results regularly for compliance and bias once the AI application is in routine production use.

If you come to believe that the team is consciously acting in an unethical way, it’s time to fire people.

The OECD principles for responsible stewardship of trustworthy AI are:

• Inclusive growth, sustainable development and well-being.
• Human-centered values and fairness.
• Transparency and explainability.
• Robustness, security and safety.
• Accountability.

When engineers proactively identify and mitigate AI risks in their digital transformation projects, they will deliver the planned business benefits.

The post How engineers can mitigate AI risks in digital transformation – part 2 appeared first on Engineering.com.

In my opening presentation, I confronted these issues by positioning PLM as a strategic business approach, thereby joining it to digital transformation, which has been CIMdata’s focus for more than four decades. And in the conference’s thought leadership vignettes, multiple PLM solution providers stressed connectivity and new tools to aid understanding and comprehension; in these vignettes, many supported my positioning of PLM.

The issues of comprehension were presented to conference attendees from several points of view. Many presenters delved into data and information quality—accuracy, completeness, structure, ownership, possible corruption, its exploding volume, and the steady growth of regulation.

Some numbers that made many attendees uncomfortable:

• There are hundreds of engineering software tools and new ones appear every week. Every engineering organization uses dozens of tools, systems, solutions, “apps,” and platforms; their constant updates are often disruptive to users

• About 800 standards apply to engineering information and its connections to the rest of the enterprise, said Kenneth Swope, The Boeing Co.’s Senior Manager for Enterprise Interoperability Standards and Supply Chain Collaboration

• 30 terabytes of data are generated in CAD and manufacturing for each of the hundreds of engines produced by Rolls-Royce PLC every year, reported Christopher Hinds, Head of Enterprise Architecture. Some output files from CFD analyses exceed 650 GB per part, he added.

Speakers also discussed how digital transformation is revealing the shortfalls in comprehension of data and information. “If we can’t agree on what data is, we can’t use it,” observed Swope. These shortfalls are caused by accelerated product development, shorter product lifecycles, and an explosion of product modifications and differentiations thanks to the software now embedded in every product.

In my conference-opening presentation, “PLM’s Integral Role in Digital Transformation,” I stressed that companies need to think beyond digitizing data, that merely converting analog data to digital isn’t enough. Yes, digitalization is at the core of an organization’s digital transformation … but moving to a digital business requires rethinking many organizational structures and business processes as well as understanding the growing value of data.

So how does PLM fit into this? Only by seeing PLM as a strategic business approach can its depth and breadth in the reach of digital transformation can be comprehended. PLM concentrates the organization’s focus on the collaborative creation, use, management, and dissemination of product related intellectual assets—a company’s core asset. This makes PLM the platform for integrating external entities into lifecycle processes—thereby enabling end-to-end (E2E) connectivity … and the optimization of associated business functions and entities throughout the lifecycle.

Don’t forget, I cautioned, that the data generated from your products and services often becomes more valuable than the products themselves. Why? Because product data touches all phases of a product’s life, these digital assets play a central role in an enterprise’s digital transformation. Hence I warned that digital transformation will collapse without the implementation of the right set of data governance policies, procedures, structure, roles, and responsibilities.

Many presenters also noted how PLM and digital transformation are helping them deal with the challenges of stiffer competition, rising costs, downward pressure on pricing, customer demands for more functionality and longer service lives, data-hungry Artificial Intelligence (AI), and Product as a Service (PaaS) business models

And while all these factors aggravate the issues I addressed, speakers expressed confidence that they will eventually reap the benefits of PLM and digital transformation—starting with getting better products to market sooner and at lower cost.

Another challenge with digital transformation and comprehension is the multitude of ways that presenting companies organize and identify their engineering systems and functions. All these manufacturers use basically same processes to develop and produce a new product or system but these tasks are divided up in countless ways; no two companies’ product-development nomenclature are the same.

Sorting this out is crucial to the understanding and comprehension of the enterprise’s data and information. Gaining access to departmental “silos” of data is increasingly seen as just the beginning of digging information out of obsolete “legacy” systems and outdated formats.

In the conference’s Day One keynote presentation, Martin Eigner of Eigner Engineering Consult, Baden-Baden, Germany, spoke on “Reflecting on 40 Years of PDM/PLM: Are We Where We Wanted to Be?” The answer, of course, is both yes and no.

