New Zealand-based Miraka, the world’s first dairy processor to get its power from renewable geothermal energy, has chosen smart manufacturing software Plex from Rockwell Automation to become even more efficient and sustainable.

Miraka will use Plex to integrate its enterprise resource planning (ERP) systems. ERP is a software system that helps organizations streamline and automate their core business processes—including financial management, human resources, supply chain, sales, and customer relations—across the entire enterprise.

Miraka says its use of geothermal energy helps it “emit 92% less manufacturing carbon emissions than traditional coal-fired factories, giving Miraka one of industry’s lowest global carbon footprints.”

The dairy company will use Plex to connect, automate, track and analyze its operations—from the pasture to the factory floor— to take its core values of excellence and innovation to the next level.

Robert Bell, Miraka CFO, calls Plex a “single source of truth,” with intuitive tools that will help Miraka optimize their business and operational performance by increasing efficiencies.

Plex supports Miraka’s goals to become even more resilient, agile, and sustainable by offering a holistic view across the enterprise so Miraka quickly can respond to market demands and customer changes without interrupting production. The Plex software was built around the pillars of smart manufacturing, helping companies not only streamline their operations, but making it easier for them to follow industry standards and grow their business.

“Plex is a modular system, so it can grow and adapt as needs change in the future, allowing companies like us to remain agile and stay ahead of the competition,” added Bell.

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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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Reducing the number of physical prototypes needed also limits unnecessary shipping, packaging and transportation emissions for each prototype iteration and it’s required raw materials. As companies move from traditional trial-and-error prototyping methods to virtual testing, they cut down on the carbon emissions associated with transporting products, tools, and prototypes across long distances.

With tools like simulation and generative design, engineers fine-tune product designs for maximum resource efficiency, reducing material consumption for each unit and often improving product performance. This results in products that use fewer raw materials and are optimized for energy efficiency, which aligns with both cost savings and environmental sustainability.

By testing designs digitally, engineers can predict how products will perform in real-world manufacturing environments. This allows for optimized and more energy-efficient manufacturing processes, reducing waste in energy consumption during production. When designs are optimized for efficiency beforehand, it often leads to more sustainable production methods.

Digital twins and product lifecycle management tools track and manage products from concept to end-of-life. This leads to products designed for longevity, easy maintenance, and recyclability, which supports circular economy principles. Products can be designed to be disassembled and reused, reducing waste and promoting sustainability in the long term.

Using digital prototyping tools, engineers can virtually test the performance of alternative, sustainable materials before committing to them. These informed decisions on material selection could favor eco-friendly materials that meet both product performance and environmental goals. It ensures that manufacturers can adopt sustainable materials without compromising on quality or functionality.

Digital prototyping also helps eliminate inefficiencies in design and manufacturing, which have a direct impact on cost. These savings can be reinvested in sustainable practices such as renewable energy adoption, waste reduction programs, or further innovation in eco-friendly products. The more efficient the process, the lower the overall environmental impact, which usually aligns with corporate sustainability goals.

For manufacturing engineers, digital prototyping provides the ability to design, test, and optimize products more efficiently, leading to lower material waste, reduced energy use, and more sustainable products. For CEOs, it means cost savings, a reduced environmental footprint, and the ability to meet increasingly important sustainability goals while staying competitive in the market. By embracing digital prototyping, manufacturers can lead in both innovation and sustainability while driving value for the company.

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Here’s a breakdown of key tools and software commonly used for digital prototyping:

3D CAD (Computer-Aided Design):

These tools allow designers and engineers to create detailed digital models of products. They support both 2D and 3D modeling and are used to design everything from simple parts to complex assemblies. Parametric and direct modeling capabilities allow for flexibility in making changes and iterations during the design process.

Simulation and Analysis:

Simulation enables users to test how a design will behave in real-world conditions without creating physical prototypes. They use mathematical models to simulate physical properties such as stress, thermal behavior, fluid dynamics, and vibration. They help engineers predict potential performance issues and optimize designs before production.

