By now, you probably already know that digital transformation is a manufacturing strategy that integrates advanced digital technologies to enhance efficiency, reduce waste and optimize resource use. But one angle that’s not always talked about is that the right application of these digital technologies can significantly improve sustainability.

Below is an examination into key areas where digital transformation drives environmental and operational benefits. It includes foundational steps for beginners and a few advanced techniques for more experienced engineers further down the digital transformation road.

Data-driven decision-making for sustainable operations         

At the foundation of digital transformation is data collection and analytics, which enable real-time tracking of key sustainability metrics such as energy consumption, material waste and emissions. IoT (Internet of Things) sensors, SCADA (Supervisory Control and Data Acquisition) systems and AI-driven analytics help manufacturers make informed decisions that optimize production efficiency while minimizing waste.

Beginners can start with IoT-enabled sensors to monitor machine performance, energy usage and material waste. From there, use basic dashboards to visualize trends and identify inefficiencies. The next step is to implement predictive maintenance using these insights to reduce unexpected breakdowns and extend machine lifespan.

Advanced users may have already deployed digital twins to create a virtual model of production systems, allowing their engineers to test optimizations before making real-world changes. AI-powered anomaly detection can automatically adjust machine parameters and reduce energy waste while integrating machine learning (ML) algorithms to analyze historical data to improve production scheduling and minimize resource-intensive downtime.

Energy efficiency and carbon footprint reduction

Manufacturing facilities are obviously energy-intensive, but energy management systems can significantly lower power consumption and carbon emissions without disrupting production.

For beginners, smart meters and IoT sensors can track energy consumption at different production stages. Once you have this data, implement automated power-down schedules for non-essential equipment during off-peak hours.

More advanced users can integrate AI-driven load balancing to redistribute energy usage across equipment dynamically. They may decide to explore microgrid solutions that combine renewable energy sources (solar, wind) with energy storage for more sustainable operations. Carbon footprint tracking software will make it easier comply with environmental, social and governance (ESG) standards and improve sustainability reporting.

Optimization for sustainable sourcing and logistics

A sustainable supply chain reduces emissions, optimizes material use and ensures responsible sourcing throughout a manufacturer’s network. Digital tools help companies improve inventory management, optimize transport routes and reduce overproduction.

If you haven’t already, implement cloud-based inventory management systems to track raw materials, reducing excess stock and waste. PLM and ERP software are the gold standard for this, but smaller manufacturers may not need all of the functionality these platforms provide and might decide to piece together the functionality they want using smaller software platforms that require less investment and cause less disruption during start-up. The goal is to gather enough data to use demand forecasting to avoid overproduction and prevent obsolete inventory or costly overstock. Next, implement a supply chain platform to ensure ethical sourcing and reduce supplier-related inefficiencies.

Advanced users are likely at least considering AI-driven dynamic routing systems for delivery fleets, optimizing transportation routes to reducing fuel consumption. RFID and GPS tracking will monitor product movement and optimize storage conditions, reducing spoilage. Next, establish closed-loop supply chains, where returned or defective materials are reintegrated into production rather than wasted.

Waste reduction and circularity

Everyone knows minimizing waste is critical for sustainable manufacturing. Advanced digital tools help manufacturers keep a handle on waste by tracking, sorting and helping repurpose materials efficiently.

Start with robust defect detection to reduce waste caused by faulty production runs. Introducing 3D printing (additive manufacturing) to minimize material waste and create precise, on-demand parts could make sense for a growing number of manufacturers. A basic data-fuelled recycling programs for metal, plastic and other byproducts can keep waste under control.

For advanced users, AI-powered sorting systems to automatically separate and classify waste materials for recycling can improve results of any recycling program. Digital product lifecycle tracking will accommodate customer product returns for disassembly and reuse, potentially taking the edge of raw material costs as digital and smart advanced remanufacturing strategies will help refurbish returned components and reintroduce them into production lines.

Smart manufacturing for sustainable production

Industry 4.0 technologies like automation, robotics, cloud computing and AR (augmented reality) can significantly reduce resource waste and improve efficiency in manufacturing environments.

Beginners can start by implementing basic robotics for repetitive tasks to improve precision and reduce material waste. Cloud-based collaboration tools will reduce paperwork and streamline production planning. Adopting AR-based training modules allows employees to learn new skills without exhausting physical materials.

Advanced users might look to deploy AI-powered collaborative robots (cobots) to enhance precision manufacturing and minimize errors, all while collecting valuable data. Edge computing from devices on the line analyzes machine data locally (rather than in the cloud). This reduces energy consumption for data processing and gives the impetus to implement real-time digital simulation models that predict potential disruptions and adjust production accordingly.

Key takeaways for manufacturing engineers

For those just starting, begin by implementing IoT sensors, analytics and basic automation to monitor and improve sustainability.

For experienced engineers: Use advanced AI, blockchain and digital twins to optimize energy, supply chains and circularity.

Whether you are just starting your digital journey or are an advanced user of the latest digital technologies, it’s important to understand efficiency is just one piece of the payback delivered by digital transformation—it’s about future-proofing operations which includes reducing environmental impact.

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Survey results highlighted escalating trade conflicts, rising global tensions, and persistent supply chain disruptions are placing unprecedented pressure on manufacturing and supply chain leaders.

