Engineers love to understand how stuff is made; and knowing how stuff is made, or how it should be made, is where SKARTEK shines. The company’s cofounder and executive manager, Christophe Payon, explains that the company specializes in designing, installing and maintaining industrial processes.

“Our customers are international manufacturers,” he says. “Our job is to produce their manufacturing lines. We do it all by ourselves, the programming, electrical and mechanical.”

For each project, SKARTEK’s goal is to hand off an end-to-end, automated, manufacturing solution to its automotive, aerospace, railway, construction and electronics customers. To achieve this, the company employs engineers of various specialties, on-site manufacturing equipment and a manufacturing research lab. SKARTEK also works with SOVA Digital and its partner Siemens Digital Industries Software to audit, optimize and enable its digital transformation. SOVA Digital helps manufacturers integrate digitalization, optimization and Industry 4.0 solutions into their current workflows.

How and why SKARTEK needed digital transformation

As SKARTEK grew, its software and processes became ill-equipped to manage its complex projects and their many stakeholders. “We started with two people; right now, we are more than 55,” says Payon. “We needed to implement a totally different way of working, as we were not able to manage our classical projects the same way. We were looking for a new support software to help us improve our organization.”

The company realized that its current computer-aided design (CAD) and data management software solution was slowing it down with performance issues and couldn’t support SKARTEK’s expanded end-to-end solutions offering. SKARTEK selected Siemens Teamcenter and Solid Edge to update, digitize and digitalize its workflows, data and development processes. This digital transformation, aided by SOVA Digital, produced benefits that were significant — some even unexpected — for Payon and his team.

The obvious, and not so obvious, design benefits of digital transformations

Payon notes that no production facility is the same. As a result, SKARTEK must adapt its solutions and workflows to the current customer they support. Nonetheless, the company has found that it can reuse many of the parts, concepts and designs created throughout its history and adapt them to different facilities.

“When doing a project, we want to reuse what is done and design something new, if needed,” Payon clarifies. “We are selling optimization. So, we want to sell the optimization we use in our own system: Design it once and reuse it in future projects. In the past, this wasn’t possible. But with Teamcenter and Solid Edge we can design once and adapt it for another project. It really decreased the time, cost and makes us more competitive for the customer.”

The company also discovered some obvious and some unforeseen ways reusing parts can speed up the development of new manufacturing systems. The obvious benefit is when a design from one project can be used, one-to-one, with another system. Now instead of re-inventing-the-wheel, SKARTEK just inserts the part it has already designed, evaluated and approved for a specific application. If the application is slightly different than the original project, then some aspects of the part may need a redesign. But since SKARTEK already has a good starting point, the engineers need only to reoptimize the part for its new application — saving development time and money.

As for unforeseen benefits, Payon points to the members of his team that need a finished design to start developing a downstream task, part, workflow or automation. Consider an engineer programming the movements of a robotic arm. While other members of the SKARTEK team develop a new gripper for the arm, the engineers and gripper designers can work in parallel.

Standardizing its workflows within Solid Edge enabled SKARTEK’s engineers to work in parallel on various design aspects. “Now, we can develop the mechatronics and controls in parallel with the mechanical side,” says Payon. “When there’s a change in one aspect of the design, the software dynamically tracks, manages and updates the design for everyone, ensuring we’re all working on the latest version.” This is because Teamcenter includes integrated revision management, automatically creating and tracking new revisions of Solid Edge parts, assemblies and drawings.

“Our target is to be a more flexible company compared to our competitors,” says Payon. “We need to be more flexible, and this flexibility is increased by collaboration. By using the software like Solid Edge and Teamcenter from Siemens, which allows us to be competitive.”

The role of data management and simulation in production line design

In the past, SKARTEK would often lose data to unstable software applications. If the program failed at an inopportune time, hours of design work could be lost. And since data was not effectively managed, even if a design were properly saved, it could be lost, forgotten or invisible to members of the team that forgot, or were unaware, of its existence.

With a digital transformation and proper data management tools, like those found in Teamcenter, engineers can access the data, information and IP they need when they need it. It is also much easier to search through the history of a company’s data lake to find solutions, or a good starting point, for a given challenge.

This easy accessibility to information, data and designs also simplifies simulation workflows. “For easy projects,” says Payon, “simulation isn’t done. We are using simulation for [developing] bigger lines and when we need a lot of new designs for the work. Simulation is important as it gives the feedback on our technical solutions.”

Payon estimates that about 10 to 25 percent of SKARTEK’s projects require simulation. But since the projects requiring simulation are complex, and CAE has traditionally been a long, hard and tedious process, these projects represented a significant amount of work. Thus, the workflow boosts Solid Edge and Teamcenter bring to SKARTEK’s simulation experts are a significant boon.

“We design everything from Solid Edge and can send it to all the other software applications that it communicates with,” says Payon. This streamlined the simulation workflow as 3D models were readily available and compatible with the CAE tools of choice. In fact, many of the simulations SKARTEK requires can be done within Solid Edge. Giving the company’s designers a familiar user interface and workflow to better assess the performance of their designs.

Digital transformation delivers productivity gains, reducing time by 30 percent

As SKARTEK made the transition to Solid Edge and Teamcenter, it was important to gain the buy-in from end users and other stakeholders. Assessing and measuring the productivity gains was essential, so SKARTEK crunched the numbers. By adding Solid Edge to the company’s workflows, project time was reduced by 10 to 15 percent. This alone would be impressive, but its success was surpassed by the addition of Teamcenter — which enabled the company to reduce project time by 30 percent.

The main sources of these productivity gains:

1. Teamcenter creates a single design environment which enabled teams to work concurrently on the latest, up-to-date, engineering information.
2. Teamcenter supports collaboration and data visibility, enabling everyone to remain on the same page, work together and avoid rework.
3. Solid Edge’s ability to dynamically manage and update CAD models to reduce the chances of any errors during product development.

Payon’s team appreciates the flexibility of Teamcenter to fit their business needs. “It’s a design data management solution you can adapt to your processes. Not many in this market can adapt to your way of working.”

He adds, “When Siemens first proposed Teamcenter, we thought it was nice, but for bigger companies. We thought it would be harder to utilize in smaller companies. In the end, with the tuning of Teamcenter to our needs, we found a way to use it that is dedicated for us. We added Teamcenter and adapted it for our needs.” And in so doing, SKARTEK is reaping many rewards.

If you, like SKARTEK, want to benefit from better performance, improved re-use of CAD data and a more integrated product development process, learn more about Solid Edge and why you should switch now, along with Teamcenter for Solid Edge data and process management.

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With 2024 rounding the corner, engineering.com sat down with Dale Tutt, Vice President Industry Strategy at Siemens Digital Industries Software. The topic: what engineers can expect from their CAD, CAE, PLM, AI and digital transformation software in 2025.

Writer’s note: This interview has been edited for length and clarity.

What will be the biggest disruptors in engineering and manufacturing over 2025 and beyond?

The obvious answer is still AI and how it’s transitioning some of the solutions our customers are using. More companies and solutions are starting to adopt it, so I think that is going to be one growth area.

The other one is the industrial metaverse. I think that conversation has shifted a lot. There was a period of hype a couple of years ago, but as we’ve continued to develop it — and having it based on physics — the industrial metaverse now brings together a more comprehensive model to visualize operations. I think a lot of companies are starting to see this as a tremendous benefit.

Speaking of the metaverse, how will augmented, virtual, mixed or extended reality (AR, VR, MR and XR) change engineering work in 2025 and beyond?

This is an area I’m always excited about because it can start to change the way people work. In the past, engineering VR workflows were about design reviews. But I think bringing the ability to create within VR makes it a transformational technology.

Engineers have traditionally worked on 2D screens. They might be able to use some tools for manufacturing simulations and clearance checks, but it was done in this 2D world. Sometimes when designing a part there isn’t the necessary context to know how big or small it is. So, being able to design in context — maybe I’m sitting inside the car designing its parts — I can actually see what it looks like. I can touch things, so to speak. The benefit for business is fewer design changes once the process of building those products begins.

With VR, people can also work on the same model in real-time. It can create a collaborative workspace where everybody interacts with the part together. It is unnecessary for everybody to be in the same room looking at the 3D model because everyone is operating in a live environment within the metaverse with the ability to collaborate on the part and communicate.

With AR, MR and XR, as we move into manufacturing and maintenance environments, we’re starting to see benefits. We have the capability to pull up specs while looking at the part. Technicians don’t have to look away at a paper and come back to the part. There are ergonomics behind that. Instead, the information is presented right there and, in some cases, even projected onto the part.

