Feeding the world isn’t sustainable — yet. According to Our World in Data, a collaborative of researchers from the University of Oxford, in 2018 the global population numbered 7.6 billion. Today, it surpasses 8 billion. To feed everyone in 2023, 45% of the world’s habitable land was used for agriculture. However, 80% of that land was dedicated to producing feed or grazing animals, supplying just 17% of the world’s calorie intake.

To experts like Marc Boland, Chief Executive Director at Nasekomo, this model is unsustainable. His solution? Insects. But don’t worry — humans won’t be eating them. Instead, insects can transform organic low-value mass into biomass, providing feed for livestock and fertilizer for crops in a sustainable and circular process. “The traditional way of production can’t address these challenges,” says Bolard. “That’s why we founded Nasekomo, a name which means ‘insect’ in Bulgarian.”

“This is an emerging industry we are pioneering,” summarizes Bolard. “There are a lot of scientific and R&D activities needed to achieve the best outcomes for efficiency and profitability. We are inspired by natural ecosystems to develop next generation circular food and beverage value chains. Our motto is, we’re created by nature. We’d like to address and fight global challenges related to continuously growing a healthy population. Insect bioconversion is our first use case to prove how the synergies between biology, technology, AI and market players can address food production global challenges. Thanks to Siemens technologies, we are developing new highly digitalized artificial intelligence driven automation for industrial scale farming operations.”

Harnessing nature’s most abundant protein source

Insects dominate the planet’s animal biomass, making them a natural source of nutrients for the food chain. According to Smithsonian Magazine, animals make up about two gigatons of Earth’s 550 gigatons of biomass. About half of that animal biomass is made up of insects. This abundance has made them a key protein and nutrient source for livestock, poultry and fish farming.

Nasekomo aims to merge the lifecycle of the black soldier fly with custom designed, manufactured and optimized industrial technologies. “Partnering with Siemens Digital Industries Software, our company ensures precision and efficiency in its operations,” underlines Stefka Mavrodieva, Chief Digital Director at Nasekomo.

The black soldier fly lifecycle and industrial technology

Black Soldier Fly (BSF) farming starts with organic biomass processing byproducts from industries such as breweries and food production facilities. Rather than disposing of this biomass, Nasekomo feeds it to fly larvae, which grow 10,000 times their size in just 12 days. These larvae are then processed into high-value products like defatted bio-protein meal, insect oil, organic fertilizer and fresh larvae — ideal for animal feed, aquaculture and pet food.

Nasekomo designs the equipment to feed, nurture, grow, process and extract products from the BSFs. “We’re not just a bug-farming company,” says Mavrodieva. “We are technology innovators, creating hardware and software solutions applicable to other controlled agricultural environments like mushroom or tomato farming.”

Unlike other insect farming operations that use crates, Nasekomo automates the process end-to-end. “Nasekomo develops a zero-crate approach involving a scaling farming technology which creates the perfect environment for the insects to deliver their exceptional biological potential. We manage every aspect to ensure we get the quality and quantity needed,” Mavrodieva emphasizes.

Optimizing insect farming with simulation, AI and Siemens Digital Industries Technologies

There is no universal method for optimizing BSF growth. Living organisms require precise environmental conditions, which is where Siemens technologies come in. “Without Siemens, we can’t manage this properly,” says Mavrodieva.

Nasekomo’s systems integrate AI and big data analytics to predict and prevent issues before they occur. Siemens software, including Solid Edge and Tecnomatix, enables virtual testing of factory processes to identify bottlenecks, optimize workflows and ensure efficient operations before physical implementation.

Before moving to detailed design, the team conducts biology experiments and simulations to ensure the organism’s health isn’t compromised. “As I shared previously, we use custom industrial technologies designed and developed to serve the needs and specifics of farming living organisms,” Mavrodieva explains. “To best manage activities related to those processes, we benefit from Siemens’ Solid Edge.”

Solid Edge, part of the Designcenter suite, enables faster design iteration, allowing for quick modifications and rapid prototyping. It also integrates with a PDM system, ensuring efficient data management, version control and easy access to design information. “Solid Edge is almost never crashing,” Mavrodieva remarks with a smile. “Stability is important when it comes to saving time and avoiding data loss.” Additionally, its precise drafting tools help engineers create detailed 2D drawings and annotations with accuracy.