Despite these challenges, Fictiv says the report also shows momentum in onshoring, AI adoption, and increasing reliance on digital manufacturing platforms.

“Concerns about tariffs and trade wars are clearly top of mind for manufacturing and supply chain leaders,” says Dave Evans, co-founder and CEO of Fictiv. “We’re seeing a level of global uncertainty and supply chain disruption we haven’t seen since 2020. However, the report also shows that companies are embracing new technologies and strategies to build more resilient and agile supply chains.”

Key Findings

• Global Uncertainty on the Rise: 96% are concerned about the impact of current trade policies, and 93% believe trade wars will escalate in 2025.
• Supply Chain Disruptions Accelerating: 77% report a lack of resources limits their ability to manage the supply chain effectively, and 68% prioritize onshoring as a key strategy.
• Scaling Production More Difficult: 91% face barriers to product innovation, and 86% report sourcing parts takes time away from new product introduction. However, 90% see digital manufacturing platforms as essential.
• Sustainability Takes Hold: 95% report that weather and climate events impact their supply chain strategy, and 91% have sustainability initiatives and governance in place.
• AI Advances: 87% report advanced levels of AI maturity, and 94% use AI for manufacturing and supply chain operations.

Fictiv says its report underscores the need for manufacturers to embrace innovation and adaptability by building more resilient supply chains, leveraging digital manufacturing, embracing AI to transform operations from inventory management to product design, and prioritizing sustainability.

This is the tenth year Fictiv has commissioned the report.

Download the full 2025 State of Manufacturing & Supply Chain Report here.

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Initial investment costs

Digital prototyping requires advanced software, hardware, and integration with existing systems, which can be expensive and require a team of engineers with strong expertise in a number of disciplines. Companies must invest in high-performance computing (HPC) resources, VR/AR headsets, simulation software and cloud storage. Small and mid-sized manufacturers will need a focused plan to deal with the cost of licensing, training, and infrastructure upgrades necessary to support digital prototyping workflows.

Software and hardware compatibility

Integrating digital prototyping tools with existing CAD, PLM, and ERP systems is complex. Many companies rely on legacy software that lacks seamless compatibility with modern digital platforms. Additionally, hardware limitations, such as insufficient GPU power for real-time rendering or VR simulation, can hinder performance.

Ensuring interoperability across different systems requires extensive customization, middleware solutions, and adopting standardized file formats. Converting models between different software such as CAD to a simulation suite, may cause loss of parametric data, constraints, or surface definitions. And older versions of software may not support files created in newer versions, leading to workflow bottlenecks.

Learning curve and skill gaps

Digital prototyping tools involve complex 3D modeling, real-time simulation, and data analytics, which require specialized expertise. Many manufacturing engineers are trained in traditional CAD and FEA simulations but may lack experience with VR, AI-driven simulations, or generative design. Companies must invest in training programs and hire or upskill personnel, which can slow adoption.

Data management and cybersecurity

Digital prototypes generate vast amounts of data in the form of design files, simulation data, and testing results which require efficient storage and version control. Managing this data within PLM and cloud systems introduces risks related to cybersecurity, intellectual property theft, and compliance with industry regulations (such as ITAR for aerospace manufacturing). Companies must implement strong encryption, access control, and secure cloud storage solutions to protect sensitive information.

Computational limitations for simulations

Real-time physics simulations, fluid dynamics (CFD), and stress testing (FEA) require high computational power. Companies using VR-based digital prototyping may experience latency issues, especially with large, complex assemblies. Implementing Level of Detail (LOD) algorithms, cloud-based processing, and GPU acceleration can help mitigate performance bottlenecks.

Validation and regulatory compliance

Some industries, such as aerospace, automotive, and medical device manufacturing, require extensive physical testing for regulatory approvals. Digital prototypes, while highly accurate, may not always replace real-world durability tests, crash simulations, or clinical trials. Companies must ensure that their digital twin models are validated against physical results to comply with industry regulations.

Yes, there are always challenges when investing in next generation technology. However, companies that strategically invest in digital prototyping, train their workforce, and optimize data security and processing power can unlock substantial benefits. As cloud computing, AI, and VR technology continue to evolve, overcoming these obstacles will become more manageable, leading to faster product development, cost savings, and improved manufacturing efficiency.

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By integrating VR with CAD (Computer-Aided Design) software, PLM (Product Lifecycle Management) systems, and real-time physics simulations, engineers can gain unparalleled insights into product behavior before physical prototyping. But there are several technical intricacies of VR in relation to digital prototyping and its applications, integration challenges, and benefits to manufacturing.

Integration of VR with CAD and PLM

Modern VR-based digital prototyping heavily relies on CAD and PLM integration. CAD software exports 3D models in VR-compatible formats (such as FBX, OBJ, GLTF) that allow engineers to examine prototypes within the virtual environment. When paired with PLM systems, engineers can track version histories, collaborate in real-time, and integrate design modifications directly into the production workflow. This connectivity ensures that design iterations remain structured and accessible to all stakeholders.

To enhance VR compatibility, many CAD software solutions incorporate native VR apps or VR plug-ins, which allow direct visualization of engineering-grade 3D models in VR without cumbersome file conversions. These tools support parametric modeling and real-time geometric modifications, ensuring high fidelity in virtual environments.