For another benefit, imagine going through the operations to assemble a product. With XR, it is no longer necessary to work in another system to log a completed task. It’s better, from a business process standpoint, to capture those steps as the person does them. This reduces the number of mistakes that can be made while checking off lists.

In the past, people checked things off manually. What happened is that someone would go through all the steps first, as they didn’t want to keep going back and forth. They would then go back to check everything off. That’s when you start missing things on checklists. XR really reduces these chances for errors, and it makes workflows easier for technicians.

Let’s go back to AI. How will it change the role of an engineer?

We’ve seen a shift in tool sets with generative AI coming into play.

Consider the industry’s need for more systems engineers. Systems engineering, in the past, has been very complex. The people doing it were very specialized. Those people are still needed, but it was a small subset of your engineering team.

Now with AI and large language models (LLMs), we’re able to democratize systems engineering. Workers can enter parameters and then the systems modeling tools can auto generate systems models. This provides more engineers access to these tools. They’re able to modify those systems models and generate software code.

That’s how I see the engineering workforce changing with AI. It doesn’t remove the need for specialists. But it gives more engineers access to those specialties.

That said, what is the future of generative design and its potential for creating more sustainable, optimized products?

We’ve seen generative design over the years for solutions like electrical systems and individual component optimizations. Going forward, generative design will be able to take LLMs, look at the IP of a company, and public domain, and enable engineers to consider more designs of complex systems.

In the past, when examining design concepts there were time limitations: “How many options can I look at while still hitting my deadline?” So, the design space might include tens of options. With generative design, the opportunity is there to look at hundreds of thousands of options.

It’s not going to be, “hit the easy button and an answer’s going to pop out,” but it will provide more options to the engineer who can then make better, faster decisions.

For example, consider applying generative design principles to the supply chain. Options such as, “This part may be cheaper but it’s farther away and has a different carbon footprint” can be considered. There is the option to make those trade-offs in a way that were not available in the past. Not only is cost a consideration, but also cost balanced with sustainability. A more holistic picture based on these different design options comes into focus.

When expanding this analysis with data from products in the field, analytics can be leveraged to optimize performance and predict maintenance and costs. As a result, when making the next design iteration, AI is going to pull in the analytics from those real-world products to produce better designs and digital twins.

I think that’s really where AI is going to help transform businesses. It’s opening up your decision space by utilizing more information.

How is AI expected to change the user interfaces of engineering software?

We’re expecting to see AI help automate the mundane. We see already that AI is helping part classification, or predicting future commands based on user actions.

To predict commands, it is easy to say, “well that’s not a big deal.” But think about the hundreds of operations someone designing CAD goes through every day. If we can save 10 to 15 seconds each time, that starts to add up.

Environmental regulations are tightening worldwide. How will CAD and CAE address the growing need for sustainable design and manufacturing?

For decades, industries have optimized around parameters like energy efficiency. For example, in the past, it’s been done to improve the efficiency of cars and commercial aircraft. There’s been interest to do that from a cost standpoint: “How can we reduce the cost of operations?” It stands to reason that by burning less fuel, by definition, less carbon is being emitted.

Solutions like CAD and CAE have been used to optimize cost, weight, quality and manufacturing processes. A lot of those things also support sustainability initiatives.

I think there’s more emphasis on it now. Like CAD and CAE software can now do carbon rollups — like cost rollups. Functionality has been added to support sustainability initiatives.

Carbon emissions are parameters in a company’s CAD model, in addition to other parameters they have been optimizing. With new regulations coming online, they can use a lot of the solutions they have been using with the added benefit of being able to project sustainability data.

What new skills and knowledge areas should engineers focus on to stay relevant through 2025 and beyond?

Think about the transition in the workforce over the last 10 or so years. We’ve seen a lot of engineers moving from being highly specialized to more generalized. In the past, there would be one mechanical designer working on CAD and another doing simulation and analysis.

Now, the tools are enabling engineers to be more cross functional. They’re doing the mechanical design on the 3D part, but they’re also doing the simulation and wire harness design. We are starting to integrate our solutions so that it’s easier for engineers to work with different tools.

The next phase we’re seeing is this movement toward more software-defined products. We’re seeing this in cars and consumer electronics. Software is defining user experiences more than in the past. When wanting to add a new capability — say auto-braking — it is now possible to add that through software and not by making mechanical changes. It is not necessary to send the car in to replace a board.

That’s driving the demand for engineers to understand software and systems engineering. I think those are the skills engineers should focus on.

How will digital twin adoption grow in 2025?

We continue to see companies recognize that they need the digital twin to fully understand their business and to optimize their operations and products. We talk about the comprehensive digital twin — which connects requirements, CAD models, simulation models and all analysis — and how that helps companies optimize and validate designs. Now it is possible to virtually test models because of physics-based digital twins. Then when moving onto physical tests, the user has more confidence and fewer changes. That helps companies avoid overruns and schedule delays, because changes are hard to make once you start building.

Some industries like aerospace and automotive have been doing this for a long time. But we’re starting to see this in medical devices where they’re making greater use of digital twins. We’re also seeing other industries that have been historically “less digital” starting to make the transition.

I think companies that don’t embrace digitalization may find it harder and harder to stay competitive. I’m not saying they won’t be competitive. But I think the companies that adopt digital transformation are seeing benefits and reducing time-to-market. They’re seeing improved performance and are better able to address sustainability regulations. I think it becomes imperative for companies to be able to understand their processes more thoroughly.

What role will quantum computing play in engineering?

I don’t have a good view on the timeline for quantum computing. But I think, in terms of “how it’s going to impact engineers,” it will be a natural evolution of what we’ve seen over the last 20 years.

As computing capabilities advanced, software solutions like CAD and CAE have taken advantage of this. As a result, it is now possible to have a 3D model with higher fidelity then it was 10 or 15 years ago.

Regardless of where computing capabilities go, we’re going to see engineers model systems with much higher fidelity than they can today. They’ll have more confidence in that model because they’re able to create it without simplifying the system to accommodate for computing power.

Going into validation or verification, it is now possible to do more of that virtually and rely less and less on physical tests. Today, maybe 10% or 20% of testing is done virtually and the rest is done with physical prototypes. There’s a point in the future, and it’s maybe four or five years away, where I think we will see that flip. Then, 80% of testing will be virtual and 20% physical — which essentially becomes a validation of your virtual testing.

What about Siemens? What should engineers expect for 2025?

We continue to expand our capabilities in the comprehensive digital twin. Users can expect their tools will become more comprehensive. They’re also going to continue to see more advancements in the use of AI in our solutions. Next, they are going to start to see VR become more commonplace in our solutions. We have a lot of exciting work that we’ve been talking about with customers and they’re going to see some of those things hit in the next 12 months. Learn more about digital transformation at Siemens.

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Globalized and highly competitive, the consumer packaged goods (CPG) industry has remained eternally resilient. However, emerging trends are set to significantly impact demand and potentially open new revenue streams. Shifts in consumer preferences, increased competition from the lowering of entry barriers, and a growing population worldwide have transformed the markets. Meanwhile, retailers have created their own formidable brands with private label product lines, which has forced brand manufacturers to adjust their prices, putting even more strain on their profit margins.

CPG companies are confronting these competitive concerns while simultaneously seeking to improve profit margins, meet regulations and satisfy consumer demands for greater levels of sustainability and transparency across the entire product lifecycle.

To address this multitude of elaborate challenges and the ever-changing landscape of the CPG industry requires an evolution of processes and technologies. This is why top performers in the industry have begun digitalizing their operations. Digital transformation is an important way to support growth, become more efficient and guarantee resilience as competition increases and consumers evolve.

Shifting consumer demands

Consumer desires have never been consistent, but today there are many interesting factors affecting these demands, which have increased market fragmentation and caused the proliferation of new brands and SKUs (stock keeping units), so it is important to understand them.

One factor is that CPG companies are now targeting the Generation Z (Gen Z) market segment. Studies indicate that about 38 percent of Gen Z is willing to try new brands and buy healthier as well as more sustainable, transparent and innovative products from multiple companies. This is an increase compared to other generations.

Other factors include the acceleration of online buying and initiatives like “eat at home,” which gained considerable momentum during COVID. These factors are forcing companies to redesign their product portfolios. According to a new report from Grand View Research, the meal kit industry in the United States could achieve a compound annual growth rate of 15.3 percent, reaching nearly $64 billion by 2030. Online platform meal kit sales currently account for over 63 percent of this market.

