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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Agentic AI: autonomous decision-making

Agentic AI refers to AI systems that act autonomously based on goals, real-time data, and feedback loops. In manufacturing, this can be seen in self-optimizing production lines, automated quality control, and predictive maintenance. Unlike traditional automation, Agentic AI can adapt to changing conditions and make independent operational decisions without human intervention, improving efficiency and reducing downtime.

Causal AI: understanding cause and effect

Causal AI goes beyond pattern recognition by identifying cause-and-effect relationships in complex manufacturing systems. This AI type is valuable in root cause analysis, process optimization, and failure prediction. Unlike conventional machine learning, which correlates data, Causal AI determines why failures or inefficiencies occur, enabling engineers to implement targeted solutions rather than just responding to symptoms.

LLMs: processing language and documentation

Large Language Models (LLMs), such as GPT-based AI, specialize in natural language processing (NLP). While not directly involved in factory operations, LLMs help automate documentation, generate maintenance reports, assist with troubleshooting, and provide AI-driven technical support. They can summarize engineering papers, create standard operating procedures (SOPs), and improve communication across teams.

Choosing the right AI

Each AI type complements manufacturing processes in unique ways, leading to smarter, more efficient operations.

Manufacturing Example: Causal AI vs. Agentic AI

Use Case: Predicting and preventing machine failures.

How It Works: A factory uses causal AI to analyze historical sensor data from machines.

Instead of just identifying correlations (When temperature rises, breakdowns occur), it determines the cause (Excess vibration due to misalignment leads to overheating, which causes failure).

This allows engineers to intervene proactively by fixing misalignment before a breakdown happens.

Use Case: Autonomous production line optimization.

How It Works: An agentic AI system monitors production efficiency in real-time and autonomously adjusts machine settings for optimal output.

If a machine slows down, the AI dynamically reallocates work to other machines without human intervention.

It learns from past production data and adapts continuously to maximize efficiency while minimizing waste and energy use.

Key Differences:

Causal AI helps engineers understand why failures happen and improve decision-making.

Agentic AI takes direct action, adjusting processes autonomously to optimize performance.

In manufacturing, causal AI and agentic AI serve different but complementary roles.

Hybrid approach: smart factory with both AI types

A factory can integrate both causal and agentic AI. Causal AI analyzes sensor data and finds that high humidity causes metal corrosion, leading to increased friction and machine failure. Agentic AI uses this insight to autonomously adjust humidity controls, slow down machines at risk, and reassign production tasks to avoid downtime. Over time, the system learns and adapts, reducing failures, increasing efficiency, and lowering maintenance costs.

Together, they create self-optimizing factories with minimal human intervention.

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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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The institute, which is headquartered in Tucheng, Taiwan, said the LLM—named FoxBrain—will be open sourced and shared publicly, but did not disclose a timeline.

It was originally designed for the company’s internal systems, covering functions such as data analysis, decision support, document collaboration, mathematics, reasoning, problem solving, and code generation.

The company says FoxBrain not only demonstrates powerful comprehension and reasoning capabilities but is also optimized for Taiwanese users’ language style, showing excellent performance in mathematical and logical reasoning tests.

“In recent months, the deepening of reasoning capabilities and the efficient use of GPUs have gradually become the mainstream development in the field of AI. Our FoxBrain model adopted a very efficient training strategy, focusing on optimizing the training process rather than blindly accumulating computing power,” said Dr. Yung-Hui Li, Director of the Artificial Intelligence Research Center at Hon Hai Research Institute. “Through carefully designed training methods and resource optimization, we have successfully built a local AI model with powerful reasoning capabilities.”

The FoxBrain training process was powered by 120 NVIDIA H100 GPUs, scaled with NVIDIA Quantum-2 InfiniBand networking, and finished in about four weeks. Compared with inference models recently launched in the market, the more efficient and lower-cost model training method sets a new milestone for the development of Taiwan’s AI technology.

FoxBrain is based on Meta Llama 3.1 architecture with 70B parameters. In most categories among TMMLU+ test dataset, it outperforms Llama-3-Taiwan-70B of the same scale, particularly exceling in mathematics and logical reasoning. Some technical specifications and training strategies for FoxBrain include:

• Established data augmentation methods and quality assessment for 24 topic categories through proprietary technology, generating 98B tokens of high-quality pre-training data for Traditional Chinese
• Context window length: 128 K tokens
• Utilized 120 NVIDIA H100 GPUs for training, with total computational cost of 2,688 GPU days
• Employed multi-node parallel training architecture to ensure high performance and stability
• Used a unique Adaptive Reasoning Reflection technique to train the model in autonomous reasoning

The company says FoxBrain showed comprehensive improvements in mathematics compared to the base Meta Llama 3.1 model. It achieved significant progress in mathematical tests compared to Taiwan Llama, currently the best Traditional Chinese large model, and surpassed Meta’s current models of the same class in mathematical reasoning ability. While there is still a slight gap with DeepSeek’s distillation model, Hon Hai says its performance is already very close to world-leading standards.