Real-time physics-based simulations

VR goes beyond static visualization by enabling real-time physics-based simulations that engineers use to assess product performance under various conditions.

Simulations can include:

Structural Analysis: Finite Element Analysis (FEA) simulations are rendered in VR, allowing engineers to visually inspect stress distributions and failure points in a virtual space.

Fluid Dynamics: VR-integrated Computational Fluid Dynamics (CFD) simulations enable engineers to observe airflow patterns, heat dissipation, and liquid flow behaviors from a first-person perspective.

Material Deformation: Soft-body physics can replicate material flexing, bending, and breaking under applied forces, giving engineers an intuitive understanding of how materials respond to different loads.

Haptic feedback and realistic interaction

One limitation of traditional digital prototyping is the inability to physically interact with the model. VR overcomes this challenge by incorporating haptic feedback devices, which simulate tactile sensations and resistance. Such devices allow engineers to “feel” surfaces, textures, and resistances as they manipulate virtual components.

In addition to haptics, real-time rendering techniques such as ray tracing and shadow mapping improve visual realism in VR environments. High-performance GPUs enable photorealistic rendering, ensuring that materials, lighting conditions, and reflections closely mimic real-world properties. By integrating physics engines developed by a number of different companies, VR prototypes can react dynamically to user interactions, providing a near-physical testing experience before production.

Design validation and ergonomics testing

Manufacturing engineers can use VR for comprehensive design validation before committing to expensive tooling and fabrication. Dimensional accuracy checks assess tolerances and fitment by placing components in a simulated assembly line, while ergonomics assessment using VR simulations allow engineers to test human-machine interactions, ensuring that equipment is comfortable and efficient for operators.

Instead of relying on physical mock-ups, engineers can conduct virtual usability studies, allowing stakeholders to evaluate user experience and product functionality in various conditions.

Industry-specific applications of VR prototyping

Automotive: Car manufacturers use VR to perform full-scale vehicle prototyping, enabling designers to test aerodynamics, visibility, and cockpit ergonomics before building physical models.

Aerospace: Engineers visualize and test complex aircraft components, such as turbine blades and fuselage assemblies, in VR environments with real-world physics simulations.

Consumer electronics: Companies test user interfaces and device form factors in VR to refine designs based on virtual consumer feedback.

Medical device manufacturing: VR enables precise simulation of surgical instruments and implants, helping engineers refine designs for biomechanical compatibility.

Technical challenges and solutions in VR prototyping

Despite its advantages, VR prototyping presents several technical challenges. Combining multiple engineering datasets (FEA, CFD, PLM) into a cohesive VR simulation can be challenging, but standardized file formats (USD, STEP, and FBX) streamline data exchange across platforms.

Running detailed CAD models in VR can be costly, as the high computation output requires powerful GPUs and optimized software workflows. Using Level of Detail (LOD) algorithms and real-time model decimation can improve performance without sacrificing accuracy. These algorithms optimize performance by adjusting the complexity of 3D models based on their distance from the viewer or their importance in the scene. Here’s how they work:

Dynamic mesh simplification – LOD algorithms swap high-detail models for lower-poly versions when objects are further away, reducing GPU load without affecting perceived visual quality.

Adaptive rendering – By prioritizing detail only where needed (on user-interacted components), LOD ensures real-time rendering efficiency.

Improved frame rates – LOD prevents frame rate drops by decreasing the polygon count in non-critical areas, ensuring smooth VR interactions at 90+ FPS (critical for reducing motion sickness).

Memory optimization – Less-detailed models free up GPU memory, allowing for larger assemblies and complex simulations without performance bottlenecks.

Hybrid use with culling techniques – Combined with occlusion culling (hiding objects not in view), LOD further enhances computational efficiency.

Future trends

The future of VR-based digital prototyping in manufacturing is set to become even more powerful with advancements in AI-driven automation, cloud-based collaboration, and hybrid AR-VR environments.

AI-driven automation integrates machine learning algorithms that analyze designs in real time to detect structural weaknesses, suggest material optimizations, and even predict potential manufacturing defects before physical prototyping begins. By continuously learning from past designs and simulations, AI can help engineers refine product performance and reduce costly trial-and-error iterations. This capability will significantly shorten development cycles while improving the reliability and manufacturability of new products.

In addition, cloud-based VR collaboration will redefine how global engineering teams interact with digital prototypes. Instead of requiring high-end local hardware, cloud-rendered virtual workspaces will allow engineers to access and manipulate detailed VR models from anywhere in the world. This technology will enable real-time design reviews, remote troubleshooting, and seamless integration with PLM (Product Lifecycle Management) systems, ensuring that teams remain aligned even when working across different locations. Cloud-based VR will also facilitate large-scale manufacturing projects by enabling multiple stakeholders—from designers to production managers—to interact with virtual prototypes without needing specialized workstations.

Furthermore, the rise of AR-VR hybrid environments will bridge the gap between digital and physical prototyping. By overlaying VR-generated 3D models onto real-world objects using Augmented Reality (AR), engineers will be able to test virtual components in real-world settings without requiring a full digital or physical setup. This will be particularly useful for ergonomics testing, assembly validation, and factory layout optimization, where seeing how a virtual component interacts with real machinery or workspace constraints is crucial.