One of the biggest factors affecting consumer demands is that the UN predicts the worldwide population will reach 8.6 billion by 2030 — with most of this growth driven by developing and emerging markets. Consequentially, many of these consumers will have limited disposable income as World Bank studies estimate that 47 percent of the global population lives on less than $6.85 USD per person per day.

Managing these factors will require CPG companies to accelerate their speed to market and be more responsive to consumer demands while guaranteeing shorter and increased NPI (new product introduction) cycles. To accomplish this, companies must digitally integrate the entire lifecycle to break down department silos and allow information and data to flow to all necessary stakeholders. This requires more than the adoption of a few digital tools or just digitalizing certain aspects of the process. Digitalization of the entire product development process is critical to success.  

Flexible factories for flexible production

The changing landscape of innovation, personalized products, accelerated e-commerce and trade shifts are also putting extreme pressures on factories and their manufacturing operations. CPG is a highly automated industry so it would seem well positioned to meet the changing landscape. However, most CPG companies implemented their automation systems between the 1980s and 1990s. Their main goal at that time was to produce large product batches as efficiently as possible. Forty years later, these same machines and automation systems have difficulty adapting to continuous product changes. This is partly because they are not equipped with modern technologies that aid connectivity and flexibility, such as IoT and AI.

Today, factories need to be able to manufacture a greater number of products and product variants. When a newly updated product recipe reaches the factory, businesses need to be able to quickly and easily update and/or reconfigure the entire manufacturing process — from line automation to machines. Manufacturers need production systems and machines that can guarantee speed and quality as well as flexibility so they can quickly adapt to market changes. Whether a company is building a new factory or converting existing factories, introducing flexibility in the manufacturing ecosystem is key.

There are several ways to increase production flexibility. Leveraging digital twin technology — combined with manufacturing planning and manufacturing operations solutions — early in the product lifecycle, enables companies to predict production feasibility and performance while minimizing downtimes and ensuring quality. One of the latest advances in the automation area is the use of virtual programmable logic controllers (PLCs). Organizations can download virtual PLCs as edge applications and integrate them directly into the IT environment, modernizing older factories or manufacturing lines.

Lines and manufacturing equipment also play significant roles in improving production flexibility. New intelligent machines often have flexible, modular designs that facilitate integration into production lines and can adapt to perform several tasks. These new technologies are applied to these machines via edge computing where they can run software at the machine level — including AI applications. IoT enabled machines can increase connectivity, easing integration and can even automate some engineering tasks.

Navigating the complexities of the supply chain

Supply chain risks remain an ever-present issue for CPG. Oil price fluctuation, political crises and wars negatively impact traditional supply routes, forcing companies to strategically analyze their product portfolios, look for alternates and evaluate new supply options. Often, businesses opt to nearshore, or manufacture close to the consumer to shorten the supply chains.

Additionally, obstacles can arise from weather and other climate-related occurrences which can hinder improving profit margins. Digitalizing the supply chain to build a “central control tower” can address these challenges. Flexible supply chain planning solutions facilitate real-time information on shipping costs, tariffs and materials, enabling better informed decisions.

Digital control towers, however, only solve part of the supply chain challenge. Contamination, for example, is a growing concern in the food and beverage industry, and is often caused by faults in the supply chain. CPG safety is also becoming increasingly important as stringent country and region-based regulations influence practices. Even still, food poisoning incidents are rising with an estimated one in 10 people falling ill from eating contaminated food globally every year. Many cases of food poisoning are caused by supply chain hiccups, with agricultural ingredients and final food products being the most susceptible.

Whether CPG companies source materials locally or overseas, the risk of interferences in the supply chain will always exist. Traceability of both ingredients and final products is the only way to certify that products are safe and legitimate. In the event there is an issue of contamination, traceability makes it possible to quickly determine the source.

The integrated lifecycle management solutions available today make it possible to develop a robust digital backbone, which can provide full traceability of products. Blockchain technology, for example, immutably stores traceability and event data, providing stakeholders with a single source of truth generated from secure data that cannot be overwritten or changed. This creates a digital footprint for the verification and validation of information. Coupled with IoT, blockchain technology can provide insights “from farm to fork,” helping to eradicate human error and enhancing transparency in a complex and multi-tiered supply chain.

Preparing for tomorrow’s problems today

Many companies in the CPG industry are reluctant to enhance and digitalize their processes through software and automation. The industry fears that implementing new technology may impact downtimes and increase operational costs. While the idea of continuing to “do things the way they have always been done” appears safe, it is not. The current landscape requires a revolution in the way companies develop, manufacture and source their products.

The CPG industry needs to implement fresh, flexible and scalable digital solutions to meet both its current and future goals while overcoming obstacles that are primed to become more complex as the population grows, ecosystems evolve and trends emerge. Through digital transformation, organizations can create one source of truth across the enterprise. Brands, programs and product lifecycles are combined into a single database that can boost collaboration across the entire CPG ecosystem while maintaining brand equity. By integrating the entire digital lifecycle, companies can improve quality, promote brand loyalty and improve speed to market for new products, enabling them to not just survive, but thrive in today’s market.

Visit Siemens to learn more about the impact of digital transformation in the food and beverage industry.

About the author

Mario Vollbracht is the Vice President of Consumer Products and Retail at Siemens Digital Industries Software. He joined Siemens in 2022 with more than 25 years of experience in the industry, including direct industry experience, management consulting and an extensive background in IT management. Vollbracht has background across the entire value chain of retail and consumer goods, with a focus on innovation, supply chain, sales, marketing and business analytics.

The post How consumer packaged goods remain profitable in a changing world appeared first on Engineering.com.

To keep up with rising product complexity, engineers need more powerful systems engineering tools that offer enhanced modeling capabilities and leverage the power of generative AI. Siemens and IBM announced last year that we are collaborating to accelerate sustainable product development and operations. Since then, the two companies have partnered to provide standard systems engineering solutions through the integration of IBM Engineering Systems Design Rhapsody with Siemens Teamcenter, Polarion and Capital. By working together, we have updated the interface of our systems engineering tools and developed prescriptive templates for engineers to seamlessly adopt the solutions. Siemens and IBM are now collaborating to augment the product development and operation optimization capabilities enabling engineers to build with IBM’s AI and data platform, IBM watsonx. This helps provide access to relevant information and tools throughout the engineering process.

The joint team is continuously extending and innovating the solutions using a digital thread approach, providing leading-edge digital tools that enable organizations to create, maintain and capitalize on digital threads. These digital threads seamlessly connect data sources across the product and service lifecycle. As a result, organizations enjoy greater accessibility to important product information and can address the challenges associated with designing next-generation products based on actual data from manufacturing and operations.

Digital threads connect every aspect of the product lifecycle

Digital threads are the essential link to a product’s mechanical, electrical, electronics and software design. This includes manufacturing and other downstream operations, such as maintenance, service and end-of-life management. Digital threads are becoming the essential and mandatory technology to cope with regulatory compliance and maintenance requirements and are implemented and defined during product design. Product-related information can flow seamlessly between cross-functional teams, breaking down data silos and communication barriers that impede collaboration and put product quality at risk. Engineers and other stakeholders work from a single source of truth to coordinate work more efficiently without worrying that information is lost or outdated. As a result, stakeholders can make well-informed decisions throughout the product development process.

The digital threads and digital twins of physical assets, fleets or factories ensure accurate contextualized data flow and optimized service processes from engineering to operations. With increasing requirements and more regulations from the EU, as well as in other countries, the need for monitoring and management of the product lifecycle has become even more important.

The new combined engineering solution from Siemens and IBM can provide data visibility and support traceability throughout the product lifecycle, from early design and manufacturing to operations, maintenance, updates and end-of-life management. This can help companies make informed decisions earlier in the design and provides an engineering process to help drive improvements in cost, performance and sustainability.

GenAI-enhanced digital threads can automate and transform workflows for companies and bring complex and more sustainable products to market faster

Systems engineering is a very complex process that requires engineers with unique skill sets. When bringing GenAI into our systems engineering solutions, the technology can help automate the creation of system models. Engineers can also then use natural language processing to operate faster. Instead of manually typing, drawing and curating the design by coding, engineers can instead verbally instruct the system and software on the task they want to perform.