Building a global franchise with insect farming

The company also aims to embrace a franchise model as it expands. This way local feed sources can meet the global demand for food quantity, quality and sustainability. This requires building numerous factories around the globe, which in turn requires standardization to ensure fast scalability and make sure each factory will operate smoothly in every location. In their industrial demonstration center near Sofia, Bulgaria, they are focusing on optimizing the larvae phase of the BSF lifecycle. Siemens Plant Simulation — an event-based, process simulation software powered by Tecnomatix — is used to simulate these facilities within these local environments.

“We simulate the process, the equipment and the target capacity before putting it into the real world,” says Mavrodieva. “We find bottlenecks and eliminate them in the virtual simulation, to reduce the risk of downtime and failures when building the factory in the real world. The insects can’t wait. To ensure the efficiency of the factory, our approach is to go to the virtual world to confirm our thinking and then translate it to the physical world.”

These simulations are also used after a facility is operational — only now it acts as a digital twin. The factory’s twin can predict the performance and help manage a facility. This is quite helpful, as process changes can be improved and tested on the digital twin long before it has any effect on the flies.

Mavrodieva explains that Nasekomo has made Siemens digital technologies a part of its DNA. As such, it never had a digital transformation. Instead, everything has been designed with Siemens’ digital framework in mind. “When we design equipment, we need to make sure we have the right tooling to manage the process properly in real time and for longer term optimizations.” As a bonus, this digital framework is also ideal to meet the high standards, documentation and regulations of the feed industry.  

By digitally keeping track of its processes, Nasekomo can also confidently recount its environmental impact. “We have an entire team taking care of this,” says Mavrodieva. “We continuously do LCAs (lifecycle assessments) to assess the impact and our footprint. We’ve done multiple analyses with the help of third parties. Research shows we have a significantly less CO2 footprint, land use and water use than traditional feed production.”

Intelligent operations with AI & SCADA

“At Nasekomo, we are building something that will transform the entire insect industry,” Mavrodieva states. “Some of our solutions are our own unique designs. We are building intellectual property and introducing significant differences compared to traditional, unsustainable approaches.”

For real-time monitoring and process optimization, Nasekomo relies on Siemens’ SIMATIC WinCC Open Architecture and its SCADA platform. “WinCC OA is really essential in our day-to-day operations,” says Mavrodieva. “SCADA systems are used to monitor and control physical processes on a large scale and over long distances. We could literally not survive without having the proper tools and capabilities to support not only our daily tasks but also our future ambitions, which are quite high.”

WinCC OA, combined with Siemens controllers, integrates with a centralized cloud management system where AI-driven algorithms oversee long-term process optimization. This integration enables real-time decision-making, predictive maintenance and continuous efficiency improvements. By leveraging cloud connectivity and AI, the system enhances operational performance, reduces downtime through predictive insights and drives ongoing process refinement based on real-time data and historical trends.

Seamless collaboration with teamcenter

Another key digital pillar at Nasekomo is Siemens Teamcenter. “Teamcenter is the underlying layer across different Siemens modules,” Mavrodieva explains. “It’s a Product Lifecycle Management (PLM) system that ensures smooth collaboration and data management across different teams and processes.”

With Teamcenter, Nasekomo maintains version control for designs, streamlines workflows across distributed teams and synchronizes operations as new facilities and technologies are developed. “For businesses that are globally distributed, this is a must!” Mavrodieva emphasizes.

The role of Siemens in a future made by Nasekomo

Siemens became a strategic partner of Nasekomo because of the unique opportunity to produce a greenfield, digital, industrial landscape. The Siemens tools Nasekomo uses include Solid Edge for design, Tecnomatix Plant Simulation X for process verification, SIMATIC WinCC Open Architecture for AI-driven operational optimizations, and Teamcenter for seamless data management and collaboration.

“We don’t go to any other provider when it comes to industry, design and platform tools,” Bolard says. “What we are doing is quite new, so we need the right experience at the table. Siemens is a trusted partner. When we need to make a decision, we sit together as a team and come to the best solution.”