As these technologies continue to evolve, VR-based digital prototyping will become an intelligent, collaborative, and highly integrated system, streamlining manufacturing workflows and enabling faster, smarter product development.

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Thirty-one percent of the 434 survey respondents predicted EV output would increase by more than 10%. A further 44% said production would grow in 2025 by up to 10%. Meanwhile, only 21% of respondents believed EV production would either remain static (8%) or decline (13%) through 2025.

“This year’s survey found that overall, automotive manufacturing professionals are optimistic about EV production growth in 2025, but unsure about reaching 100 percent electric vehicle production timetables due to factors now often beyond the factory environment,” said Joerg Reger, managing director of ABB’s automotive business Line. “There’s strong evidence that EV manufacturing capabilities are now considerably improved, and significant change has taken place in terms of introducing new production technology as well as upskilling workforces. ABB Robotics has made wide-scale changes to our own robotic and automation portfolio to support our customers and drive this transformation forward at pace.”

Despite the optimistic EV forecast from manufacturing experts, there was a decline in confidence about meeting proposed EV deadlines. When asked whether 100% EV production was achievable to meet regional deadlines set between 2030-2040, 31% believed this was an impossible target compared to 27% the previous year and just 18% in 2022. Overall, 65% were skeptical about achieving full EV production within the 2030-2040 timeframe.

Further examination of the downturn in confidence found that the main barriers were now deemed to be ‘outside the factory’ with levels of consumer demand and confidence in charging infrastructure. The survey also indicated that manufacturing experts are predicting strong growth in hybrid powertrains during 2025, with 67% of those surveyed believing that plug-in hybrid electric vehicle (PHEV) production would grow and 20% forecasting that production would increase by over 10%. Hybrid Electric Vehicle (HEV) figures were equally optimistic with 62% of those surveyed expecting output to grow in 2025.

“Hybrid passenger vehicle production remains buoyant with the global manufacturing community expecting to produce more cars in 2025. These results support the survey’s main findings that the overall pace of EV adoption is currently not fast enough to reach some of the upcoming legislative deadlines for a 100% electric future,” said Daniel Harrison, chief analyst for Automotive Manufacturing Solutions.

“Within the manufacturing environment, the production of numerous powertrains across several model lines can create considerable complexity and additional cost, which has been pinpointed in our previous surveys produced in partnership with ABB Robotics.”

Automotive has traditionally been the backbone of the robotics industry. In 2020, however, the Association for Advancing Automation (A3) found that yearly orders of robots for non-automotive sectors surpassed automotive robot orders for the first time in North America. Fast forward to last year, automotive orders declined 15% in 2024 compared to 2023 in North America, according to A3. A3 said it was optimistic automotive orders will bounce back by the end of 2025.

“I think there is room to grow in automotive,” Alex Shikany, executive vice president of A3, recently told The Robot Report. “What we saw over the last two years, with the lower quantities of orders, had a lot more to do with manufacturers pivoting their strategies with regard to not getting the performance they thought they would get out of all their electric ambitions.”

Robotics impact on EVs

When questioned about how well manufacturing companies are embracing robotics, new OEMs, startups, and pop-up manufacturers were the leading adopters with 63% investing “very well” or “quite well.” This was matched by 63% of technology specialists investing “very well” or “quite well” in robotics, leading all other manufacturing groups.

In third place were legacy OEMs with 53% investing “very well” or “quite well” in robotics, the survey found. Referencing the previous survey results, new OEMs, startups, and pop-up manufacturers who were embracing robotics and automation “very well” or “quite well” dropped from 66% in 2023 to 63% in 2024, indicating a slight decline in perceived adoption.

According to the survey, autonomous mobile robots (AMRs) exhibited the highest expected increase, with 25% predicting a strong increase and 39% expecting a slight increase. Collaborative robots (cobots) were in second place with 22% predicting a strong increase and 35% expecting a slight increase, followed by articulated robots in third place with 19% predicting a strong increase and 39% expecting a slight increase.

Click the image to enlarge it.

Yes, humanoids also made the list. According to the survey, humanoids were cited to strongly increase by 27% of Asian respondents, compared to only 5% in Europe and just 2% in North America. Several humanoid manufacturers are testing with automakers, including Apptronik, Boston Dynamics, Figure, and UBTech, to name a few. Of course, Tesla is also developing humanoids.

UBTech this week released a video showcasing a swarm of humanoids performing a variety of tasks and working together inside a Zeekr smart factory in China. You can watch the video below.

According to the survey, 54% of respondents see anticipated high initial costs as the greatest obstacle to smart factory implementation.

This story was originally published on The Robot Report.

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Many designers are familiar with the challenges associated with creating electrically conductive tracks across shapes with curved and complex surfaces. Let’s look at some of these issues and the drawbacks of conventional approaches in depth.

A constant compromise?

Product makers often complain that they cannot place components, such as switches or sensors, where they’d ideally like them. This is often due to constraints that limit connectivity options: a physical wire won’t fit or is too heavy, for example. Additionally, surfaces may need to be functionalized with antennas or frequency-selective components. These issues are particularly acute on complex or curved surfaces.

Some engineers will have explored traditional printed electronics, such as laser direct structuring (LDS). LDS uses nanoparticle-loaded polymers to create conductive links on curved and complex surfaces. However, the very high cost of these polymers means this technique is generally only feasible for small-scale components — think smartphone parts. Even where LDS is economically viable, you must make the entire part out of the specialized polymer, which may not be ideal for your use case.