Visualization/Rendering:

Used for creating and visualizing digital models with a focus on aesthetics and functionality, these tools are important in the early design phase. They help visualize how a product will look and function in its environment, making them useful for concept development, visualization, and basic design adjustments.

Virtual Reality (VR) and Augmented Reality (AR):

Part of the hype cycle five years ago, VR and AR have quietly become a big part of interacting with digital prototypes in immersive, 3D environments. VR tools simulate a fully virtual model, whereas AR tools overlay digital models onto the physical world. Both are used to visualize how prototypes will look and behave in real-world settings, providing an intuitive way to review designs and test user interactions.

Product Lifecycle Management (PLM):

PLM tools manage a product’s entire lifecycle—from initial design to end-of-life. They integrate various design, simulation, and testing stages to allow teams to collaborate efficiently, track revisions, and maintain up-to-date data across all stages of the product development process.

Rapid Prototyping and 3D Printing:

These tools convert 3D models into instructions for additive manufacturing (such as 3D printing), enabling the creation of physical prototypes quickly and cost-effectively. They are commonly used to test form, fit, and function of designs before committing to full-scale production.

Digital Twin Platforms:

A digital twin is a virtual representation of a physical product, system, or process. These platforms collect data from sensors and simulations to provide a real-time, dynamic view of the product’s performance. They are used to monitor the performance of prototypes, track how they evolve over time, and optimize their operations in real-world conditions.

Generative Design:

Artificial intelligence (AI) and cloud computing are combining to automatically generate optimized design solutions based on specific input parameters and constraints (such as weight, material, strength, and cost). They create myriad design options to help designers explore innovative, efficient solutions that might not be immediately obvious in traditional design processes.

Electronic Design Automation (EDA):

These tools are used to design and prototype electronic circuits and printed circuit boards (PCBs). They help engineers create, simulate, and test electronic components and systems in the digital space, ensuring that the circuits are functionally correct before physical assembly. They also help manage the layout and routing of components for optimal electrical performance.

Each tool plays a role in different stages of the digital prototyping process, whether you’re creating a 3D model, simulating real-world conditions, testing functionality, or preparing for physical production. For manufacturing engineers and managers, selecting the right set of tools depends on the specific needs of the product being developed, such as complexity, cost, and how closely the prototype needs to mirror the final product. Tools like SolidWorks, ANSYS, Fusion 360, and Teamcenter are often popular in industrial environments where both design and testing are crucial.

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Faster Product Development Cycle

Digital prototyping allows engineers to create virtual prototypes of products, which can be tested, analyzed, and iterated upon quickly. This leads to faster product development cycles because physical prototypes—often time-consuming and costly to build—are replaced with digital models that can be easily modified and tested in virtual environments.

Multiple teams (design, testing or manufacturing) can work on the same digital prototype simultaneously, allowing for more collaborative efforts and quicker decision-making.

Cost Savings

Traditional prototyping involves building physical models, which can be expensive, especially if several iterations are needed. With digital prototyping, these physical prototypes are eliminated, saving on materials, labor, and production costs.

Problems in the design phase are identified and resolved early, avoiding costly rework during the manufacturing phase. This reduces the likelihood of production delays and scrap due to design flaws.

Simulation tools allow engineers to analyze the material requirements and structure before physical production, leading to optimized use of materials and reducing waste.

Enhanced Design Precision and Quality

Digital prototypes allow engineers to simulate real-world conditions, ensuring that designs are optimized for performance, strength, and durability before moving to production. This leads to higher-quality end products with fewer design flaws.

Engineers can perform complex tests (e.g., stress, thermal, fluid dynamics) on digital prototypes to see how they will behave under various conditions, which might be difficult or impractical to replicate with physical prototypes.

Quick revisions to the digital prototype enable engineers to try out multiple variations of a design, ensuring the best possible version before manufacturing.