Visit Siemens to learn how they can help bring a greenfield industrial 4.0 process into reality.

The post Don’t Scream: A unique protein source may save the world appeared first on Engineering.com.

It’s not surprising or new – the need for highly skilled engineers is reaching an all-time high, driven by the requirement to continuously improve everything from medical devices to automotive design, artificial intelligence, robotics, data analysis and more. According to the Bureau of Labor Statistics, engineering employment is expected to grow 7% over the decade from 2021-2031, while SAE International forecasts that one in three engineering roles is expected to remain unfilled through 2030, due to the workforce’s lack of relevant technological skills.

And as the technologies in the engineering industry continue to advance, this gap will only grow wider if left unaddressed.

“The shift toward more automation and simulation, digitization, the use of data – all these technology-driven changes are speeding up,” says Sean Gallagher, director at Huron Consulting Group. “This places demands on professionals to stay current by keeping up with these shifts and the changing demands of the job market.”

Add in demographic changes from workers aging out of the occupation and retiring, and the result is an innovative industry that needs skilled engineers at a rate outpacing the amount of talent graduating from colleges and universities. 

From undergraduate degrees to licensure and continuing education, becoming an engineer takes time. 

“The pipeline to become an engineer — and depending on the type of engineering, to become licensed — is a long one,” Gallagher says. “So, there are only so many people who are qualified to fill these jobs. And there’s a lot of demand for these jobs.”

If this skills gap isn’t addressed, Gallagher says, this could lead to waning progress.

“We won’t have enough engineers to build the things we need, the pace of innovation will slow down, you’ll see shortages and other undesirable events in the job market,” he says. “A skilled engineering population is at the core of so many different industries and areas of the economy and society. Historically, that’s why national governments and employers, colleges and universities, all these stakeholders around the world, worked to develop the engineering workforce and invest in that important pipeline of talent.”

Closing the gap with microcredentials

One emerging path forward to help close the engineering skills gap is microcredentials.

The term ‘microcredential’ is something of a catch-all, Gallagher explains. It describes a credential that is more compressed than a macro-credential (e.g., a traditional college degree).

“What we’re talking about is a skill-focused, targeted, short educational program,” he says. 

These microcredentials can be issued by colleges and universities, but also by companies, non-profits, and training organizations. Because of the focused and targeted nature of microcredential programs, they can be a useful way for engineers to refine and verify their understanding of specific skills — whether that’s AI programming, change or process management, or a specific software or tool — without the time investment and expense of degree programs. And because microcredentials are designed around industry needs, they can bridge the gap between engineering theory and practice, and provide upskilling and reskilling opportunities for engineers looking to expand their capabilities.

Moreover, in the wake of pandemic-era shifts to online education and a desire for flexible learning opportunities, many microcredential programs are fully digital and can be completed online. This means they are accessible, affordable, and easily shareable on a resume or LinkedIn profile through verifiable achievements in the form of digital badges and certificates that link to information about the credential and completion confirmation.

These digital microcredentials also slot in well with the shift toward more inclusive hiring strategies that encompass a focus on prioritizing skills and talent assessments, rather than strictly hiring based on degree achievement. 

“Most major engineering employers have all kinds of excellent internal training,” Gallagher says. “But if those don’t result in a credential of some sort, how can a worker use that, in terms of portability and recognition in the job market?” 

Microcredentials fill this niche, giving workers a way to present and demonstrate their abilities, and giving hiring managers an easily verifiable way to assess candidates’ skills during the hiring process. 

In short, microcredentials are key to closing the engineering skills gap, by providing greater visibility into the skills engineers possess, making it easier to digitally share and verify proof of these skills, and ensuring engineers are up to date with the most current training and information for their field.

For a deep dive into how microcredentials bridge the skills gap in engineering, read the Huron-produced and Siemens-sponsored white paper Addressing the engineering skills gap with credentials.

The post How microcredentials can solve your hiring challenges and close the skills gap appeared first on Engineering.com.