For those looking to create conductive tracks across larger curved surfaces, the solution has generally been to shape a film containing the conductive tracks around the surface. However, anyone who’s tried this will know it’s a challenging process, with particular risks when joining conductors. It’s generally also limited to simpler shapes, laborious, and requires a highly skilled worker.

Then, you add in broader challenges. The high cost of the tooling required for traditional manufacturing techniques means flexibility is a luxury most cannot afford. Once you commit to a design, making changes is expensive, and the ability to produce multiple product variants is limited. Moreover, convoluted, multi-step logistics and production processes are typically required to build and assemble discrete parts into the final product. In addition to higher costs, all of this handling increases the risk of damage to components during production.

Put together, these issues will likely impact your products in various ways. Because of manufacturing limitations, you may struggle to create the form or function you want. You’ll lack the freedom to alter designs or create lots of product variants once you’ve tooled up. You could be experiencing high rates of damage and failure during manufacturing or simply be paying high costs due to complex logistics and handling. Where technology needs to be adaptable to competitive and perhaps hostile environments, these traditional manufacturing methods can be dangerously slow to respond.

A new way to create conductive tracks on complex and curved surfaces

Research engineers at Q5D have been developing a new set of manufacturing techniques to address these and other challenges. The techniques use a high-accuracy gantry robot to form conductive tracks as narrow as 100 microns onto large, complex, or conformal surfaces that typically form part of larger electrical devices. Crucially, the approaches can be applied to virtually any type and shape of substrate. This includes large objects, such as antennas or frequency-selective shielding in aircraft nosecones.

The techniques use materials such as copper and silver to give surfaces electrical function. This means you can connect devices via tracks or add features such as antennas or capacitive touch to your surfaces. Depending on which technique you use, metal tracks that support high currents can achieve base metal conductivity of up to 100%.

In parallel, the team behind the techniques aims to simplify manufacturing, compared to conventional methods of creating conductive tracks on curved and complex surfaces. Where other approaches require some level of off-machine processing, including difficult manual tasks, the newly developed techniques enable on-machine metallization, meaning there are fewer overall steps and less handling.

Unlocking new design opportunities

As product designers and engineers ourselves, we’re incredibly excited about the potential of these new techniques. We foresee them enabling the engineering community to add electrical function to whole new form factors and sizes that would previously have been too complex or costly to manufacture. We also see them creating opportunities to place components or functionality in new locations or produce parts that are currently unviable.

Elsewhere, the techniques bring the freedom to use the right material for each part of the product and lay the necessary conductive track onto it. This can eliminate the need to compromise on the overall material used, or the requirement to make a whole component out of expensive polymer, which may not be fit for purpose.

We’re also excited about the manufacturing flexibility these techniques promise. Because the tooling requirement has been removed, it’s as easy to create 1,000 of the same part as it is to create 1,000 different parts or variants.

For budget holders, these new approaches will bring opportunities to reduce production and assembly costs due to less handling, logistics, and human input, as well as lower risk of damage to components during production and assembly. Automating what would traditionally have been largely manual processes is also a proven way to enhance overall product quality, meaning product failure rates once in the field should also reduce.

Pushing the boundaries of engineering

To summarize, these new techniques extend the boundaries of what design engineers can create:

• Lay down a conductive track on virtually any shape and type of surface, at scale.
• Place connectivity or electrical function in places that wouldn’t have been possible before.
• Reduce or eliminate compromises you’ve traditionally had to make in your designs.
• Or simply reduce the manufacturing complexity and cost of your product.

Let’s wrap up with some use cases where these new approaches could be effective. A great example would be mobile phone antennas, where you could simplify production compared to the conventional printed electronics techniques typically used today.

In the automotive industry, these techniques are being adopted to reduce the cost and complexity of manufacturing and installing components such as vehicle interiors. They can also unlock new functionality in dashboards, such as capacitive touch surfaces, or provide greater flexibility around the placement of switches.

Because the techniques can be applied at scale and are suitable for use on composite materials used in aerospace, they offer aircraft makers new opportunities in areas such as thermal management of wing heating and the aforementioned nosecone example.

Other large-scale engineering could also benefit from using techniques on curved surfaces inside radomes for frequency-selective shielding.

The R&D team at Q5D is exploring the broader potential of these techniques in manufacturing. To learn more or see a demonstration, contact the team at q5d.com/contact.

Simon Baggott is the chief marketing officer at Q5D Technologies. He has over 20 years of experience connecting people with products across B2B and B2C technology businesses. Simon has worked in both large multinational corporations such as BOC, GE, and JDR Cable Systems and in small to medium enterprises. He holds a BEng degree in materials engineering from the University of Swansea, Wales.

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As systems have evolved from carburetors and simple exhaust systems to precision fuel injectors,  emission systems and traction and braking control systems, similar advancements have been made in electrification with new architectures, components for electric motors, battery packs and advanced power electronics. Combined, these advancements are pushing engineers to reimagine how vehicles are designed and driven for maximum efficiency, reliability and safety.

Experts from two industry-leading electronics companies — Matt McWhinney and Kirk Ulery, business development managers at Molex and Shawn Luke, technical marketing manager at DigiKey — shed light on the current state of the electrification movement and key considerations for the future of the automotive industry.