Better Collaboration and Communication

Digital prototypes can be shared across teams and even external partners globally, enhancing communication between departments (e.g., design, engineering, manufacturing) and improving alignment on goals, timelines, and requirements.

Managers and stakeholders can easily visualize and interact with the digital prototype, facilitating more effective decision-making and feedback. This improves transparency in the design process and helps prevent misunderstandings.

Streamlined Production Processes

Digital prototypes can be designed with the manufacturing process in mind, allowing engineers to simulate how a product will be built, identify potential manufacturing challenges, and optimize the design for ease of production.

Once a digital prototype is refined, it can be used to create detailed specifications for tooling, machinery, and assembly processes. This helps ensure a smoother transition from design to actual manufacturing with fewer adjustments needed on the factory floor.

Simulating assembly processes in the digital space can help identify potential issues in assembly lines or machinery setups, minimizing costly mistakes and time-consuming adjustments during physical production.

Customization and Flexibility

With digital prototypes, engineers can quickly adapt designs to meet customer-specific needs or adjustments. This is particularly advantageous for industries that require high levels of customization or need to pivot based on market demands.

Once a product design is finalized digitally, scaling up production becomes smoother because the digital model can be used for a variety of manufacturing methods (e.g., additive manufacturing, CNC machining, injection molding).

Integration with Advanced Manufacturing Technologies

Digital prototypes integrate seamlessly with additive manufacturing (3D printing), which enables quick and cost-effective production of physical prototypes or even end-use parts, enhancing flexibility in the prototyping process.

Engineers can optimize designs specifically for additive manufacturing techniques using digital prototyping, which fits into Simulation-Driven Design for Additive Manufacturing (DfAM). This reduces the need for costly adjustments during the actual production process.

Enhanced Innovation and Risk Mitigation

Digital prototyping allows engineers to experiment with different design ideas without the risk or cost of building physical prototypes. This fosters a more innovative environment, as teams can quickly test a variety of concepts and find the best solution.

By identifying design flaws, performance issues, and manufacturing challenges in a digital environment, potential risks are minimized before physical production begins, leading to reduced risks of product recalls or costly revisions during the manufacturing phase.

Sustainability Benefits

The ability to simulate and optimize designs virtually reduces the need for multiple physical prototypes, resulting in less material waste and a more eco-friendly development process.

Through digital modeling, engineers can analyze energy consumption, waste, and emissions, helping to design more energy-efficient products and production processes that align with sustainability goals.

Investing in digital prototyping offers significant advantages in terms of cost savings, faster time to market, and improved product quality. They can use it to test and iterate designs rapidly, streamline manufacturing processes, and encourage more innovative solutions. Ultimately, digital prototyping helps create higher-quality products, reduce risks, and improve the overall efficiency of the manufacturing process, all of which make it a powerful tool for modern manufacturers.

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AI is not going to replace your workers and make strategic executive decisions. But any manufacturer not carefully considering a well-thought-out AI strategy is leaving money on the table. And it doesn’t matter if you are an enterprise-scale manufacturer or regional company with less than 150 employees, there are plenty of ways to extract value and gain an edge from AI.

“One area where manufacturers can quickly find ROI from AI is real-time knowledge sharing and translation, where AI breaks down language barriers by instantly translating and summarizing documentation like standard operating procedures, repair logs, and shop floor communications,” says Qaiser Habib, Head of Canada Engineering at Snowflake, a cloud data platform developer based out of Bozeman, Montana.

Snowflake has developed a flexible cloud-based platform for modern data teams looking to store, process, and analyze data in a highly scalable and cost-effective manner while enabling collaboration across different departments and organizations. The company has offices in 50 cities around the world. Habib speaks to us from Snowflake’s Canadian engineering hub in Toronto.

“This enables global manufacturing teams [and supply chain partners] to collaborate seamlessly, share best practices across facilities, and access critical operational insights in their native language.”