Dr. Eigner expressed his frustration in PLM’s fragmented landscape. We are still tied to legacy systems (ERP, MES, SCM, CRM) that depend on flawed interfaces reminiscent of outdated monolithic software, he pointed out. As digitalization demands and technologies like IoT, AI, knowledge graphs, and cloud solutions continue to grow, the key question is: Can the next generation of PLM solutions meet the challenges of digital transformation with the advanced, modern software technologies available?

“The vision of PLM till exists,” Dr. Eigner continued, “but the term was hijacked in the late 1990s while the PLM vision was still being discussed. Vendors of product data management (PDM) solutions applied the term for their PDM offerings” which “mutated from PDM to PLM virtually overnight.”

“Ultimately,” he noted, “business opportunities and ROI will be significantly boosted by the overarching Digital Thread on Premise or as a Service,” leveraged with “knowledge graphs connected with the Digital Twin.” Applying “generative AI can optionally create an Omniverse with enhanced data connectivity and traceability.”

This stage of digital transformation, he summarized, “will improve decision making and support AI application development.” In turn, these “will revolutionize product development, optimize processes, reduce costs, and position the companies implementing this at the forefront of their industries. And we are coming back to our original PLM vision as the Single Source of Truth.”

The challenges of getting this done were addressed by Michael Carlton, Director, Digital Technology PLM Growth at GE Aerospace, Evendale, Ohio, using what he termed as “developing a best-in-class Enterprise PLM platform to increase productivity and capacity amid rising demands for digital thread capabilities, technology transformation, automation, and AI.” His remedies included “leveraging AI, cloud acceleration, observability, analytics, and automation techniques.”

“Uncomfortably ambitious productivity improvements,” Carlton continued, include “reduction in PLM environment build cycle time, parallel development programs on different timelines, shifting testing left (i.e., sooner), improved quality throughout, automated data security tests, and growing development capacity.”

The issue of PLM and the boardroom was raised in a presentation, by John Snow, Research Director, Product Innovation Strategies, at International Data Corp. (IDC), Needham, Mass. In his data-packed Day 2 keynote, Snow detailed how complex this issue is and the “disconnect between corporate concerns and engineering priorities.”

PLM, observed Snow, “maintains the digital threads that define the product ecosystem: weaving together product development, manufacturing, the supply chain, and service to balance cost, time, and quality.”

The opportunity for engineering in the boardroom is that “80% of product costs is locked in during design,” however, the Cost of Goods Sold (COGS) is 10X to 15X higher than Cost of R&D (CR&D), Snow explained.

“Poor product design,” Snow continued, “has an outsized impact on COGS, but good design does,” too. Thus, “increasing the engineering budget can have a big impact on profits (if properly allocated).” Current efforts to leverage design for manufacturing & assembly (DFM/A) are falling short,” he added.

Near the other end of the corporate size scale from GE Aerospace is Hollister Inc., Libertyville, Ill., an employee-owned medical supplies manufacturer of ostomy and continence products. Stacey Burgardt, Hollister’s Senior Program Manager for PLM, addressed PLM implementation challenges in her presentation on “The Role of Executive Sponsorship in PLM Success at Hollister.”

Burgardt, formerly R&D and Quality Leader, outlined Hollister’s PLM vision as three transformations:

• To product data centric from document centric

• To digital models from drawings, and

• To live collaboration and traceability from systems of record.

In her appeal to sponsors, Burgardt estimated total expected benefits through 2030 at $29 million. This sum included significant gains from improved efficiency of associates, smaller software costs, and reduced waste, scrap, and rework.

Unlike every other presenter, Hollister has yet to implement PLM, though not from lack of effort dating back to 2018. Hollister is currently finalizing PLM solution selection and planning. Burgardt focused the need for executive sponsorship and strategies to secure it. “Identify the right executive sponsors in the governance model including the CEO and CFO,” she said, “and the

leaders of the main functions that PLM will impact, and someone who has seen a successful PLM who can advocate.

“Be persistent,” she concluded, “and be adaptable.” Address sponsors’ concerns and “If it’s not the right time, keep the embers burning and try again.”

And this led to my conference summation topic: sponsorship. The fact that PLM and digital transformation are now recognizably tougher and will take longer than once hoped led to my Executive Spotlight panel discussion at the end of Day 2: “The Role of the Executive Sponsor in Driving a PLM Transformation.” My four panelists agreed high-level sponsorships are indispensable … and we discussed how to identify, enlist, and maintain those sponsorships.