There will be a process to develop the knowledge base that trains the model, but over time it will be able to assist the engineers with recommendations and speed up the design process. Then the next step will be to generate software codes automatically using GenAI technology, leveraging the extensive knowledge base that has been built into your system. In reality, this helps to democratize systems engineering and make it accessible to more people with a broader skill set. There has always been a shortage of specialized experts in the marketplace, and this approach can help broaden the potential pool of engineers that companies can hire for systems engineering jobs, accelerating the product development process and shortening time to market.

IBM’s AI and data platform, IBM watsonx securely enables engineers to use large language models (LLMs) and other foundation models in a truly open environment, avoiding vendor lock-in based on development in IBM Research. This open approach enables engineers to design highly complex products with confidence and spend more time focusing on key differentiators such as quality and sustainability.

Siemens and IBM are also collaborating on developing a SysML v2-based solution with an associated migration path for businesses to transition to the next generation Systems Lifecycle Management solutions. SysML defines a modern modular standard for the specification, analysis, design, verification and validation of a broad range of systems and systems-of-systems. Service lifecycle management can assist in maximizing business value for product serviceability by connecting service engineering and maintenance to facilitate new collaborative processes between OEM and operators.

Systems engineering is being adopted by nearly every industry as a more holistic, collaborative and efficient approach is required in the market

Most people know systems engineering from aerospace and automotive industries; they are indeed the early adopters of the systems engineering approach. However, today the application of systems engineering extends beyond these industries into a wider range of sectors that have electronics and software embedded in their products. These industries require a holistic, collaborative and efficient approach to designing complex electromechanical systems.

Electronics

The integration of electronics into a wide range of products and industries is fueling significant growth and innovation around the world. As technology continues to evolve, we can expect to see even greater convergence of electronics with other emerging technologies. For electronics designers and manufacturers, they need a solution that unifies electrical, mechanical and software domains on a single platform. This can provide a comprehensive system view that encourages innovation and accelerates development cycles. They also need a smart manufacturing strategy to enable businesses to connect and streamline processes from customer desire, engineering and production to service.

Aerospace

As the early adopter of systems engineering, organizations in this industry are developing cutting-edge platforms and systems with exceptional performance goals. Governments are transforming infrastructure and security systems for new aircraft and technology. Companies need innovation, facilitated by collaborative, synchronized program management across the product lifecycle and value chain. Multidisciplinary design and optimization provide a comprehensive design solution that integrates critical aspects of product development, including mechanical, electrical and software design. That way when requirements change, all aspects of the design can adapt simultaneously, significantly speeding up and reducing the impact of change.

Automotive

Software and systems engineering accelerates the development of electric vehicles (EV), advanced driver-assistance systems (ADAS), interaction with the vehicle for maintenance and usability and autonomous vehicle (AV) feature deployment. It does so by utilizing methodologies, processes and tools that manage the rapid increase of software and electronics while providing mechanical system alignment. As vehicle software becomes increasingly interconnected and integrated across multiple domain systems, combining advanced software and systems engineering are required to ensure software and hardware interoperability. This approach will help deliver vehicle performance, compliance, safety and cybersecurity while meeting challenging cost and timing targets.

Design and manufacture sustainable and complex products faster

Companies today face more pressure than ever to deliver sustainable and quality products on short timelines. At the same time, rising product complexity is making it harder to coordinate cross-domain engineering and manufacturing work with other functional departments. Siemens and IBM’s joint solution for systems engineering and asset lifecycle management helps manufacturers realize continuous integration across domains from concept through operation.

For additional information about current Siemens and IBM joint offerings visit our respective websites at the Siemens + IBM Partnership Page.

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It’s easy to forget the technology that supports the food industry when you look at a meal on a plate. After all, a lot of food preparation is still done at home, or by a local restaurant. But what about the ingredients? They must come from somewhere and they need to be processed into a consistent, high-quality product usable by chefs, home cooks and fast-food operators. In truth, the amount of engineering behind the foods we eat is enormous.

Blendhub is well versed in the hidden complexity behind the food industry. The company aims to streamline food production into easy, repeatable processes to ensure a product has the same quality and consistency regardless of where it is made, who makes it and the local ingredients available.

“The founding principle,” as Henrik Stamm Kristensen, founder and chief moonshot officer at Blendhub, puts it, “was there are trillions of recipes available online from Google search to cookbook uploads and individual blogs, all together containing millions of different food ingredients. We set out to connect the dots and ‘kill the black box’ – read transparency – by identification of every single ingredient and its functionality in the final food product.”

To make this dream a reality, Blendhub couldn’t just worry about its own workflows, products and equipment; it also had to keep a close eye on suppliers from around the world. Even a simple ingredient, like milk, can have different qualities, material properties, compositions, allergens and tastes due to local regulations, animal feeds, climates, livestock and more.

Using Siemens Digital Industries Software’s Xcelerator portfolio of solutions for digital transformation, the company was able to digitally transform its own and other food producers’ product lifecycle, create a central, cloud-based repository of supplier data and scale it to the point where anyone could take a food product idea to market launch anywhere in less than 3 months’ lead time. And the best part is, Blendhub and Siemens are sharing this technology with the world.

Who is Blendhub?

At the end of the day, Blendhub offers hungry consumers, and food and drink companies, powder-based food ingredient solutions or powdered food products. Think of just-add-liquid meals like dry pancake batter: Add water, dairy or vegetable milk, eggs (if you’re feeling ambitious) and the powder to a bowl, then mix, cook and eat. Blendhub may have been the company contracted by a given brand to produce the powdered mix.

What really sets Blendhub apart from its competitors is its extreme flexibility and localization. Traditionally, a company might spend years and millions of dollars to open a new food facility. In fact, Stamm Kristensen laments the first time he opened a food production facility in Spain. “It was a turning point,” he says. “We did the same thing most other food producers do. We went out to the market, we looked for hardware, blending equipment, you know, all the equipment to build a facility. It took a little more than two years, and we understood that the final factory was not replicable … it does not make sense!”

After what Stamm Kristensen describes as, “all that hassle,” he called in his engineering team with a mission: build a blending and packaging factory that can fit in a 40 ft container. It must be easily transported, cleaned, recommissioned, validated, certified, switched to a new product and installed in any room with the right size to house it. With this equipment, Blendhub could now make its products where the customers and/or ingredients were located.

The next time Blendhub needed a new facility, all Stamm Kristensen and his team needed was to find a factory building for rent large enough to fit the container, a quality control lab and a food formulation and reformulation lab. The first facility of its kind opened in India in less than nine months and cut the cost of specific food recipes by 30%. Better yet, Blendhub now had a replication model to deploy anywhere on a narrow budget.

But to capture big name customers like Unilever and PepsiCo, who Stamm Kristensen says his facilities are approved by, this replication model needed to be taken a step further. It needed to ensure that wherever the facility was, its recipes and final products were consistent. After all, a globally branded food product is supposed to taste the same wherever you are, so the solutions created by Blendhub must be the same as well. To ensure this level of consistency, Blendhub needed exceptional and instant quality control and a digital transformation.

Blendhub’s digital transformation reimagines the food industry

Stamm Kristensen notes that the first step was to digitize the quality and production equipment as the hardware wasn’t cloud-based. Next, Blendhub created software to run and communicate with its equipment. The idea was to have the software control not only the recipe and ingredients, but also the operations of the equipment.

To ensure quality, Blendhub started using near-infrared spectroscopy to assess the quality of its products and the products of its suppliers. But due to insufficient software quality, they created their own ChemoMetric Brain platform. For example, a potential supplier might send Blendhub a sample of their whey powder. Blendhub can then run those samples through the spectroscopy machine both before and after mixing it into a product. They can then assess the homogeneity of the blend and how the finished product performs to see if it meets specification. By repeating this process, Blendhub started to produce a library of its ingredients, product compositions and performance based on spectroscopy data. These results now act like fingerprints to check anything that goes in or out of a facility.

Stamm Kristensen adds, “If we can digitize the suppliers that we have approved ourselves, and if we can digitize the outcome — the blended powder-based solutions that we supply to our customers — then what if we actually could digitize [any] formulation that is made by anyone in the world?”

This concept then became the crux of Blendhub’s food-as-a-service model.

Food-as-a-Service?

The idea of food-as-a-service, at least to Blendhub, is to enable different food formulators, ingredient suppliers and experts to participate in a project to digitize the whole powder blending lifecycle. Suppliers can input their own spectroscopy data and then others can assess their product’s quality. Participants can then customize recipes and compositions, based on all of this collected data, to create recipes and shared value.