This is one of several “low-hanging fruit” areas Habib says will drive successful adoption of AI amongst manufacturers.

Manufacturers can also find near-immediate AI ROI is in key areas such as predictive maintenance, where AI monitors equipment to prevent costly unplanned downtime. Additionally, AI guided maintenance solutions can analyze repair logs and equipment manuals to recommend troubleshooting steps faster for more effective repairs.

According to Habib, the key to creating AI ROI is using AI solutions that understand your company and industry-specific processes rather than generic solutions.

AI predictions for manufacturing

Habib made a few predictions about how manufacturers will approach and interact with AI for the next year or so. Here’s a look:

Shift from AI hype to ROI: 2025 will mark the transition from AI experimentation to measurable business impact. 2024 saw widespread hype and experimentation as organizations explored AI and solidified their data strategies. Organizations are now demanding concrete returns, and engineering teams must identify specific, high value problems where AI can deliver measurable results and move beyond proofs of concept to full production systems that emphasize accuracy and reliability. The focus will be on operationalizing large language models (LLMs) and taking evolved approaches to security, governance, and observability.

This shift is particularly evident in the manufacturing industry, where enterprise AI applications are delivering tangible results through computer vision quality control systems. These systems are transforming production by dramatically reducing inspection time compared to manual processes, catching subtle defects that human inspectors might miss, preventing costly downstream quality issues, and enabling 24/7 continuous inspection without fatigue.

Organizations will prioritize AI projects that can demonstrate clear financial impact within specific timeframes, moving away from speculative “AI for AI’s sake” projects toward targeted solutions for well-defined business problems.

Engineers will face the stress test: With attention shifting towards ROI, engineering teams are experiencing unprecedented pressure to validate AI investments while maintaining code quality, security, and team wellbeing. As engineers, we are accustomed to working in fast-paced environments with new technologies and innovations, but there is now added pressure to balance rapid AI implementation with sustainable team practices.

To foster a productive and engaged workforce, leaders must ensure their teams have a crystal clear understanding of the business case behind their AI initiatives and provide the necessary training needed. As organizations prioritize these efforts, upskilling through certification programs and professional development initiatives will remain a critical focus in the workplace.

The key to success in 2025 will be finding the sweet spot between aggressive AI adoption and sustainable engineering practices. Teams that can demonstrate ROI while maintaining team health and code quality will set the standard for the industry, making strategic upskilling and clear business alignment more critical than ever.

AI-as-a-Service will accelerate AI adoption: AI-as-a-Service (AlaaS), which is the delivery of AI tools, apps, and capabilities as a cloud-based service, will emerge as a game-changer in 2025, making AI implementation more accessible and cost-effective for organizations facing adoption hurdles. By removing the need for large upfront investments in hardware and development, AIaaS allows companies to bolt AI capabilities onto existing systems with built-in security and governance features.

Now, instead of requiring developers and engineering teams to build and maintain their own AI systems, they can focus on solving business problems rather than building AI infrastructure from scratch. This also means teams can scale their AI initiatives based on their needs and select AI tools or services tailored to specific use cases. For example, a healthcare provider could use AlaaS to quickly stand up a diagnostic tool that analyzes medical images for early disease detection, while a large retailer can rapidly deploy GenAI-powered chatbots that pull from warranty and return policies to enhance customer service.

The AIaaS revolution also creates new opportunities for engineers to develop and monetize their own AI applications within cloud-based platforms. Similar to how mobile app stores transformed software distribution, AIaaS platforms will enable a new generation of AI-powered startups and create additional revenue streams for developers.

Manufacturing’s AI sweet spot

What does a sweet spot for AI adoption and engineering sustainability look like for a small or medium sized manufacturer that doesn’t have the scale to dedicate an executive to managing this?

“For small and medium manufacturers, the AI adoption sweet spot lies in focusing on practical, high-impact projects with clear ROI and avoiding overly complex, experimental initiatives,” Habib says, citing examples such as using conversational AI interfaces to make ad hoc data and analytics more accessible, creating agents to automate tasks like quality inspections, or implementing retrieval-augmented generation (RAG) systems to search, summarize, and recommend actions from knowledge bases.