To conclude, looking back over the two days’ presentations, I think the answer is “yes” to my questions in the first paragraph. And the sooner this rethink gets going the better.

The post In the rush to digital transformation, it might be time for a rethink appeared first on Engineering.com.

CoT as structured engineering reasoning

Chain of Thought formalizes what expert engineers intuitively do: breaking complex problems into logical, sequential steps that can be examined, validated, and improved. Enhanced with AI partnership, this structured reasoning becomes scalable organizational intelligence rather than individual expertise.

At its core, leveraging AI is about mastering the art of questioning. The transformation occurs when engineers move from asking AI “What is the solution?” to guiding AI through “How do we systematically analyze this problem?” This creates transparent reasoning pathways that preserve knowledge, enable collaboration, and generate solutions teams can understand and build upon.

As such, here is a reusable CoT template for technical decision-making:

“To solve [engineering challenge], break this down systematically:

1. Identify core constraints: [performance/cost/regulatory requirements],
2. Analyze trade-offs between [options] considering [specific criteria],
3. Evaluate effects on [downstream systems/processes],
4. Assess implementation risks and mitigation strategies.”

This template works across domains—thermal management, software architecture, regulatory compliance—because it mirrors the structured thinking that defines engineering excellence.

Practical applications in product innovation

CoT methodology proves most powerful in early-stage ideation, complex trade-off analysis, and compliance reasoning where traditional approaches miss critical interdependencies. Based on the target persona, this can translate in various use cases, such as:

Early-stage product ideation:

“To develop [product concept], systematically explore: 1) User pain points and current solutions, 2) Technical feasibility and core challenges, 3) Market positioning and competitive advantage, 4) Minimum viable approach to validate assumptions.”

Engineering trade-off analysis:

“When choosing between [options], evaluate: 1) Performance implications on [key metrics], 2) Cost analysis including lifecycle expenses, 3) Risk assessment and failure mode mitigation, 4) Integration requirements and future modification impacts.”

Compliance and regulatory reasoning:

“To ensure [system] meets [requirements], structure analysis: 1) Requirement mapping to measurable criteria, 2) Design constraint implications, 3) Verification strategy and documentation needs, 4) Change management for ongoing compliance.”

These frameworks transform AI from answer-generator to reasoning partner, helping engineers think systematically while preserving logic for team collaboration and future reference.

PLM integration—CoT as a digital thread enabler

CoT becomes particularly powerful when integrated into Product Lifecycle Management (PLM) and related enterprise resource systems—creating data threads that preserve not just what was decided, but why decisions were made and how they connect across development lifecycle. Just imagine these scenarios:

Design intent preservation:

“For [design decision], document reasoning: 1) Requirements analysis driving this choice, 2) Alternative evaluation and rejection rationale, 3) Implementation factors influencing approach, 4) Future assumptions that might affect this decision.”

Cross-functional integration:

“When [engineering decision] affects multiple disciplines, analyze: 1) Mechanical implications for structure/thermal/manufacturing, 2) Software considerations for control/interface/processing, 3) Regulatory impact and verification needs, 4) Supply chain effects on sourcing/cost/scalability.”

Digital thread connection points:

• Link design decisions to original requirements and customer needs.
• Connect material choices to performance targets and compliance requirements.
• Trace software architecture to system-level performance goals.
• Map manufacturing choices to cost targets and quality requirements.

This ensures that when teams change or requirements evolve, critical decision reasoning remains accessible and actionable rather than locked in individual expertise. From a business outcome perspective, this can contribute to continuity across product generations and reduce time spent retracing design decisions during audits, updates, or supplier transitions.

Strategic reality: revolution or evolution?

While CoT methodology delivers measurable improvements, the strategic question remains whether this represents fundamental transformation or sophisticated evolution.

Evidence for transformation: Though evidence remains scarce, early adopters of structured CoT approaches report measurable improvements in knowledge transfer efficiency, design review effectiveness, and decision consistency. Organizations consistently cite enhanced team collaboration, reduced rework cycles, and improved knowledge retention when engineering reasoning becomes explicit and traceable. These patterns suggest systematic capability enhancement rather than marginal improvement.

Case for evolution: Critics argue CoT merely formalizes what competent engineers have always done. Revolutionary breakthroughs—the transistor, World Wide Web, breakthrough materials—often emerge from intuitive leaps that defy structured frameworks, suggesting excessive systematization might constrain innovation. Regardless, the accelerating sophistication of AI demands that engineers critically assess not just what they build, but how they think.