For an example, Stamm Kristensen considered a retired food formulation expert with a wide range of industry knowledge. That expert is likely to still experiment with new recipes on their own as a hobby or a means to make money on the side. They can use Blendhub’s repository of data to customize their own recipes. The company could then offer this recipe, as a product, to a customer. Everyone down from the suppliers to the expert wins, as Blendhub ensures they receive a margin of the formulations’ profits.

“We can show that with these freelance formulators, with these ingredients suppliers, we are now creating a participation model where everyone [creating and participating are] taking advantage of that shared value,” says Stamm Kristensen. “So, this is … why we have turned our business model into a Food-as-a-Service model where we invite many other people and organizations to participate.”

Blendhub’s partnership with Siemens

To bring its digital transformation and food-as-a-service model to reality, Blendhub approached Siemens Digital Industries Software. Tools like Siemens Opcenter, for manufacturing operations management (MOM), and Totally Integrated Automation (TIA), for monitoring and controlling production in real time, were used to bring data from differing equipment into the Siemens ecosystem. But perhaps the most influential result from this partnership is how Blendhub is exploring the use of Teamcenter X, Siemens’ cloud-based Product Lifecycle Management (PLM) solution designed to provide organizations with a scalable, flexible and easy-to-deploy PLM platform to manage product data and processes throughout the entire product lifecycle, from design and development to manufacturing and maintenance.

Stamm Kristensen says, “This is where Blendhub and I came and said, ‘what if we can start using Teamcenter X and start utilizing some very useful capabilities for multiple users, companies and people to connect on one single platform?’” The idea is to use Teamcenter X as the backbone for Blendhub’s library and food-as-a-service platform vision. Its users can then access the data and produce a recipe on the database that anyone can use. As a result, everyone benefits by accessing the same single source of truth.

For years, Siemens has been connecting suppliers from all industries with OEMs via its Teamcenter software. Blendhub is a recent example of a small or medium business making full use of Siemens’ solutions in an effort to achieve sustainable success..

“Most companies are only thinking about their own problems internally, but we look beyond that and say, ‘how can we bring our internal solutions to the utilization of many of the other small and medium enterprises around the world?’” Stamm Kristensen says. “And that is exactly the same reason why with Siemens, we are pushing the boundaries to start prototyping what we could do with Teamcenter.”

Visit Siemens to learn more about how Siemens’ digital transformation technologies that can impact the food and beverage industries.

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The Cremina classic lever-operated espresso machine. We like the “Swiss” red one pictured here. (Right) The Cremina modeled in Solid Edge. (Image: Felix Wey/ Schätti AG Metallwarenfabrik.)

Thomas Schätti and his brothers, all mechanical engineers, run multiple family businesses from their headquarters in Glarus, Switzerland under the family name, Schätti AG Metallwarenfabrik. Jos Schätti is the eldest and president of Schätti, and Stefan Schätti is COO. They were introduced to Olympia Express, makers of espresso coffee grinders, which appealed to Thomas Schätti on several levels. Schätti had always loved the mechanical aspects of mechanical engineering, and also loved the aesthetics of industrial design, having studied both subjects. Top that off with his love of espresso, and it seemed like a no-brainer for the brothers to invest in Olympia Express in 2011 and install Thomas as CEO to run it.

The Schätti brothers were exposed to business and mechanical engineering at an early age as their grandfather ran a sheet metal business in the 1940’s, which their father eventually took over. When asked why they became mechanical engineers, Schätti said they were “conditioned” by the family business as they grew up learning the trade by working on machines and doing stamping, milling, painting and welding. “We were conditioned, but we were also technically interested, and it suited us,” said Schätti.

The Schätti brothers took over the family business together in the early 1990’s, which Schätti said was tough at times; as you can imagine, the three brothers occasionally butted heads.

Today the family business is 90 years old, and they continue to pride themselves on family tradition and quality. The core business continues to revolve around design and manufacturing of products made from sheet metal, including electrical household appliances, components and devices for ventilation technology, as well as fittings and mechanisms for furniture, white goods and mechanical and apparatus engineering. They also currently design and manufacture the Olympia Express machines, since taking over the brand in 2011.

The Olympia Express brand goes all the way back to 1928, started by Italian Luigi Bresaola who ran the Olympia Café in the border town of Italy in Switzerland. “He was technically very gifted,” said Schätti. “He was northern Italian from Trieste, which is one of the most important places for coffee as well. He started a coffee bar [in Switzerland] and he was not happy with the coffee machine, so he started to design his own coffee machines.”

After making his machines available to cafes and restaurants under the name Olympia Express, he started offering espresso machines for home use.

Today the Schätti brothers design and manufacture the Olympia Express machines in house as they continue to combine the company’s passion for Italian espresso and the old tradition of Swiss craftsmanship. They make machines that are built to last, as the tanks are made out of chrome steel. We’re talking built to last for decades, as “50-year-old Olympias” are still a highly sought after machine and often kept in the family and passed down from generation to generation.

Olympia Express uses Solid Edge from Siemens, and Schätti said when they took over the company the previous owners had designed in 2D, so the data was not very useful to them. They remodeled all the parts of the machine in Solid Edge and made new models as well as spare parts. Essentially, they started fresh and built a complete data engineering set. Since then, they’ve made various design iterations and improvements to the machines.

Schätti said overall they have kept the analog look of the machine. He said while some companies might focus on innovation and progression and changing the look, Olympia Express has stuck to the traditional appearance of their machines while also increasing the choice of materials to provide the highest quality product possible.

Though Schätti’s combined businesses have around 12 employees in the engineering department, Olympia Express only requires one engineer. Schätti said Solid Edge is extremely easy to learn and includes helpful tutorials within the interface. They have also developed an in-house tutorial for their draftsman interns, typically ages 15 to 16, where they cover the fundamentals of Solid Edge, enabling them to model a small mechanism and produce a drawing within three days.

To illustrate how easy it is to learn Solid Edge, Schätti uses his daughter as an example. She was able to design and 3D print a tamper base, pictured below.

He said one of the benefits 3D modeling with Solid Edge provides is that it speeds up the design process and reduces the number of prototypes. They can easily model and quickly 3D-print a prototype to get a better feel of the components in various materials.

Since they use a lot of sheet metal in the machines, having a tool like Solid Edge that includes a module for sheet metal was very important to the company.

They also use Teamcenter for design review and to manage design revisions. “It becomes even more important to be able to maintain the data than for the engineer to create it,” said Schätti.

He said they also use the rendering capabilities for internal communications. In addition, they sometimes service machines that are 50 or 60 years old, so they are very fond of the exploded views capability in Solid Edge. With exploded views, they can provide documentation on how to assemble and also how to service the machines so the servicing department can see which spare parts they need and how to replace them. “That’s where Solid Edge has proven very helpful, as well: making exploded views to create documentation on how to replace parts and how to assemble them initially,” he said.

These high-tech machines may not be for everyone as they command a hefty price. They start around US $3,000. They offer three machines (available in three colors) and one of the most sought after is the Olympia Express Cremina. The New York Times wrote the “Cremina 67, a lever-operated machine designed in 1967, is ‘the best Espresso machine in the world.’”

In addition, Olympia Express offers an updated version, the Cremina SL, and the Maximatic semiautomatic machine that provides espresso in just a few steps. Lastly, they offer the Moca espresso grinder.

Despite the name and logo, Schätti said Olympia Express is more in the “slow foods” category. “It’s more of a celebration to make coffee and make an espresso as it takes time to prepare it. And the quality of course, it’s really good. It’s like the small machines can really make coffee like the quality of a barista machine in an Italian coffee bar,” said Schätti. “The size is just a fraction of the professional machine, but it is built like a professional machine. The materials are the same as in the big machines the baristas have. It’s like having a home barista.”

About the price, Schätti said if coffee is important to you, you will spend a lot of money on a coffee machine if it’s the right one for you. He often justifies it to his mountain biking friends saying it’s similar to how they spend five or six grand on a mountain bike.

He said the Olympia Express machines are definitely on the wish list of many serious coffee lovers. Most must save up to make the purchase.

Schätti also has a wish list for Solid Edge. What Schätti said he’d like to see in Solid Edge is the ability to test temperature stability better. “Temperature stability is a very, very, very important point and temperature control is a very important thing in coffeemaking and maintaining a stable temperature. We are lucky that we inherited machines that can hold a temperature. But if you want to design new machines you must in fact reach the same quality level and simulating temperature distribution and flow would be really helpful. It’s on our wish list.”