At a small manufacturer, RAG systems could be used to improve customer support, technical documentation, or internal knowledge management. For example, an AI-powered chatbot could retrieve relevant technical manuals or past troubleshooting cases to answer customer inquiries more accurately. Additionally, RAG systems could assist with automated reporting or data analysis by generating insights from production data or historical reports, helping the manufacturer make better decisions.

Habib says manufacturers will leverage AIaaS through readily available cloud solutions that integrate with their existing operations. For example, they could use AI-powered demand forecasting services to optimize inventory levels, implement digital twin simulations to test process changes before deployment, or utilize natural language AI to automatically generate technical documentation from production data.

He adds that these solutions can be implemented incrementally, with costs scaling based on actual usage, making AI adoption more manageable and cost-effective than building custom solutions from scratch.

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It’s clear that digital prototyping has become a transformative tool for various industries, particularly in manufacturing. It offers significant advantages such as reducing time-to-market, improving product design, lowering costs, and facilitating more effective collaboration.

Here are some of the key industries that benefit the most from digital prototyping:

Digital prototyping in the automotive industry

Digital prototyping allows for rapid prototyping of car components and systems, significantly reducing the need for physical prototypes. Engineers can simulate and optimize designs virtually, testing different materials, shapes and performance characteristics. These models will be used across different departments (e.g., design, manufacturing, testing) can access the same digital model, ensuring a consistent understanding of the product.

Physical prototype testing can be expensive, especially in the automotive industry. With digital models, manufacturers can run simulations (such as crash tests, aerodynamics, or stress analysis) to ensure safety, reliability, and performance. This also aids in customizing vehicles for specific customer requirements, ensuring faster production of tailor-made solutions, such as when responding to fix a recall or if the customer has changed its design of another system in the vehicle.

Why It’s Important:

• Enables simulation of complex systems (e.g., engines, suspension, electronics) before physical production, which is crucial for reducing defects.
• Helps with the integration of new materials, which can be tested virtually for performance under various conditions.
• Assists in evaluating manufacturability, reducing the need for redesigns and ensuring cost-effective production methods.

Aerospace innovation with digital prototyping

Much like in the automotive sector, digital prototyping allows for the simulation of aerodynamics, structural integrity, and performance. Engineers can identify weak points, test material stress, and even simulate changes in environmental or operating conditions (like when a door plug suddenly pops out of a fuselage).

Prototypes are subjected to complex testing scenarios in the virtual world, reducing the risk of costly repetitions during physical tests or in the real-world application of the products. These digital tools allow engineers to simulate lightweight and composite materials for weight-saving without compromising strength or safety.

Why It’s Important:

• Aerospace components are typically high-cost and subject to stringent safety regulations. Digital prototyping helps ensure compliance with these regulations while minimizing waste and rework.
• Engineers can model complex assemblies (e.g., aircraft wings, propulsion systems) virtually, which is vital given the precise engineering requirements and intricate geometries in aerospace.
• Digital prototyping helps with the creation of parts that are difficult to manufacture conventionally, facilitating the integration of additive manufacturing (3D printing) for complex geometries.

Prototyping in consumer electronics

In consumer electronics, digital prototyping enables faster time-to-market for electronics such as smartphones, laptops, and wearables. Features like thermal analysis, signal integrity, and stress testing can be done virtually, allowing engineers to refine designs and optimize performance before committing to hardware.

Why It’s Important:

• The complexity of electronics (e.g., PCBs, microprocessors) demands a high level of precision, and digital prototyping can simulate electrical, mechanical, and thermal behaviors.
• Engineers can optimize the placement of components on a board or inside enclosures, allowing for better designs that minimize space and improve overall efficiency.
• Digital tools also help to evaluate manufacturability, ensuring that designs can be easily translated into production with minimal assembly issues.