Strategic balance: Successful engineering organizations are not choosing between structured reasoning and creative innovation—they are developing meta-skills for knowing when each approach adds value. CoT excels in complex, multi-constraint problems where systematic analysis prevents costly oversights. Pure creativity dominates breakthrough innovation where paradigm shifts matter more than optimization.

Future-proofing perspective: As AI capabilities accelerate from text generation to multimodal reasoning to autonomous design, organizations building frameworks for continuous methodology evaluation—rather than optimizing current techniques—will maintain competitive advantages through technological transitions.

Chain of Thought may represent the beginning of engineering’s AI integration rather than its culmination. The methodology’s emphasis on explicit reasoning provides tools for navigating technological uncertainty itself, perhaps its most valuable contribution to engineering’s digital future. CoT may be the missing link between today’s prompt-based AI assistants and tomorrow’s agentic co-engineers—moving from reactive support to proactive design collaboration.

Whether revolution or evolution, CoT offers engineers systematic approaches for amplifying problem-solving capabilities in an increasingly AI-integrated technical landscape.

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Volkswagen’s recent strategic moves highlight a company at the crossroads of transformation. On one hand, VW is making bold investments in AI-driven engineering and forging strategic alliances to position itself as a leader in next-generation automotive innovation. On the other, it faces the stark realities of large-scale execution—rising manufacturing costs, operational challenges, new electric vehicle (EV) entrant competition, and financial pressures.

To stay competitive, VW has embraced generative AI, digital twins, and software-defined vehicles. Announced in December 2024, its partnerships with PTC and Microsoft to develop Codebeamer Copilot aims to revolutionize Application Lifecycle Management (ALM) with AI automation. Meanwhile, the adoption of Dassault Systèmes’ 3DEXPERIENCE platform signals a commitment to integrating model-based engineering (MBE) for optimized vehicle development.

At the same time, Volkswagen’s $5.8 billion investment in an alliance with Rivian showcases a strategic bet on the future of electric mobility. However, alongside these forward-looking investments, Volkswagen must grapple with fundamental execution challenges—managing rising production costs, navigating supply chain disruptions, and ensuring that its transformation efforts deliver tangible business outcomes.

Accelerating engineering transformation

Volkswagen’s collaboration with PTC and Microsoft to develop Codebeamer Copilot signals a strong commitment to leveraging generative AI in Application Lifecycle Management (ALM). Codebeamer is being augmented with AI-driven automation to enhance software development efficiency, a critical step as automotive manufacturers increasingly shift towards software-defined vehicles.

Software is no longer just an enabler; it is now at the heart of automotive product differentiation. For Volkswagen, a legacy automaker, competing with software-native disruptors requires a fundamental shift in how vehicle development is structured. Codebeamer Copilot represents more than an AI-enhanced ALM tool—it is part of a broader shift toward agile, continuous software deployment, ensuring that VW’s vehicles remain at the forefront of digital innovation.

Simultaneously, VW’s adoption of Dassault Systèmes’ 3DEXPERIENCE platform aims to optimize vehicle development processes. This move reinforces the industry’s pivot towards integrated digital twins, where real-time collaboration and model-based engineering (MBE) accelerate product lifecycle governance. The 3DEXPERIENCE platform aligns with the growing need for cross-functional collaboration between mechanical, electrical, and software engineering teams, bridging gaps that have historically slowed down the development process. While these investments showcase Volkswagen’s intent to streamline development, execution remains key—successful deployment will hinge on cultural adoption and seamless integration with legacy systems.

Strategic EV alliances: the Rivian gambit

Volkswagen’s $5.8 billion partnership with Rivian announced in November 2024 signals a strategic hedge against legacy constraints. The alliance provides VW with access to Rivian’s advanced EV architecture, allowing the German automaker to accelerate its EV portfolio without reinventing the wheel. In return, Rivian gains the financial backing and industrial scale necessary to compete in an increasingly saturated EV market.

This collaboration is emblematic of a broader trend in the automotive industry: the shift from closed innovation models to open collaboration. OEMs are recognizing that building everything in-house is neither cost-effective nor agile enough for the rapid technological shifts defining the industry. By working with Rivian, VW positions itself to benefit from the startup’s agility while bringing its own mass-production expertise to the table.