Vehicle model landscape

While the highly watched demand for electric vehicles (EVs) and hybrid vehicles continues to increase, sales of new EVs have slowed over the last several months due to many factors, including market and public policy. Industry experts cite cost and the limited charging infrastructure in the U.S. as two major reasons.

“We’ve had fits and starts on electrification in North America,” Ulery said. “If you’re going more than 100 miles at a time, you know charging infrastructure needs to be addressed.”

Hybrid vehicles, on the other hand, are outpacing EV sales. According to Edmunds data, hybrid purchases in the U.S. saw their biggest surge in 2023, increasing from more than 750,000 sales in 2022 to tipping over one million sales in 2023.

Another emerging category is the mild hybrid, which uses a battery-powered electric motor to supplement gas or diesel usage. Most mild hybrids run on a 48 V electrical system, which is a higher voltage than the electrical systems of a traditional combustion engine vehicle. The 48 V system powers components that are not reliant on the engine, enabling better operational efficiency.

Even with the fast pace of innovation in automotive design, gas-powered vehicles still rule the roadways. According to research by Edmunds, 82% of new vehicles sold today in the U.S. rely on gas. However, the electrification movement is well underway among traditional vehicles to the most advanced high-tech electric models.

Electrifying under the hood

“One constant we’re seeing is a lot more electrification — mechanical systems are becoming electrified in all vehicles for many reasons — especially to drive efficiency,” Ulery noted.

One example is stop-start technology, which turns off the engine when a vehicle stops and automatically restarts when the driver releases the brake or pushes the gas pedal. While this feature can put extra demand on some components, it aims to improve fuel efficiency and reduce greenhouse gas emissions.

Other examples of electrification under the hood are in radiator fans, power steering, HVAC systems and cooling pumps. All these systems used to be powered by belts off an internal combustion engine (ICE). Electric water pumps are replacing mechanical radiator pumps for more efficient performance, and the precise control with electrical cooling can extend the lifespan of these parts. If there is extended battery management, they also circulate coolant throughout a vehicle to regulate the temperature of the battery pack, electric motors and power electronics.

Switching to electric-powered modules such as power steering pumps makes the system no longer reliant on the engine, reducing parasitic loads and allowing for more available horsepower. Therefore, automakers can install smaller engines in some vehicles and retain the same driving performance while gaining efficiency benefits and outputting lower emissions.

“Electrification has opened the door to innovative new vehicle designs,” said Luke. “Without the need to accommodate the ‘belt driven architecture’ with a traditional internal combustion engine, auto manufacturers have more flexibility in where to distribute batteries and charging ports, the ability to increase the amount of space for passengers or cargo, and more.”

Overall, the electrification movement is replacing traditional mechanical with precision electrically controlled systems that can be more efficient. Combined with advancements in software control, modern vehicles are cleaner, more energy efficient, and offer performance and sustainability for both passenger and commercial drivers.

Vehicle battery advancement

Over the last decade, vehicle manufacturers have switched from 12 V to higher voltages, such as 24 V (especially for commercial vehicles) and now to 48 V batteries to increase power capability, reduce vehicle weight, improve acceleration and realize fuel savings.

Legislation in the U.S. and Europe has been laying the groundwork for emissions reduction in newly built vehicles. A combination of regulatory and market forces is behind the increasing shift to mild hybrid architectures, which include integrated starter generators; 48 V is not only growing in mild hybrids but also seems likely to emerge in more ICE platforms.

The shift to 48 V architecture involves more than just increasing the system voltage. It also requires a change in the electrical foundation. Feature-rich, higher-performance vehicles rely on lighter and smaller components that deliver the same electrical efficiency as a higher-density model.

“The common thing is that both 12 V and 48 V systems are moving traditional mechanical functions off a serpentine belt to a series of electric motors,” said Ulery. He shared an example of a heavy-duty pickup truck using mechanical energy for its power steering. In many vehicles, this function is becoming electrified. “The amount of energy needed for the power steering takes away from the engine’s horsepower, so by moving it to a separate electrical system, drivers can maintain more power through the drivetrain.”

The automotive industry’s move to higher voltage systems is a gradual one, given the significant impact on the design and manufacturing process. Each manufacturer’s transition is on a different timeline based on their products, technical maturity and the requirements of the customers they serve. Plus, all are held to standards and design practices related to the technologies they will be using, including:

• ISO 21780 covers requirements and tests for the electric and electronic components in road vehicles equipped with an electrical system operating at a nominal voltage of 48 V.
• The VDA Recommendation 320 is published and maintained by the ZVEI-German Electrical and Electronic Manufacturers’ Association. It covers a wide range of specifications and test requirements for electric and electronic components in motor vehicles to develop the 48 V power supply.

Following the standard to achieve smart battery management is integral to the success of 48 V architecture. With the right design process, automakers can avoid inefficient power storage, increased costs and potential safety risks to drivers.

Interconnection fundamentals to prioritize safety

With vehicles requiring more power than ever to support increasingly sophisticated electrical features, a reliable connector design for 48 V systems relies on several fundamental factors to meet vehicle performance and safety standards.

“Having electronics and the infrastructure — the interconnects to support your vehicle — is essential for safety,” said McWhinney.