FoxBrain’s development—from data collection, cleaning and augmentation to Continual Pre-Training, Supervised Finetuning, RLAIF, and Adaptive Reasoning Reflection—was accomplished step by step through independent research, ultimately achieving benefits approaching world-class AI models despite limited computational resources.

Although FoxBrain was originally designed for internal group applications, in the future, Foxconn will continue to collaborate with technology partners to expand FoxBrain’s applications, share its open-source information, and promote AI in manufacturing, supply chain management, and intelligent decision-making.

NVIDIA provided support during training through the Taipei-1 Supercomputer and technical consultation, enabling Hon Hai Research Institute to successfully complete the model pre-training with NVIDIA NeMo. FoxBrain will also become an important engine to drive the upgrade of Foxconn’s three major platforms: Smart Manufacturing, Smart EV and Smart City.

The results of FoxBrain are scheduled to be shared publicly for the first time during NVIDIA GTC 2025 Session Talk on March 20.

Hon Hai Research Institute, the research and development arm of Foxconn, has five research centers. Each center has an average of 40 high technology R&D professionals focused on the research and development of new technologies, the strengthening of Foxconn’s technology and product innovation pipeline.

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Traditional prototyping methods require physical models, making iteration cycles slow and expensive. Digital prototyping, using CAD (Computer-Aided Design) and CAE (Computer-Aided Engineering) tools, allows teams to create and modify virtual prototypes in real time. Engineers can share 3D models with stakeholders for instant feedback and collaborative design adjustments. CAD tools allow engineers to modify component dimensions and geometries parametrically. After a change, all dependent features update automatically. Cloud platforms enable multiple users to access and edit designs simultaneously from different locations, fostering seamless collaboration.

Enhanced communication between cross-disciplinary teams

A major challenge in manufacturing is the effective communication of design intent between different departments. Digital prototyping bridges this gap by providing a shared visual representation of the product, minimizing misinterpretations. Model-Based Definition (MBD) replaces traditional 2D drawings with 3D annotated models that include dimensions, tolerances, and material specifications, ensuring a single source of truth. Recently, technology such as augmented reality (AR) and virtual reality (VR) allow engineers to visualize and interact with digital prototypes in a simulated environment, improving understanding and communication.

Improved supply chain and vendor collaboration

Manufacturers often work with suppliers and vendors who contribute to product design and assembly. Digital prototyping facilitates smoother collaboration by enabling external partners to access and review design files in a controlled environment. Integrating software like product lifecycle management (PLM) into the prototype phase centralizes design data, version history, and approvals, ensuring all stakeholders work with the latest files. Neutral file formats such as STEP and IGES ensure compatibility between different CAD software used by suppliers and manufacturers.

Digital twins in digital prototyping

Digital twins provide a real-time, data-driven virtual model that continuously updates with real-world inputs. Unlike static CAD models, digital twins integrate IoT sensor data, AI-driven simulations, and real-world performance metrics, ensuring all teams—design, manufacturing, and quality control—have the same information. Not only does this eliminate version conflicts, but it also allows the manufacturing team to plug the product into a digital twin of the production process to validate its manufacturability.

Going a step further by using AI-driven analytics provides insights into material stresses, energy efficiency, and operational reliability, reducing costly late-stage revisions. Whether optimizing thermal performance in an EV battery or stress loads in an aircraft wing, digital twins bridge the gap between virtual and physical prototyping, streamlining collaboration and accelerating time to market.

Digital prototyping has revolutionized collaboration in the manufacturing industry by providing real-time design capabilities, enhancing communication, streamlining validation and regulatory approvals, improving supply chain coordination, and accelerating product launches. By leveraging cutting-edge CAD, CAE, PLM, and simulation tools, engineering teams can work together more effectively, reducing costs and improving product quality. As technology advances, the integration of AI, AR/VR, and digital twins will further enhance collaboration, making digital prototyping an essential component of modern manufacturing.

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However, while digital prototypes offer many advantages, their accuracy compared to physical prototypes depends on various factors, including the fidelity of simulations, material properties, and real-world conditions.

One of the main advantages of digital prototypes is their ability to predict performance through advanced simulation. Computational Fluid Dynamics (CFD), Finite Element Analysis (FEA), and other modeling tools enable engineers to assess structural integrity, aerodynamics, and thermal properties without the need for physical testing. These simulations are highly accurate when based on well-established physics and validated algorithms. In many cases, digital prototypes can predict failure points and optimize designs with a high degree of confidence, reducing the number of physical prototypes needed.