However, alliances alone are not enough. To realize the full potential of this partnership, VW must overcome internal friction—balancing traditional automotive development processes with the more iterative, software-driven approach championed by Rivian. Success will depend on VW’s ability to integrate new ways of working without disrupting existing operations.

Executing transformation amid industrial pressures

While Volkswagen continues to push forward with its digital and electrification strategies, operational challenges remain a persistent theme. Rising material costs, supply chain bottlenecks, and production inefficiencies have placed significant financial pressure on the company. In 2024, VW reported 4.8 million vehicle deliveries—an impressive figure, but one that comes against the backdrop of increasing competition from Tesla, Chinese automakers such as local market leader BYD, and emerging EV startups.

Manufacturing complexity is another hurdle. Unlike Tesla, which designs its vehicles with highly streamlined production methods, VW is contending with legacy platforms that require significant re-engineering to accommodate next-generation propulsion systems and digital architectures. This tension between past and future is not unique to VW but serves as a reminder that digital transformation is as much about unlearning as it is about innovation.

To bridge this gap, Volkswagen must double down on operational efficiency while ensuring that its transformation investments deliver clear, measurable returns. This means refining its global production footprint, streamlining supplier relationships, and investing in workforce upskilling to ensure that its employees are equipped for the future of mobility.

Balancing disruption with execution

Volkswagen’s trajectory exemplifies the duality of digital transformation: bold investments in AI-driven engineering and strategic alliances, juxtaposed with the realities of industrial-scale execution. The success of these initiatives will depend on VW’s ability to navigate integration complexities, mitigate disruption risks, and sustain operational resilience.

For manufacturing engineering leaders, the key takeaway is clear: transformation is not just about adopting new technologies but ensuring their successful convergence with business imperatives. It requires a relentless focus on execution—aligning investments in AI, ALM, PLM, and EV strategy with pragmatic, scalable implementation roadmaps. The future of Volkswagen, and indeed the broader automotive industry, will be defined by those who can master this balancing act.

As digital and physical converge faster than ever, Volkswagen’s journey serves as a crucial case study that highlights both the promise and pitfalls of large-scale digital reinvention. The automaker’s success will hinge on its ability to harmonize technology adoption with industrial pragmatism, ensuring that innovation is not just pursued but effectively realized at scale.

The post VW’s digital journey balances bold moves with the realities of execution appeared first on Engineering.com.

Nestled in Boston’s Back Bay during the first three days of April, ACE 2025 marked a key milestone: Aras’ 25th anniversary. It was a celebration of a quarter-century of innovation in the PLM space, built on the vision of founder Peter Schroer. What began as a small gathering has grown into a global forum for transformation. Aras Innovator continues to position itself as a challenger to legacy PLM systems, offering an open and adaptable platform.

“Building on the company’s red box concept,” as presented several years ago by John Sperling, SVP of Product Management, the Aras strategy is rooted in an overlay approach and containerization—designed to simplify integration and support relationship-driven data management. CEO Roque Martin described Aras’ evolution from its early roots in PDM and document control to today’s enterprise-scale PLM platform—enabling connected intelligence across functions and domains.

This trajectory mirrors the wider PLM market shift—from rigid systems to flexible, integrated platforms that support customization, adaptability, and data fluidity across engineering and operational boundaries.

AI, cloud, and the connected enterprise

Nowadays, it is close to impossible to discuss tech/IT/OT or digital transformation without exploring new opportunities from artificial intelligence (AI). Cloud and SaaS are established deployment standards across enterprise software solutions. Nevertheless, PLM tech solutions often lag when it comes to adopting modern architecture and licensing models.

The intersection of PLM and AI is rapidly redefining transformation strategies. Aras’ ACE 2025 conference embraced this momentum through the theme: “Connected Intelligence: AI, PLM, and a Future-Ready Digital Thread.” This theme reflects how AI has become more than an emerging trend—it is now central to enabling smarter decision-making, increased agility, and value creation from data.

While cloud and SaaS have become standard deployment models, PLM platforms have historically struggled to keep pace. Aras is challenging that with an architecture that emphasizes openness, extensibility, and modern integration practices—foundational enablers for enterprise-grade AI. In this landscape, the importance of aligning AI readiness with digital thread maturity is growing. PLM no longer sits at the periphery of IT/OT strategy—it is becoming the backbone for scalable, connected transformation.