Engineers in startups and small to medium business (SMBs) wear a lot of hats. This is mostly due to the common product development challenges these new and/or small organizations face:

1. People challenge: the company doesn’t have enough individuals to fill in all the roles.
2. Technology challenge: the company doesn’t have access to every tool to perform optimally.
3. Process challenge: the company relies on undefined workflows with various data silos.

As the ‘technological brains’ behind the operations, engineers are often tapped to find solutions to these issues. This is problematic, as the main objective of engineers and simulation experts is to get products working and onto market faster.

Julien Simon, product manager of NX Performance Predictor, explains how these common challenges can be addressed using simulation-driven design. In this article, he highlights the top three benefits of computer-aided engineering (CAE) — via NX Performance Predictor — for SMBs and startups.

1.     Improved innovation, product quality and performance

The main objective of simulation-driven design is to predict the performance of any given model within the real world. This has multiple benefits including reducing errors in the final design, verifying or testing assumptions and finding flaws early — when they are easier to fix.

Julien Simon uses the example of a design review meeting between marketing and engineering teams. As people see the geometry in the meeting, they may say “it’s a good idea, but they will ask questions. Now you can put your design in a virtual world to assess performance and quality while within the meeting in the real-world. So, you can already give some KPIs in the early phase.”

These real-time assessments around a boardroom not only improve the quality of a design, but they also promote a culture of innovation. If a marketer thinks a product might sell better if it has an extra curve to its profile, then the engineer can test how that curvature affects aerodynamics or structural integrity right before the marketer’s eyes. The engineer doesn’t have to return back to their desk, spend a few days simulating, and then fall behind half-a-dozen other geometry suggestions.

“If you start to discuss the design from the beginning with a lot of people in the company, you can discuss more ideas,” says Julien Simon. “You can discuss it on the 3D design and test it out — live. There are lots of things you can improve when you have a real product in front of you, the usability, the weight, and you can make trade-offs based on what you see. By embracing more proposals of ideas, it allows you to be more innovative and test more solutions.”

2.     Improved product costs and revenue

Simulation-led design offers many methods to save on costs while boosting revenue. Consider the previous examples. As engineers improve the quality of the product — by testing out more innovative ideas from various sources — they are likely to improve a product’s performance in the market. The engineers also improve communications with common stakeholders, simplifying workflows and improving productivity because, as Julien Simon says, “you test the idea as soon as it comes.”

It isn’t just engineers and coworkers that benefit from the ability of simulation-driven design to test customizations; customers benefit, too. “There is lots of customization in products today,” says Julien Simon. “You can purchase a customized bike online and use the simulations from these tools to show the customer if the size of their frame is okay or not. This helps the customization process as it provides guidance.” And with product customizations comes higher sales, market value and customer satisfaction.

From a simulation-driven design perspective, a product’s performance is based on its ability to perform its given task while remaining durable throughout its lifecycle. Thus, engineers can use CAE to improve a design by increasing a products durability or reducing its loads. “It’s a trade-off of weight, cost and durability,” confirms Julien Simon. “That’s why simulation driven design is so important. It’s easier to do this at the start than in the end of the design process as it will cost less to implement.”

Julien Simon also suggests that “70% of costs can be saved using simulation-driven design.” He cites that tools that encourage simulation-driven design, like NX Performance Predictor, streamline development and make better products; this leads to higher sales and lower costs.

He adds that these tools also reduce the cost to train CAE users. What once took weeks of theoretical background and training can now be done in a day. Siemens even offers digital learning tools to help train new users to use CAE.

3.     Improved development workflows and time to market

Speaking of reducing the training to get users to use CAE, this also leads to another big benefit: improving development workflows. There are only so many simulation experts and, in most cases, they are working at capacity. Therefore, if simulation is to help further improve products it must be usable by more individuals.

Julien Simon states there are two ways these improvements can come about. First, simulation-led design tools, like NX Performance Predictor, are designed to work within tools designers are comfortable with. As a result, it can, “demystify the simulation process so more users can use it,” he says. “When you want to broaden simulation to more people, you need to assess the cost of training. For that we see simulation-driven design solutions can be learned in a couple of days.”