However, digital models are only as accurate as the data and assumptions they rely on. Material properties, for example, can be difficult to model precisely. Factors such as manufacturing tolerances, imperfections in materials, and variations in environmental conditions can significantly impact the real-world performance of a product. While digital simulations can incorporate many of these variables, they may not always capture unpredictable interactions that occur in physical prototypes. For example, the way a material behaves under repeated stress or exposure to extreme temperatures may differ from what a digital model predicts.

Another limitation of digital prototypes is their reliance on simplifications and approximations. Engineers often need to make assumptions about boundary conditions, loads, and constraints to make simulations computationally feasible. While modern computing power allows for highly detailed models, there are still practical limits to what can be simulated accurately. Certain real-world phenomena, such as fluid turbulence, material fatigue, or unexpected wear and tear, may not be fully captured in a digital prototype.

Despite these challenges, digital prototypes continue to improve in accuracy due to advancements in artificial intelligence (AI) and machine learning. AI-driven simulations can analyze vast datasets and learn from past test results to refine their predictive capabilities. Digital twins, which are real-time virtual representations of physical objects, further enhance the accuracy of digital prototypes by continuously updating simulations with real-world performance data. These innovations help bridge the gap between digital and physical prototypes, making virtual models more reliable than ever.

Physical prototypes, on the other hand, provide direct, tangible results that do not rely on assumptions or simplifications. Testing a physical product allows engineers to observe real-world interactions that might be difficult to predict digitally. For instance, a physical prototype can reveal manufacturing challenges, unexpected material behaviors, or unforeseen user interactions that a digital model may overlook. This is especially critical for industries such as automotive and aerospace, where safety and reliability are paramount.

That said, physical prototyping comes with its own limitations. It is often expensive, time-consuming, and resource intensive. Creating multiple iterations of a physical prototype for testing and refinement can significantly extend the development cycle. Additionally, physical testing may not always reveal underlying design flaws until multiple tests are conducted under different conditions. In contrast, digital prototypes allow for rapid iteration and refinement before committing to physical production.

In many cases, a hybrid approach combining both digital and physical prototyping yields the most accurate results. Companies often use digital prototypes for early-stage design validation and optimization before moving to physical prototyping for final verification. This approach maximizes efficiency while ensuring that products meet real-world performance requirements. Industries such as aerospace, automotive, and consumer electronics rely on this balance to reduce costs while maintaining high reliability and safety standards.

Digital prototypes have significantly improved in accuracy and reliability, but they are not yet a complete substitute for physical prototypes. The effectiveness of digital models depends on the quality of input data, the complexity of real-world interactions, and the limitations of computational modeling. While digital simulations can predict performance with remarkable precision, they must be complemented by physical testing to validate real-world behavior.

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ABB has announced it will invest $120 million in the U.S. to expand the production capacity of its low voltage electrification products.

The Zurich-based industrial products manufacturer says investment will enable it to meet increasing demand from customers in a wide range of key growth industries, including data centers, buildings and utilities.

The move is expected to create 50 new jobs at a new advanced manufacturing facility in Selmer, Tenn., and will double the size of its existing manufacturing site in Senatobia, Miss., creating 200 new jobs.

“Demand is increasing steadily for advanced electrification technologies, driven by growth in key sectors including data centers and utilities. Today’s announcement will support our future growth in the U.S., ABB’s largest global market,” said Morten Wierod, ABB’s Chief Executive Officer.

As part of the commitment, ABB will invest $80 million to build a new, 320,000 sq. ft. facility in Selmer, replacing an existing ABB Selmer operation. This is expected to increase production capacity by more than 50%. The new factory will produce essential electrical distribution equipment for large-scale industrial and technology-driven facilities, including data centers, factories and high-rise residential and office spaces where consistent, high-quality power is critical. ABB’s busway and bus plug products help businesses simplify their power distribution, supporting seamless expansion or reconfiguration without the need for extensive rewiring. The new facility, expected to open in Q4 2026, will add 50 new skilled jobs.

ABB will also invest $40 million to double the footprint of its Senatobia facility to meet increasing demand for advanced low voltage circuit breakers from customers across North America. When the new facility opens in Q2 2026, the 200 new jobs will increase the workforce in the Senatobia facility to more than 1,000.

In the past three years, ABB has invested more than $500 million in its US business, including opening a new $100 million manufacturing facility and innovation laboratory for industrial electric drives in New Berlin, Wisconsin in October 2024. A new $40 million ABB factory will open in Albuquerque, New Mexico in April 2025, to manufacture the latest technologies for power grid hardening and resilience.

All new ABB sites showcase the latest technologies for sustainable operations, bringing together ABB’s digital and renewable energy solutions to increase energy efficiency and reduce emissions.

ABB’s US revenue was close to $9 billion in 2024, accounting for about 27 percent of the ABB Group total. With approximately 17,000 employees across the US, ABB has nearly 40 manufacturing, distribution, and operational facilities across 20 states including nine major R&D centers, with a presence in all 50 states. Today, approximately 75-80 percent of the products ABB sells in the US are manufactured in the U.S.

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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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