Bridging old and new

Martin opened ACE 2025 by recalling that the term “digital thread” originated in aerospace back in 2013—not a new concept, but one whose visual metaphor still resonates. With the announcement of InnovatorEdge, Aras showcased the next leap in PLM evolution—designed to connect people, data, and processes using AI, low-code extensibility, and secure integrations.

With InnovatorEdge, Aras introduces a modular, API-first extension designed to modernize PLM without discarding legacy value. It strikes a balance between innovation and compatibility, targeting four key priorities. It balances innovation with compatibility and addresses four key areas:

1. Seamless connections across enterprise systems and tools.
2. AI-powered analytics to enhance decision-making capabilities.
3. Secure data portals enabling supply chain data collaboration.
4. Open APIs to support flexible, industry-specific configurations.

By maintaining its commitment to adaptability while embracing modern cloud-native patterns, Aras reinforces its position as a strategic PLM partner—not just for managing product data, but for navigating complexity, risk, and continuous innovation at scale.

Data foundations

As we stand at the intersection of AI and PLM, ACE 2025 made one thing clear: solid data foundations are essential to unlock the full potential of connected intelligence. Rob McAveney, CTO at Aras, stressed that AI is not just about automation—it is about building smarter organizations through better use of data. “AI is indeed not just about topping up data foundation,” he said, “but helping organizations transform by leveraging new data threads.”

McAveney illustrated Aras’ vision with a simple yet powerful equation:

Digital Thread + AI = Connected Intelligence

This means:

• Discover insights across disconnected data silos.
• Enrich fragmented data by repairing links and improving context.
• Amplify business value using simulation, prediction, and modeling.
• Connect people and systems into responsive feedback loops.

Every mainstream PLM solution provider is racing to publish AI-enabled tools, recognizing that intelligence and adaptability are no longer optional in today’s dynamic product environments. Siemens continues to evolve its intelligent enterprise twins, embedding AI into its Xcelerator portfolio to drive predictive insights and closed-loop optimization. Dassault Systèmes recently unveiled its 3D UNIV+RSE vision for 2040, underscoring a future where AI, sustainability, and virtual twin experiences converge to reshape product innovation and societal impact. Meanwhile, PTC strengthens its suite through AI-powered generative design and analytics across Creo, Windchill, and ThingWorx. Across the board, AI is becoming the common thread—fueling a transformation from static PLM to connected, cognitive, and continuously learning platforms.

With so much movement among the established players, is Aras’ open, modular approach finally becoming the PLM disruptor the industry did not see coming? Across the board, AI is becoming the common thread—fueling a transformation from static PLM to connected, cognitive, and continuously learning platforms. Gartner VP Analyst Sudip Pattanayak echoed this in his analysis, emphasizing the need for traceability and data context as cornerstones of digital thread value. He identified four critical areas of transformation:

1. Collaboration via MBSE and digital engineering integration.
2. Simulation acceleration through democratized digital twins.
3. Customer centricity driven by IoT and usage-based insights.
4. Strategic integration of PLM with ERP, MES, and other platforms.

From a business standpoint, this translates to strategic benefits in risk management, compliance, product quality, and brand protection. For instance, digital thread traceability supports:

• Warranty tracking and root cause analysis for recalls.
• Maintenance, usage, and service optimization.
• Real-time feedback loops from market to R&D.
• Commercial impact modeling from product failures.

Pattanayak concluded that enterprises should not aim for total digital thread coverage from day one. Instead, the priority is identifying high-value “partial threads” and scaling from there—with AI capabilities built on solid, governed, and well-connected data structures.

The post Aras Software at 25: PLM transformation through connected intelligence appeared first on Engineering.com.

With data gathered from nearly 600 full-time engineers, this survey reveals more than just salary figures. It explores benefits preferences, vacation norms, job roles, and career trajectories, offering a detailed snapshot of the professional engineering landscape. It also raises important questions about workforce sustainability, with a significant portion of respondents nearing retirement age and relatively few early-career engineers represented.

What do engineers prioritize in compensation packages? How prevalent are bonuses and retirement contributions? What does the average career path look like in terms of discipline, job function, and level of responsibility? These are just a few of the insights emerging from this year’s results.

The full report offers data for engineers navigating career choices and for employers looking to attract and retain top technical talent. And best of all, it’s free to access. Simply register here for all the details: https://www.engineering.com/resources/engineering-com-salary-survey/?utm_source=LinkedIn&utm_medium=editorial&utm_campaign=ENGSS2025

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