The other strategy is to use AI and algorithmic assisting tools like topology optimization. As the AI learns about optimal designs for the industry, it can help guide newer engineers to produce better designs. Meanwhile, topology optimization tools can take input requirements, such as stress, and provide a near-optimal shape based on its given algorithms.

In both cases the output from the tool may not be the final design, but it helps more users get to that near-optimal geometry faster. This way the simulation experts can focus on more pressing models such as final design optimizations, validations and verifications. Additionally, it enables teams to work more concurrently so that a design that might take a week to produce could instead take days.

Why NX Performance Predictor?

Julien Simon notes the importance of high-end simulation to help organizations finalize, verify and validate designs. But for SMBs and startups its important to start with a tool that is scalable, like NX Performance Predictor. This is because it enables engineers to:

• Get prototypes and designs ready as fast as possible.
• Find failures early so that worktime and budgets can be invested optimally.
• Ensure data is collected, used, reused and scaled as the company grows.

He adds that because the tool is integrated into Siemens cloud-based platform solutions it also enables organizations to scale their digital solutions as they need them. “Starting in NX Performance Predictor, you can reuse what you did there, access the licensing system in the cloud to access another tool, and maintain digital continuity. For instance, you can dig deeper into the design in the Simcenter Portfolio as the start-up grows into a larger company.”

To learn more about NX Performance Predictor and how it can scale with a growing organization, visit Siemens.

The post Top 3 simulation-driven design benefits for startups and SMBs appeared first on Engineering.com.

“There are several things to consider here and the first is collaboration,” says Julien Simon, senior product manager of NX Performance Predictor at Siemens Digital Industries Software. “People need to collaborate during the development phase of a product. I think the unique challenge is how can we continue to improve our work and design.”

Processes like simulation-led design, via tools like CAE in-CAD, may sound like they only benefit simulation experts and design engineers. But according to Julien Simon, its benefits ripple throughout an organization. He shared his thoughts on how simulation-led design and CAE in-CAD benefit design engineers, reporting managers and business leaders. He also explains how simulation experts fit into this new design paradigm.

How design engineers benefit from CAE in-CAD — and the role of experts

In the engineering profession there can still be siloes. For instance, the gap of knowledge between designers and simulation experts, or the gap between engineers from one discipline to another.

“To progress in your design workflow, you need to get feedback from each one,” says Julien Simon. “This takes time because you need to exchange information.” This information will often need to be tailored to the workflows of the individual receiving it. Transforming the data this way can be tedious, costly and time consuming. And the person receiving it can’t instantly return feedback. They will need to perform their own analysis, which again, takes time. Meanwhile, feedback is easier to implement into a design when it’s received as soon as possible — so the model is fresh in the designer’s mind.

Consider the workflows between the designer and a simulation expert. Traditionally, the designer would send a CAD model to a simulation expert who will then need to:

• Import the file into CAE software.
• Clean up the geometry to ensure that it is compatible with the study (such as making it airtight for a fluid simulation).
• Cut any small geometry that could overly delay results by increasing computations.

Only then could the simulation expert run their analysis which, like the above steps, could take days to complete.

“For an automotive company to produce a part, they may need five to six iterations between design and simulation,” notes Julien Simon. “The design can take two to three days to iterate, and the simulation can take close to a full work week to complete. So, if you make five iterations, that is about 30 days spent iterating on one part. With simulation-led design, you can go down to just one or two iterations, just 10 days.”

Implementing simulation-led design, by embedding CAE tools within a CAD environment, can significantly reduce development times. Now imagine the same individuals are working on producing a new part. Only this time, the designer has access to CAE in-CAD tools. This brings simulation further to the left side of the product development workflow.

Most CAE in-CAD tools are designed to simplify and speed up the simulation workflow, so that most designers with a typical engineer’s understanding of physics can produce results — without an expert. The expert can still validate the work of the designer, but in the meantime they can focus on more complex simulations.

“There are trends of a lot of moving workflows [expanding the scope of traditional engineers] and a lot of engineering newcomers,” says Julien Simon. “So, to provide CAE tools for a beginner to gain knowledge and confidence, this will improve their work and performance.”