Speaking of wish lists, the Olympia Express machine is on ours at engineering.com.

Visit Siemens to learn more about the Solid Edge for Startups program.

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Written by: Dale Tutt and Jim Thompson, Siemens Digital Industries Software

The medical device and pharmaceutical industries are emerging from a period of unprecedented challenge and upheaval. Supply chain challenges and growing complexity have impacted the industry at a time when speed and agility have become critical in the delivery of new therapies, devices and drugs. Meanwhile, the management of risk and protection of patient safety and privacy have remained paramount despite shortening timelines.

This disruption, however, has sparked a wave of innovation and ingenuity in the design and production of medical devices and pharmaceuticals. The addition of digital elements, electronics and software to the life sciences industry has powered the creation of smarter and cheaper medical devices and accelerated the development of new drugs. The ongoing digitalization in the delivery of healthcare is also laying the foundation for a shift in how care is delivered, where home is becoming the new hospital.

Medical device and pharmaceutical trends in the post-pandemic era

The pandemic highlighted the significant built-in risk associated with highly global supply chains; which had become the norm across industries. The pandemic also prioritized condensing already shrinking development cycles. Disruptions to semiconductor production also hampered the production of various devices, vehicles and equipment. This impacted nearly every industry as computing and electronics approach ubiquity.

Meanwhile, the rapid development of the COVID diagnostic tests and vaccines demonstrated to the industry that faster timelines can be achieved within the current regulatory framework, catalyzing device manufacturers and pharmaceutical companies to search for better means of managing the complexities of drug and device development to minimize development cycles.

Shifting care from hospital to home

Methods of delivering care have also undergone some upheaval in recent years. Hospital beds are in high demand as many populations age and require increased care. With limited space and growing costs in traditional hospital and clinical settings, the ability to deliver high quality care remotely and to support patients outside the confines of the hospital is becoming crucial.

This requires increasingly intelligent, robust and safe medical devices that can be distributed to patients for use in their homes or on the go. For device manufacturers, the result is increased complexity in the design, engineering and regulatory clearance of new devices to ensure safety, efficacy and ease-of-use by untrained users. For example, smart glucose monitors are now available that can be worn or implanted, helping diabetes patients manage their condition more easily and reduce episodic emergency clinical care. The smart device continuously monitors blood glucose levels, displaying this information for the patient to use in tracking their blood sugar and its responses to diet, exercise, stress and other factors.

The age of software-defined care

Software is an increasingly central piece of the delivery of modern therapies, and this will only heighten in the future. Software is everywhere in life sciences and healthcare. It is used in the design, production, operations, delivery and management of devices and drugs. Software is also being used increasingly as a medical device itself. The integration of software into such a multitude of processes has enabled remarkable advancements in the technology of medicine, aiding in the diagnosis and treatment of disease, the management of chronic conditions and the organization and security of patient data. Yet, the growing application of software and digitalization in the medical space incurs some challenges.

All this software, including updates whether used by a medical provider or by a patient, must undergo rigorous certification and risk assessment to ensure patient safety and efficacy. Furthermore, smart medical devices, software as a medical device and even wellness devices such as a smart watch all produce or manage highly sensitive patient data. Manufacturers must therefore adopt rigorous data security and risk management protocols with the same attention and investment paid to traditional innovation pathways for medical products and therapies.

Digitalization fosters improvement and innovation

Digitalization and digital twins are being adopted by the medical industry for their ability to improve underlying engineering, design and data management processes. This becomes particularly important as devices become more complex, smart and software-defined. Medical devices are an especially good fit for advancing the digitalization of design, engineering, testing and production as many are discrete-manufactured products, created in the same way as cars or smartphones — two examples of industries that are viewed as leaders in digitalization. The comprehensive digital twin of a new medical device enables engineers to simulate and predict multiple aspects of device performance, such as heating or power consumption, to ensure reliability and safety. Likewise, digitalization is a boon to the development of surgical robotics that are highly precise and robust, enabling surgeons to perform delicate tasks with confidence.

Due to the potential advantages digital twins can offer, digitalization is top of mind for medical device and pharmaceutical companies. Yet, the necessity for safety and minimizing risks, and the high regulatory standards with which new devices and drugs must comply, have prevented these industries from embracing digitalization on the same timeline as others, such as the automotive and consumer electronics industries. For manufacturers of medical devices and drugs, the potential benefits of a new technology or methodology must be sufficiently evaluated and proven to displace a legacy process due to the importance of preserving product quality and patient safety.

So, looking to the future, how can engineers and designers expect digitalization and the digital twin to advance in the medical device and pharmaceutical industries to further adoption?

The evolution of digitalization in the delivery of new therapies

Broadly, continued investment in digitalization will open new opportunities for the application of the digital twin, automation, artificial intelligence (AI) and the industrial metaverse with respect to the development of medical devices, implants and pharmaceuticals. The application of these technologies will be crucial for increasing the productivity of healthcare systems and improving the remote delivery of care. As space in hospitals becomes increasingly limited, especially for aging populations, the ability to provide effective care whether in the hospital or at the home will be vital.

The pharmaceutical industry is already taking advantage of AI to analyze drug performance to better understand how the body absorbs different medications. The regulatory approval of a new pharmaceutical requires multiple stages of testing in both laboratory and clinical settings. These trials typically produce large data sets on the drug’s efficacy, side effects, risks and patient data. AI is being used to examine extremely large data sets, looking at biomarkers to characterize the complex interactions between individual anatomy, genetics and the chemistry of the drug being administered. By considering results from multiple studies involving several drugs, biologists and physicians can use the power of AI to uncover interactions that may not be obvious in any individual study, resulting in drugs that are safer and more effective.

Looking further into the future, the industrial metaverse may be applied to the delivery of healthcare to reduce the time patients spend in care facilities, costs and improve condition diagnosis. A typical doctor visit may transition to a remote consultation in which the physician can examine a digital twin of some part of the patient’s anatomy, helping both the provider and patient better understand the patient’s specific disease and treatment options.

Furthermore, the work already being done with AI and pharmaceutical development may enable physicians to customize a drug or therapy to the patient’s anatomy, genetics and condition, accentuating health benefits and mitigating side effects. Routine wellness visits may also be more effective at catching conditions early, preventing lengthy and expensive hospital stays and improving patient outcomes.

A digital future for medical devices and pharmaceuticals

As medical device and pharmaceutical manufacturers emerge from a disruptive and uniquely challenging period, they face a future of growing complexity, speed and innovation. Medical device makers are managing the development of devices that are smarter, increasingly complex and more user friendly. This places additional strain on traditional design and engineering methods. Meanwhile, the need for speed of innovation in both device and pharmaceutical spaces has elevated, driving manufacturers to seek process improvements that can accelerate development cycles while preserving safety, efficacy and data security.

Digitalization and the digital twin are relatively nascent in the medical device and pharmaceutical industries. Yet, with a commitment to digitalization and the application of powerful technologies like the digital twin, AI and the industrial metaverse, manufacturers of medical devices and pharmaceuticals can build a foundation for transformative capabilities that will enable them to develop new generations of devices and therapeutic drugs. These advanced therapies will contribute to a system of care that is more productive, accessible and effective.

About the Authors

Dale Tutt is Vice President of Industry Strategy at Siemens Digital Industries Software. Dale leads a team of experts to develop and execute industry-specific product and marketing strategies in collaboration with the global product, sales, and business development teams. With over 5 years of experience in this role, combined with extensive experience in the aerospace and defense industry, Dale has a deep understanding of the challenges and opportunities facing companies as they embark on their digital transformation journeys. Connect with Dale on LinkedIn.

Jim Thompson is the Senior Director of Digital Strategy for the Medical Device and Pharmaceutical Industries at Siemens Digital Industries Software. Jim has three decades of experience in product lifecycle management and solutions development. His current role focuses on strategy and solution management for the Medical Device and Pharmaceutical industries. Connect with Jim on LinkedIn.

The post How digitalization in medical devices and pharmaceuticals sparks innovation appeared first on Engineering.com.

Written by Todd Tuthill, Vice President for Aerospace and Defense Strategy and Marketing, Siemens Digital Industries Software.

The enormous breakthroughs artificial intelligence (AI) has had in the past couple years are common knowledge at this point. AI itself is nothing new, but recent advances in hardware and GPU technology have enabled the operation of more sophisticated machine learning models that can handle larger amounts of data and make better predictions, pushing AI past the hype threshold into a legitimate tool to do business with.