Optimizing Industrial Equipment and Machinery

Digital prototyping is especially useful in designing and testing large, complex machinery or industrial systems, like pumps, compressors, or conveyor systems. It helps in simulating real-world conditions while pushing feeds and speeds to the limit without a physical prototype. Engineers can simulate the operation of machinery under stress or in extreme conditions, helping predict failure points and prevent costly downtime after production.

Digital prototypes allow manufacturers to design variations of machines with different features tailored to specific operational requirements, improving efficiency and productivity for their customers and providing a competitive advantage.

Why It’s Important:

• Engineers can simulate how the system will operate in different environments and configurations, helping to identify design flaws before physical production starts.
• Helps in determining the optimal assembly process, minimizing tooling costs, and ensuring that all parts fit together correctly.
• Allows for virtual testing of wear and tear on components, extending the machine’s lifecycle and improving product reliability.

Medical devices: digital prototyping for precision

Few manufacturers face stricter regulatory standards than those developing medical devices. Digital prototyping helps manufacturers ensure that devices meet safety and performance standards throughout the lifetime of the device. For custom implants or prosthetics, digital prototyping allows for the creation of patient-specific devices, enhancing the product’s fit, comfort, and function. they can be virtually tested for wear, fatigue, and safety under various simulated conditions, reducing the risks associated with product failures.

Why It’s Important:

• The high precision required in medical device manufacturing means that digital prototypes can be tested in ways that physical models may not easily allow.
• Virtual models help in ensuring that devices meet all necessary ergonomic, mechanical, and functional requirements before they are physically produced.
• It also allows for better collaboration with medical professionals in the design process, ensuring that the devices are as effective and comfortable as possible for patients.

Digital prototyping in Architecture and Construction

It’s hard to find an industry with higher upfront costs in the design stage than architecture, construction and infrastructure. Digital prototyping in this industry is often associated with Building Information Modeling (BIM), which creates detailed, 3D models of buildings and infrastructure to simulate the performance of the structure, such as energy usage, airflow, and stress on materials. Architects, engineers, and clients can conduct virtual walkthroughs of the building before any physical construction begins, ensuring that all aspects of the design are feasible and functional—once a structure is poured, it’s exceedingly difficult and expensive to make changes. With digital prototypes, architects, engineers, and construction teams can collaborate more efficiently, as they have a unified visual model to work from.

Why It’s Important:

• Digital prototyping helps with evaluating structural integrity and safety during the planning phase, ensuring that the design can be built efficiently and cost-effectively.
• Engineers can work with architects to ensure that the materials chosen for construction are manufacturable, sustainable, and cost-effective.

Digital prototyping has become invaluable across these industries by speeding up design processes, cutting costs, improving collaboration, and ensuring product quality. For manufacturing engineers, it offers the ability to test designs virtually, optimize product performance, and address potential issues before moving into physical production, leading to a more efficient and cost-effective manufacturing process. As industries continue to embrace digital transformation and digital twins, digital prototyping will become even more integral to in nearly every sector of the economy.

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Definition and Process Overview

Traditional prototyping involves the physical creation of a product or component, usually using materials that are similar to those intended for the final product but sometimes out of cheaper plastic or even clay. These prototypes are often built manually, either by hand or using a subtractive manufacturing process like milling or machining.

The process begins with a design, followed by building a physical model through methods such as 3D printing, CNC machining, or injection molding. After a prototype is built, it’s tested for form, fit, and function. Based on test results, the prototype may be revised, and the process starts at the beginning again.

Digital prototyping, on the other hand, is entirely virtual. It involves using Computer-Aided Design (CAD), Computer-Aided Engineering (CAE), and other advanced simulation tools to create a digital representation of the product. Engineers simulate the behavior, performance, and manufacturability of a design without having to physically produce it. These digital prototypes are analyzed for structural integrity, aerodynamics, thermal properties, and other parameters before any physical prototype is made. Virtual testing (e.g., finite element analysis, computational fluid dynamics) helps refine the design.