These results won’t be as in-depth as what a simulation expert can produce, so they should still be included in the iterative process, but it should be close enough to guide the designer towards a semi-optimized design. Meanwhile, the simulation expert can focus on more in-depth simulations in parallel to the CAD process. This shrinking of the product development Gant chart, via parallel work, should also reduce the total number of iterations to develop or validate a design.

How do reporting managers benefit from all of this?

It’s the reporting manager’s job to synchronize teams and product development processes. Hence, their fundamental duty is to ensure that what is reported between teams is correct and tailored to their needs. Julien Simon explains that with many simulation-led design tools, a part of this data transfer and knowledge sharing is streamlined.

“I saw that for reporting managers, the challenge was they need to understand more of the design,” said Julien Simon. “They then need to take care of, gain and share that knowledge. With simulation-led design they see more productivity and team achievements. They have more KPIs they can use to validate workflows and simulations. They have more things to overlook, and this gives them a way they can validate the data. That gives them more confidence about the project they are discussing.” From Julien Simon’s point of view, it’s about breaking the siloes between teams, engineers and stakeholders by improving communication.

Julien Simon adds that the experience of some stakeholders can also help others on different teams. For instance, after reviewing a simulation, a plant manager may notice there could be an issue producing the part. This can help guide the design team and avoid mistakes later in development. All this communication can then be facilitated by the reporting manager and the design tools.

Why should business leaders care about simulation-led design?

Julien Simon was very upfront about how to discuss the benefits of CAE in-CAD and simulation-led design to business leaders and C-level management. He explained, “from what I’ve discussed with business leaders, simulation moving left isn’t what they are interested in. They are interested in ‘how my team can become more performant.’”

Thus, it is important to show business leaders how simulations can facilitate communication and support initiatives like environmentalism, cost reduction, faster time to market, fewer product delays and reduced manufacturing downtime. In other words, the goal is to show top management that there is a return on investment when shifting simulation to the left.

In this sense, the argument about how simulation-led design can effectively shrink a product development’s Gant chart — by enabling designers and simulation experts to work in parallel — is something business leaders will pay attention to, not the CAE in-CAD tools themselves.

“We are trying to show them that today, development workflows are seen as a line. You start from the beginning with design, then simulation, then manufacturing and so on,” says Julien Simon. “But in reality, the workflow can be more like parallel tracks. By bringing simulation earlier into the process, it exchanges information at an early stage between these tracks, improving the communications between these tracks and decrease the number of iterations.”

There are also unexpected ways that simulation-led design can benefit business leaders. Julien Simon talks of a time he heard a marketing team discussing a product with designers and simulation experts. He said, “In the end, the marketing team put that simulation in the company’s flier because they saw how impressive it was in the meeting. They wanted to share the company’s confidence in its products directly with the consumer.”

NX Performance Predictor and the future of simulation-led design

Julien Simon explains that Siemens Digital Industries Software offers NX Performance Predictor as its specific CAE in-CAD solution. “NX Performance Predictor helps the designer become more performant by helping them use simulation to evaluate a component. With NX Performance Predictor, the idea is to get instantaneous feedback on a part design.”

What sets NX Performance Predictor apart from others is that its environment is optimized to guide simulation newcomers to quickly learn the tool and produce results. “We are making a lot of effort to make the interface and workflows straightforward,” he says. “It’s naturally embedded in the CAD environment so it’s easy to deploy. They don’t need to train on new software, and they don’t need to transfer data from one tool to another. It’s tools they are used to using daily.”

NX Performance Predictor is also optimized to transfer data and geometry to other tools within the Siemens Teamcenter platform, making it easier to share knowledge and communicate with teams. “We see connections between all the data in the enterprise coming,” said Julien Simon. “And we still want to push people to go a step forward to be more performant in their process.” In addition, the NX Performance Predictor simulation data model can be reused in Simcenter for the expert to validate or enrich the simulation.

Julien Simon also hinted towards new features coming to NX Performance Predictor and other Siemens Teamcenter products. For instance, he explained that AI can help democratize simulation — or at least help the user to be more performant — even earlier in the design process. It can, for instance, guide the user during the definition process. He adds that these AI tools won’t replace engineers; rather, they will act as an assistant, help guide users and offer a starting point that then needs to be optimized.