Since 48 V systems operate at a higher voltage (than 12 V), connectors and electrical systems must be built with robust materials and proper insulation for safe, reliable performance. This becomes even more important if the voltage is higher than 48 V.

Connector failures can cause vehicle system malfunctions or safety hazards. To prevent disconnections, connectors should include locking mechanisms and strain relief, as well as regular inspections and maintenance checks.

“Safety and monitoring control of the electrical system is more important now than ever,” said McWhinney.

Maintaining signal quality is crucial for higher voltage applications. Poor signal integrity can precipitate malfunctions, so connectors must minimize signal loss and interference with shielded cables, as well as proper grounding and strategic placement. Addressing these considerations requires innovation and expertise, which is where advanced connector solutions come into play.

“It feels so much like table stakes, but it’s underrated how important the interconnect is in automotive design, especially for safety,” added Luke.

Keeping up with change and certification of parts

Meeting safety requirements is a top priority, but McWhinney notes that an additional challenge is the constant change in vehicle electrical system requirements, which pushes manufacturers to keep up and constantly revise connectors and other components.

Manufacturers can always refer to the US Council for Automotive Research (USCAR) to track performance requirements and carefully review and certify approved components for safe use in the automotive industry.

Components that adhere to USCAR/LV214 or similar qualifications are typically high-quality, rugged and reliable parts that can take a beating on the road without sacrificing performance. For example, Molex’s MX150 connector series offers components engineered for vehicles that face harsh environments and are durable against extreme temperatures, vibration and moisture.

“With more innovation opportunities in vehicle design, more vehicle manufacturers are embracing electrification practices,” said Luke. “Because of the hyper-fast innovation cycle, there are few standard platforms in the space. However, the increased variety offers consumers more options, and we expect the cost of vehicles will likely decrease as technology advances and production ramps up.”

Considering commercial vehicles

While much has been said about passenger cars, everything discussed in this article has been going on much longer in the commercial vehicle (CV) space. Commercial vehicles quickly transitioned from 12 V to 24 V systems to power diesel and some electrical systems, which allowed them to have smaller starters in the past. There is also a long history of electric HVAC in CVs, especially in buses, construction and agriculture vehicles and heavy-duty trucking, among others.

Commercial vehicles are typically designed to help their owner or operator make money and, therefore, must work reliably. The pressure for a CV to perform reliably is typically higher than that of passenger cars, so extra sealing and robustness are needed.

Whether designing for passenger or commercial vehicles, engineers today must consider numerous complex, power-hungry systems and features that not only meet consumer and commercial demand but are also highly efficient, durable and safe.

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Nothing could be further from the truth.

Granted, any new trend will always have an element of bandwagon business from marginal players and hype-riders looking to benefit from the latest trends.

But in terms of how digital transformation and AI are being researched and implemented in the manufacturing industry, there is plenty of steak to go along with all that sizzle.

One of the best ways to distinguish between an over-hyped trend and something with substance is to watch who is watching it. A great place to see that in action was at the recent ARC Industry Leadership Forum, which took place in Orlando, Fla. February 10-13.

This year’s event was almost entirely focused on AI, digital transformation and Industry 5.0 in manufacturing. It attracted more than 600 attendees representing some of the biggest companies in the manufacturing sector.

Indeed, the top 30 of these attending companies with publicly available financial numbers had a combined 2023 market cap of $4.22 trillion. If this market cap were a country, it would rank as the 4th largest economy in the world, just behind Germany ($4.5 trillion GDP) and ahead of Japan ($4.20 trillion GDP). Most of these companies were users undergoing significant digital transformation initiatives.

The fact that these industrial heavyweights are already fully invested in implementing AI and digital strategies shows the scale of the opportunity, and the huge strategic risk of ignoring it—we’re talking Blockbuster Video-level strategic risk.

But the question remains: where do you begin, especially if you don’t have the capital and assets of these massive multinational businesses?

Everywhere, all at once

In the current state of things, engineering leaders can be easily overwhelmed with all the trends and challenges thrown at them. Mathias Oppelt, vice-president of customer-driven innovation at Siemens Digital Industries (Siemens is certainly a technology vendor, but also manufactures its products using the latest smart manufacturing principles), hears about this from his customers daily and summed it up nicely during his session at the ARC Forum:

“You need to act more sustainably; you need to have higher transparency across your value chain. Have you thought about your workforce transformation yet? There’s a lot of people retiring in the next couple of years and there’s not many people coming back into the into the workforce. You still must deal with cost efficiency and all the productivity measures, while also driving energy efficiency. And don’t forget about your competition—they will still be there. And then there’s all that new technology coming up, artificial intelligence, large language models, ChatGPT—and on it goes, all of that all at once.”

Sound familiar?

Even with all these challenges, everything must now be done at speed. “Speed and adaptability will be the key drivers to continuing success. You need to adapt to all these challenges, which are continuously coming at you faster. If you’re standing still, you’re almost moving backwards.” Oppelt said.

The answer is simple, offered Oppelt with a wry smile: just digitally transform. The crowd, sensing his sarcasm, responded with nervous laughter. It was funny, but everyone understood it was also scary, because no one really knows where to start.