These advancements cannot be understated as there is a massive demand for AI, opening numerous opportunities across industries.

Aerospace and defense (A&D) is not only an industry that can benefit from AI, but also one that needs it. Despite the immense growth the A&D industry is expected to profit from in the coming years, a growing worldwide workforce shortage threatens to hinder this progress, exacerbated by increased product complexity from the integration of new technologies. If A&D companies wish to stay in business in the future, they will find AI to be a necessary component in accelerating their digital transformation journeys and producing the next generation of aircraft and spacecraft.

Expecting turbulence

There is a lot to look forward to in the realm of aerospace. For instance:

• Companies are researching different forms of sustainable propulsion.
• Engineers are designing the next generation of defense aircraft.
• The United States is poised to return to the Moon and land humans on Mars.

The future of aerospace has never looked more exciting.

Yet these ambitious goals rely on new technologies, and with these new technologies comes increased product complexity. Sustainable propulsion systems for commercial aircraft and enhanced networking capabilities for drones — for example — present new design considerations that must be integrated within the same or shorter design cycles. Additionally, as product complexity increases, so does tool complexity, as new powerful software is developed to design and validate everything from the smallest microchips to wing shapes. All these considerations and tools will take engineers years to learn, and even longer to become experts in.

This comes at an inopportune time as the industry struggles to overcome a worsening workforce shortage that is spanning the globe. The Boston Consulting Group predicts that by 2030, one out of three engineering positions may go unfilled due to a lack of required skillsets. The A&D industry is expected to create all these incredible products and innovations, but it lacks the engineers to bring them to fruition.

Transforming engineering with AI

AI has the potential to counteract the worst effects of the workforce shortage and growing product complexity. At an individual level, it gives engineers new ways to do their work, transforming how they interact with tools and enabling them to gain knowledge and experience faster.

Many industries are already integrating AI copilot systems into their tools, which engineers can utilize, through conversation, to make their jobs easier. By simply asking questions with natural language, engineers can have the copilot automate workflows and clear mundane tasks, as well as quickly access the copilot’s library of specialized knowledge to better understand the software tool. This lessens the need to have an expert on hand to guide newly hired engineers.

Companies can take a step even further by integrating AI directly into design tools. Not only would the AI be able to learn from expert users of the software tool, but it could also use those experts’ workflows to streamline the use of the tool itself. This enables the AI to develop a set of best design practices to help engineers with everything from component placements to wing shape optimization. With AI helping develop better workflows, engineers will be in a better position to navigate product complexity.

All these applications culminate in multiplying the impact of engineers, allowing them to focus on higher-level engineering and critical thinking as AI handles the mundane work. Not only would this reduce the intensity of the workforce shortage, but it would also create a more attractive working environment to draw in new engineers.

Addressing trust

Despite the excitement surrounding AI, some people are still bound to be skeptical of the technology. There are those concerned about AI being unable to effectively do work that has long been done by humans and the results it generates, while others are concerned with AI’s ability to keep proprietary data secure. The latter is especially important in the A&D industry, as many programs are data sensitive or outright classified.

While these concerns are valid, they are not so different from when previous technological innovations changed how work is done. Therefore, they can be addressed. Efforts can be made to reduce the black box obscuring how an AI comes to its conclusions, as well as educate users to better understand how AI functions and where it could be best applied. Regarding proprietary data, instead of drawing on public information from the Internet, like ChatGPT, the AI of an aerospace company can be run locally. This way it only learns and accesses data from the company’s secure and proprietary data lake.

AI in digital transformation

The capabilities described earlier in this article are just the tip of the iceberg. At a higher level, AI can be the key component to help accelerate companies’ digital transformation journeys to levels once thought impossible.  

Most companies in A&D have already begun their digital transformations, making inroads in configuring model-based workflows and data archiving, as well as connecting data across engineering domains and increasing traceability. With AI, however, companies can go even further. Automating mundane tasks and streamlining engineers’ workflows with copilots and tool assistants is just the beginning. AI is already being used to generate component designs, write support documents, find optimized solutions and perform many other tasks that we once thought only humans could do. Right now, these capabilities are limited to single engineering domains, but with every leap in AI technology, they grow closer to applying to full multi-domain physics models.

As products increase in complexity and the aerospace workforce gets tighter, AI is positioned to enhance the work of human engineers and enable the A&D industry to overcome its obstacles. By the time 2030 approaches, AI will have dramatically altered the way A&D does engineering, and there will be two types of companies: those who have gone out of business and those who embraced AI.

About the Author

Todd Tuthill is the Vice President for Aerospace and Defense Strategy and Marketing at Siemens Digital Industries Software. Todd’s engineering background is in systems design with functional engineering and program leadership roles and a strong vision for digital transformation. His 30+ aerospace leadership career spans McDonnell Douglas/Boeing, Moog, Raytheon, and Siemens. His experience encompasses all aspects of A&D programs, including design, model-based systems engineering, software engineering, lean product development, supplier/partner management and program management. In his role at Siemens, he is a passionate advocate for the advancement of digital transformation across the A&D industry.

The post Why aerospace needs artificial intelligence appeared first on Engineering.com.

Written by Nand Kochhar, vice president of Automotive and Transportation for Siemens Digital Industries Software.

The U.S. autonomous car market size is estimated to reach $37.56 billion by 2029, growing at a CAGR of 20.5% between 2024 and 2029. However, the widespread implementation of automated vehicles (AVs) in the real world (Figure 1) depends on the verification and validation (V&V) for designing, manufacturing and of course, operation. Hence, AVs must safely and reliably drive on roads in all weather and traffic conditions on urban, suburban and back-country roads.

To continue the march towards autonomy, companies must transform the methods with which vehicles are developed and put into the market. While great opportunity exists in the future, companies face several impediments to supplying connected, automated and software-defined vehicles to the world.

Building AVs is a particularly complex process because the entire vehicle is a system of mechanical, electrical, electronic, network and software systems. State-of-the-art components from each of these domains are required to create the most sophisticated vehicles ever produced.

Thus, the V&V of AV safety, reliability and performance in all traffic scenarios is a daunting task. Projections indicate AV platforms will need to complete the equivalent of billions of miles of testing to ensure their safety and reliability. And, because the vehicle involves interconnected and interdependent systems, the complexity compounds.

Connecting real world and simulation data via digitalization

The development of advanced driver assistance systems and AVs is a data-driven engineering process. Numerous measurements are generated, analyzed and incorporated back into the design at each step in the lifecycle. Translating the raw data gathered into engineering insights that drive improvements and optimizations is where competitive advantages are won and lost.

Companies can successfully address the complexities of these challenges by using a mixed-reality, digitalized approach to the development, building, verification and validation of their vehicles and driving systems.

Real-world data collection is critical to providing accurate V&V. Typically, the information collected from a physical test is immense. Data-collection software can perform an initial analysis to distinguish what is pertinent to the testing objective at hand. This enables teams to make well-informed decisions on data storage and processing priority.

Essential hardware elements, sensors, actuators, controllers or complete autonomous driving systems need to be tested. Hardware components are verified and validated using simulators to mimic on-road driving scenarios with varying environments, traffic and road conditions.

Hardware-in-the-loop simulation software can help with the testing of sensor systems. These solutions can test camera-based perception systems used for advanced driver assistance systems by integrating the actual camera sensor into the testing environment. This helps increase simulation fidelity because the system is processing real sensor data. Camera projection boxes, as well as camera injection setups, allow engineers to test the camera-based perception systems under challenging conditions.

This information becomes more powerful when converted into the virtual domain. Real-world tests enrich and inform simulated vehicle environments and driving dynamics. Scenarios captured during real-world testing can be imported and recreated in a simulation solution (Figure 2). Within the simulation environment, engineers can change parameters of the scenario (such as weather conditions or vehicle speeds) so that they can interrogate all facets of system performance in different driving environments.

Using data from physical testing and simulation, AV engineers can quickly identify on-road edge cases and assess vehicle behavior in all driving scenarios. As the AV is an integrated system, its development platform also needs to be integrated to test and retest the operation of the vehicle in realistic virtual scenarios throughout the design process. By implementing both design and simulation on one platform, test results and simulation data can be readily reincorporated into the vehicle design. This produces a closed-loop feedback system that improves not only the design but also operation and physical characteristics of the vehicle.

When this approach is used, a comprehensive digital twin of the AV then can be created. This empowers an efficient closed-loop AV development lifecycle that spans from design to verification and validation and even through to in-field maintenance.