“AI will never replace humans. Human creativity is an important stage,” Julien Simon says. However, “the path to becoming a better designer is not finished today. I see plenty of improvement coming soon.”

To learn more about the role simulation-led design plays in the day-to-day work of a design engineer, read the Performance Predictor installment in the NX Tips and Tricks series.

The post See how simulation-led design adds value to the whole team appeared first on Engineering.com.

As the clock ticks into 2025, digital technologies will continue to reshape the manufacturing industry. According to a Deloitte Research Center for Energy & Industrials report from 2019, 83 percent of manufacturers believe that digital solutions will revolutionize the way products are made within the next five years. Six years later, the potential benefits have included significant gains in resource efficiency, labor productivity and product quality, as well as cost reductions and advances in safety and sustainability.

Industries are already seeing an increase in the number of companies — from start-ups to small job shops and large OEMs — using digitalization to simulate and optimize their products and processes before they open a new factory. Then, once it is in operation, they can leverage the comprehensive manufacturing data that is collected on the factory floor to anticipate and refine production workflows. Furthermore, they can use this digital transformation to optimize production while increasing sustainability and ensuring their future in manufacturing. After 40 years in this business, I am excited to see how these transformations will redefine what is possible in manufacturing.

Digital threads are the pillars of digital transformation

Many companies in the world have adopted CAD tools for the digital transformation of product design and development. But few have adopted digital transformation capabilities for manufacturing, planning, production, optimization, logistics, supply chain and the managing of services and products in use. These are key areas of emphasis and something that companies need to work hard on. The digitalization of manufacturing and the planning of the production facility ensures that companies are more efficient, effective, resilient and agile.

By embracing a comprehensive digitalization strategy, manufacturers can digitally define entire workflows, enabling seamless collaboration between design, engineering and manufacturing. Products can be designed, simulated and produced in the digital world before doing so in the real world. This can enable a much higher utilization of production facilities, which means increased automation, the usage of more robotics and more smart operations and operators empowered by cloud computing and AI. As evidence, digital manufacturing is already being embraced by many industries, including automotive, aerospace, electronics and healthcare.

Digital threads are the communication links between solutions and are built for various products. These products must be well-connected to “play well together.” Customers see the value in the end-to-end communication chain, but they do not necessarily need to deploy it throughout every process or equipment. It is important to provide flexibility so customers can customize the solution for their specific needs. For instance, a manufacturer can pick a single solution, such as CAD/CAM software, to start and then extend their solutions while creating an end-to-end digital thread. But they need to know that all these different components are going to work together in the digital thread, enabling the information to flow from product development to the shop floor and back.

For example, Siemens created a comprehensive digital twin of its motors and drives factory in Nanjing, China. This enabled extensive analysis in the digital world before making any real investment. By simulating various scenarios like the interactions between robots, AGVs and humans, engineers could optimize processes in the virtual environment. Thus, Siemens was able to reduce costs and ensure that the final facility operated smoothly from day one. This approach not only enhanced operational efficiency but also reduced risk and accelerated Siemens’ time-to-market, providing significant benefits to the company and its customers.

By integrating key digital technologies (such as cloud computing, artificial intelligence, the industrial metaverse and big data analytics) into production processes, industrial companies of all sizes can realize efficiency increases, reducing costs and gaining faster production cycles through real-time monitoring and automated decision-making, improving their manufacturing capabilities.

The impact of cloud technology

Advances in cloud computing are providing the manufacturing industry with scalable, cost-effective solutions that enhance operational efficiency. By enabling seamless data sharing and integration across different departments and locations, the cloud facilitates better collaboration.

The benefits of cloud technology are significant for companies of all sizes. They are even greater for small and medium-sized businesses (SMBs) as it democratizes access to advanced manufacturing tools for the creation of digital twins and to enable the digital thread. The total cost of ownership for software is reduced by minimizing upfront expenditures and ongoing maintenance and upgrade costs. SMBs can use the cloud to leverage digital manufacturing tools without the need for significant IT infrastructure investments. This accessibility enables SMBs to implement digital transformation strategies that might previously have been out of reach — when compared to large enterprises.