Bite the bullet, but take small bites

“The continuous improvement engineers out there know how risky it can be to bite off more than the organization can chew or to try to drive more change than it can manage,” says Doug Warren, senior vice president of the Monitoring and Control business for Aveva, a major industrial software developer based in Cambridge, U.K.

“It helps to take bite-sized pieces, and maybe even use the first bite to drive some incremental benefit or revenue to fund the next bite and then the next bite. You can sort of see this this self funding approach emerge, assuming the business objectives and the metrics tied to those business objectives show results.”

Warren is puzzled by how slow a number of industrial segments have been to fully embrace digitalization and digital transformation, saying that “…it seems like everyone has at least dipped a toe or a foot into the water,” but the number of organizations that are doing it at scale across the whole enterprise is lower than most people would guess.

“The level of technological advancement doesn’t come as a big surprise, and where we go from here won’t be a big surprise. The trick will be how fast you get past the proof-of-concept and into full scale deployment,” he says.

From Warren’s perspective, if you’re not taking advantage of the digitalization process to fundamentally change the way you’re doing work, then you’re probably not getting as much value.

“To just digitize isn’t enough. How do we change those work processes? How do we inject more efficiency into work processes to take advantage of the technological advancements you are already investing in? That’s the special sauce,” he says, conceding that it’s difficult because people typically prefer routine and structure. “That’s probably got a lot to do with the lack of real speed of adoption, because you still have to overcome the way you’ve always done it.”

Warren says a good way to look at it is like a more nuanced version of the standard continuous improvement initiatives companies have been undertaking for decades.

“Continuous improvement is incremental changes over time, where digital transformation provides at least an impetus for more of a step change in the way we perform work, whatever that work might be.”

What’s old is new again

One of the main points of hesitation towards full scale implementation of digital transformation or AI initiatives is the perceived newness of it and the uncertainty or risk associated with the perception of so-called “bleeding edge” technology.

The thing is, none of this is all that new. The concept of the neural network was developed in the 1940s and Alan Turing introduced his influential Turing Test in 1950. The first AI programs were developed in the early 1960s. If you are a chess enthusiast, you’ve certainly played against AI opponents for the last 20 years. Most popular video games have had story lines fuelled by AI-powered non-player-characters (NPCs) for almost as long.

What has changed over the last few decades is the amount of computing power available, the democratized access to that compute power through the cloud, and the speed provided by the latest advances in chips.

This growth of available computational power and technology can now be applied to all the improvements organizations have been trying to achieve with continuous improvement. And they are proving to be most effective when combined with the extensive knowledge found within companies.

“Industry definitely provides complexities because it’s not just AI and machine learning (ML). There’s also domain knowledge, so it’s really a hybrid approach,” says Claudia Chandra, chief product officer for Honeywell Connected Industrials based in San Francisco.

Chandra earned a Ph.D. in artificial intelligence and software engineering from UC Berkeley 25 years ago and has spent her career working with data, AI, edge platforms and analytics.

“I’m not for just AI/ML on its own. It’s really the domain knowledge that needs to be incorporated along with (AI’s) first principles. The accuracy would not be there without that combination, because data alone won’t get you there,” Chandra said.

“That tribal knowledge needs to be codified, because that gets you there faster and might complement what’s in the data. So, digitization is the precursor to AI/ML—you need to collect the data first in order to get to AI/ML,” she says, reiterating that it must be a step-by-step process to reduce risk.

Chandra says companies that have taken these incremental steps towards digitalization and embrace the cloud or even more advanced tech such as AI/ML will find that their digital transformation is no longer a behemoth with all the pain and risk that go with it. Plus, any vendor with a good understanding of the technology will provide at least a starting point—including pre trained models—so companies don’t have to start from scratch. “But ultimately, as you train it more, as you use it more, it will get better with the data that’s specific to your company,” she says.

Certainly, the success of any AI-enabled digital transformation initiative is all about the underlying data and training the AI appropriately to get the required accuracy. But it takes several steps to set the conditions for value generation: Commit to a project; start small with the right use case; and be persistent and diligent with the data. Once you get a small victory, put the value and the experience towards the next project. With such an approach, you will soon learn why AI and Industry 5.0 are here to stay—and so will your competition.

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In the automotive industry, products and production processes are becoming increasingly complex. This complexity rises within each engineering domain and across disciplines. With the advent of hybrid and electric vehicles, the complexity of engineering and systems management continues to grow exponentially. So how can component and vehicle manufacturers continue to develop their products at the same pace and at competitive cost? Some may be reluctant to abandon tried-and-true processes. Still, the most progressive manufacturers have realized it is time to re-evaluate their approach and develop new processes best suited to today’s demands. The most successful businesses are adopting model-based systems engineering (MBSE), enabling them to remain competitive, agile and cost-effective while tackling the challenges of quality, increased regulation and sustainability.

Download today to learn how MBSE can improve vehicle development.

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

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

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

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

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

How to measure tariff risk in a manufacturing company

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

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

Cost of goods sold (COGS)

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

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

Supply chain lead time

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

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

Inventory turnover

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

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

Gross margin

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

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

Product cost variance

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

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

Supplier performance and reliability

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

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

Cost of imported goods

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

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

Customer pricing and profitability

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

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

Market share

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

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

Cash flow

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

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

Production efficiency and overall equipment efficiency

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

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

Risk exposure by country or region

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

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

Regulatory compliance and tariff changes

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

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

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

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