Encountering unsafe scenarios in a virtual environment, not on the road

High-fidelity simulations using a comprehensive vehicle digital twin also provide a virtual environment for identifying unknown unsafe scenarios. Engineers can combine the knowledge from known real-world situations with mathematical prediction and simulation methods to uncover possible alternative critical scenarios. They can discover and analyze these scenarios more efficiently in a virtual environment, reducing the number of unknown-unsafe scenarios and the risk incurred when deploying AVs.

As stringent regulations are adopted by governments that focus on road safety, they will likely guide the future of virtual vehicle testing and simulation technology standards to help streamline consistency and acceptance of AV certification.

Collaboration is key to building confidence

In the United States and worldwide, standards were developed for vehicle certification. As regulations matured, confidence in the products was built. The engineering environment for AVs also needs to build that confidence, to prove the connection between physical testing and virtual testing so that the authorities can confidently approve standards that will benefit everyone, manufacturers and customers alike.

Three areas are crucially important to fully autonomous vehicles being introduced successfully: public acceptance, technology and regulations. The automotive and transportation industries have a challenging task ahead of them to address the needs of all three areas.

Standards should help balance out this tension between technology and regulations. They would help guide companies, like Siemens, that develop tools for AV manufacturing companies. These tools could then help ensure that the standards are in alignment with the technology that they are developing and vice versa. Standards, and the verification and validation they call for, may be driving the speed with which we will see AVs become common.

The process of designing, building, testing and scaling autonomous cars is complex and time-consuming. As such, advancement must rely heavily on collaboration and partnerships, which will drive innovation, improve functionality and ensure safety for everyone on the road.

About the Author

Nand Kochhar is the vice president of Automotive and Transportation for Siemens Digital Industries Software. He joined Siemens in 2020 after nearly 30 years with Ford Motor Company, where he most recently served as Global Safety Systems Chief Engineer. In this capacity, Kochhar was responsible for vehicle safety performance of all Ford and Lincoln brand products globally. He also served as Executive Technical Leader, CAE and as a member of Ford’s Technology Advisory Board. Kochhar’s tenure at Ford also included executive engineering leadership across a range of disciplines including in product development, manufacturing, digitalization, simulation technology development and implementation.   

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Bob Schuster is no stranger to the automotive industry. The GM veteran saw ‘the writing on the wall’ and decided it was time to make it on his own. Though already a manufacturing expert, Bob quickly trained up on Siemens technology, acquired CNC equipment — and Schuster Mechanical LLC was born. His success in the automotive field, and now adjacent industries, continues to this day.

“I’m small and attentive,” says Schuster. “Jobs that I landed were due to my reputation. I will collaborate with the assigning engineer and make sure the job is completed to their satisfaction. I’m not done until they are happy.”

But this level of success, especially for an individual, does not come unexpectedly. Schuster says that much of what he does is made possible using Siemens Zel X, because it enables him to foster and grow close relationships with his clients.

Schuster Mechanical’s secret to success is helping the “big guys”

Schuster Mechanical takes on the important, but small, jobs that big CNC outlets do not want. “They want volume,” Schuster says. “They can’t do smaller stuff like I can.” However, large manufacturers often need one-off replacements for old machinery. This important but underserved niche is where Schuster Mechanical flourishes.

As a one-man-show, its Schuster’s skills, flexibility and adaptability that separates his shop from the rest. If customers call him to a production facility to help design a tool, replace a broken part or make minor changes to a line, he shows up when needed. “Afterall, you can’t stop production,” he says. “I will see the existing design and propose a new one.”

Schuster’s close, attentive and accurate services do not come cheap. But his skill, work and adaptability speak for themselves. “Right now, there is a shortage of available shops,” says Schuster. “But you can’t do any job at the door. You need to be well suited … [its about] having a good relationship and getting the work you like. I’m not doing precision, ultra precision [or volume] work. I’m doing the other work. You have to match the work at the door to your capabilities.”

When makers match work to their capabilities it goes beyond their personal skills; it also boils down to the tools they use. To overcome most of those capability challenges, Schuster has turned to Zel X.

What Schuster Mechanical makes and the challenges it faces

Schuster mostly makes new grippers or effectors and installs them to legacy equipment. These parts must be custom designed to grab, mount and move parts during manufacturing and assembly.

Often customers only need one part; however, this does not minimize its importance as it may be used to make hundreds of thousands of products a day. These new parts could replace ones at the end of their lifecycle or adapt equipment to make new products — or even multiple products.

“My job is to build the fixture and it’s important for me to design it properly and deliver it on time,” he says. This accuracy and time sensitivity is key. If the part is not ready on time, then it may not be available for installation during a plant’s scheduled maintenance. As a result, the customer could lose millions of dollars waiting for the part. Additionally, if the part does not work as designed, equipment may fail, or products may come out of spec. This would also cost customers millions as they try to fix the issue on the fly.

Another challenge Schuster faced when making new parts for old equipment is that the reference data you need is often inexact, incomplete or non-existent. Sometimes the equipment he adapts could have reference manuals hundreds of pages long and he only needs to extract a few details to make the part. However, if the customer made prior changes to the equipment, that reference manual may be out of date.

Sometimes, the only way to get the information Schuster needs is to see and access the current installation in person. However, accessing the part can be hard and dangerous. During early development, Schuster explains, he must “find a window, maybe 20 minutes, to get in there and take it apart, take pictures, put it back together and take all that data to build my improved part.”

He also often crawls into the equipment a second time to install his new part. During installation, he has “been in [situations where] I thought I had time to finish a project and I was fabricating on the fly … suddenly I was told this line has to run. I had minutes to get it going and fortunately I landed on my feet, and it worked.”

How Schuster Mechanical confronts development cycle hiccups

It is of course best to avoid challenges like the ones noted above. Though issues will arise with any project, Schuster and his customers can foresee and address most of them early in development using better means of communication.

“I address these challenges by being honest with the customer,” says Schuster. “When I look at the data and there is a misunderstanding … I call the engineer right away and verify.” He notes that after one of these moments of honesty, the customer involved ordered eight new parts the very next day. “Pointing out the error made them happy; they trusted me.”

Schuster also uses Zel X to bridge communication gaps with customers and peers. “Zel X makes collaboration instantaneous. [With email you] needed to export CAD, describe it in PDF and the person on the other end needs to interpret that. That’s a time-consuming process. With Zel X, I still need to do some of the same steps … but instead of an email, the data updates and shares. We can communicate on the phone while looking at the same data and make decisions faster.”

In fact, this improved form of communication has enabled Schuster to reduce on-site visits. “I get to design much sooner,” he says. “I have the ability to do a quick design review and get started. These were done in person before. Now this happens much less.”

Zel X also has tools that stakeholders can use to markup, comment and revise data. As a result, Schuster is up to date on what the customer is up to, and his customers knows where he is with a project. Thus, the design process runs smoothly without in-person interactions. “The markup tools are built-in and we are able to talk back and forth and steps are recorded. Misunderstandings were honest before, but now you can see it written so we understand and finish much faster.”

When things do go wrong, Zel X is also helpful. It enables Schuster’s customers to keep him aware about how his parts are running. “The sooner I know it’s successful the better,” he says. “I need feedback on any concerns right away. Zel X is the way to do that. [With it, customers can] communicate with me like in the review process. If something was overlooked — sometimes by one or both of us — it’s usually very simple to punch out these little details. It’s easier to correct these faster [in Zel X] than with email.”

What else does Schuster Mechanical do with Zel X?

Another big challenge machine shops face is ensuring that a customer’s data is secure, tracible, accessible and available in a way that fosters collaboration between stakeholders. It has built-in tools for collaboration. When I put data into it, its more secure than it was before.”

Schuster also uses features in the software for quoting, restricting data access (based on a user’s credentials) and storing information safely on the cloud. If he or any stakeholder needs to look up data on the system, they must use two-step authentication. This ensures users only see the data they have permissions to see.

“It’s a step in the right direction and long overdue, like 20 years overdue,” he says. “The methods we shared things with in the past weren’t good.  Almost all shops can do the work effectively, but customers need to be comfortable their data is in good hands. They trust Siemens to be a good steward of their data, and I think they are. That’s a good thing for everybody.”

As for Zel X’s quoting abilities, Schuster says, “The quoting process marches along much more efficiently and quickly. It can shorten the quoting process and make it more comfortable and safer for both parties … Everything is recorded.

To learn more about how Zel X, click here

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