Overall, cloud computing is an enabler. By adopting cloud-based solutions, manufacturers of all sizes can achieve greater flexibility and agility, enabling them to quickly adapt to market changes and evolving customer demands, while also positioning them for success in a rapidly evolving industry. Additionally, the cloud supports artificial intelligence (AI) and machine learning (ML) applications, which can optimize manufacturing operations and improve product quality. Additionally, the implementation of high-performance computing can enable companies to do things at a much faster pace.

Harnessing industrial AI

Advances in AI enable manufacturers to analyze the vast amounts of data they are collecting from the shop floor. This provides valuable insights that can improve efficiency and reduce errors. Using copilots or AI to automate complex tasks frees up valuable resources and personnel. This can transform intricate documents into actionable processes that speed up operations and minimize mistakes. For instance, in the digital thread example above, AI is used for the generative design of 3D printed parts and for feature-based machining of complex components using automated CNC programming. This enables non-expert users to complete expert-level tasks without the need for advanced training or years of experience.

By leveraging AI, manufacturers can better utilize their digital twins to predict maintenance needs, reduce downtime and extend the lifespan of equipment. This can help manufacturers with both productivity and sustainability targets. It also can assist companies in making informed decisions about resource utilization, ensuring raw materials are used efficiently and cost-effectively.

Overall, AI has the potential to deliver significant benefits to manufacturers while helping to ease their digital transformation.

In addition, AI and cloud technology will play a critical role in the development and use of innovative technologies and manufacturing techniques, like the industrial metaverse.

Venturing into the industrial metaverse

The industrial metaverse represents an evolution in manufacturing that brings together all the digital technologies previously discussed to transform the future of manufacturing. It leverages the comprehensive digital twins of product and production, combined with information from edge devices and IoT sensors, to deliver a cohesive, physics-based digital environment that mirrors the real world and can be monitored and adjusted in real-time.

By providing enhanced visualization, collaboration and decision-making tools while leveraging advanced GPUs and AI, the industrial metaverse has the potential to revolutionize the industry. It enables companies to experience the digital world as if it were real, helping them to anticipate potential issues and optimize operations before any physical production begins, significantly reducing costs and improving efficiency.

This immersive digital space will be enabled by the cloud, which provides the infrastructure companies need to store and process the massive amounts of data generated by the metaverse. The cloud also facilitates enhanced collaboration as it enables the sharing of data across departments and ecosystems.

Siemens is working with forward looking companies like FREYR, a Norwegian clean battery producer, to demonstrate how the industrial metaverse can help manufacturers collaborate and work in the future.

As more companies adopt this technology, the ability to leverage the digital thread and create detailed digital twins of manufacturing facilities will become standard practice. Customers will benefit from better collaboration and decision-making, faster time-to-market, improved product quality and more sustainable manufacturing practices.

The future of manufacturing

The digital transformation of manufacturing is not just a trend but a fundamental shift that is reshaping the industry. By embracing digitalization, manufacturers can bring together data, cloud computing, artificial intelligence and the industrial metaverse to reduce operational expenses, increase throughput and boost profits. This approach helps them achieve unprecedented levels of efficiency and productivity. This shift not only promises greater agility and resilience in manufacturing but also paves the way for competitive pricing and sustainable practices, ensuring a robust future for global industry.

Visit Siemens to learn more.

About the author:

Zvi Feuer is senior vice president of Digital Manufacturing Software Solutions for Siemens Digital Industries Software, a business unit of Siemens Digital Industries. Digital Manufacturing Software develops software products to deliver world-class solutions for manufacturing (part & assembly) planning, simulation, validation and operations (MES, scheduling, quality and automation).

In his current role, his responsibilities include leading global teams and initiatives to develop and service customers worldwide by providing Digital Manufacturing Software solutions that range from optimization of production and service facilities, design of assembly lines, development and validation of production systems, to the programming of CNC machines for managing production lines and plants.

Feuer has over 35 years of experience in enterprise software business management, with a primary focus in the manufacturing industries domain.

The post Digital transformation impacts manufacturing for the better appeared first on Engineering.com.

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, part of the Designcenter suite, 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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