Time and Speed

Traditional prototyping often requires significant lead times. The process of creating a physical prototype can take days to weeks, depending on complexity, material availability, and equipment setup. Each iteration of a prototype requires time to fabricate, test, analyze, and refine. Changes made to the design often require a new prototype, adding additional time, effort and cost.

Digital prototyping dramatically reduces this time. Since the prototype exists digitally, changes can be made and tested almost instantaneously. Engineers can simulate hundreds or even thousands of design iterations in a fraction of the time it would take to build physical models. For instance, a simulation run that might take hours could result in immediate feedback on the design’s performance, which could take days or weeks with traditional methods.

Cost Implications

The costs associated with traditional prototyping can be substantial. Creating physical models incurs material costs, labor, and machine time. Costs multiply quickly if numerous iterations of a design are needed, especially if the prototype uses expensive materials. Furthermore, testing the physical prototype might lead to additional expenses for failure analysis, repairs, and remanufacturing of prototypes.

With digital prototyping, costs are primarily associated with software licenses, computing power, and highly skilled engineers. Digital models also allow for the identification of design flaws early in the process, reducing the need for costly last-minute changes or expensive rework in the manufacturing phase.

Flexibility and Iteration

Traditional prototyping is inherently less flexible. Making design changes typically requires rebuilding or altering physical components, which can be time-consuming and costly. Even minor adjustments, such as adjusting a hole diameter or reshaping a contour, likely requires a full rebuild of the prototype, adding cost and time.

On the other hand, digital prototyping offers virtually unlimited flexibility. Changes can be made to the digital model instantly, and simulations can be rerun in a matter of hours, enabling rapid iteration. This is especially beneficial when testing alternative design solutions or optimizing performance criteria. The ability to quickly explore a wide range of design possibilities without incurring significant costs or rework gives engineers a powerful tool for refinement.

Accuracy and Precision

Traditional prototyping is limited by the precision of the manufacturing process. Even with advanced machinery, minor inaccuracies can creep into prototypes due to human error, material imperfections, or mechanical limitations of the equipment used. These inaccuracies may be evident in part dimensions, surface finish, or overall assembly.

Digital prototyping, conversely, allows for a level of precision that is only limited by the software and initial design. CAD software offers highly accurate digital representations, and simulations can handle various factors, such as material properties, forces, or motion to ensure the prototype performs as intended. This accuracy translates into better-prepared designs before any physical creation takes place

Collaboration and Communication

Collaboration and communication in traditional prototyping are often hindered by geographic and logistical limitations. Engineers, designers, and manufacturers need to meet in person to discuss changes or challenges with the physical prototypes. This can lead to delays in feedback and miscommunications, especially if teams are spread out over multiple locations.

With digital prototyping, collaboration is streamlined. Design files can be shared and worked on in real-time, regardless of location. Virtual simulations provide a common platform for discussing and analyzing design performance, which leads to more effective decision-making and quicker responses to design challenges. Cloud-based collaboration, where multiple engineers or departments can simultaneously evaluate a design, further accelerate product development.

Testing and Validation

Traditional prototyping allows for physical testing, which can be invaluable for assessing the real-world behavior of a product, particularly in cases where external forces such as temperature, pressure, or human interaction must be accounted for. However, physical testing is often time-consuming and limited in scope.

In contrast, digital prototyping uses advanced simulations to test a product’s performance under various conditions without requiring physical tests. Through techniques like finite element analysis (FEA), computational fluid dynamics (CFD), and kinematic simulations, digital prototypes can be subjected to virtual stress tests, fatigue analysis, and other scenarios to predict real-world performance with a high degree of accuracy. While some physical testing is still essential, digital simulations provide a more cost-effective and faster way to validate designs.

Sustainability Considerations

Traditional prototyping can generate substantial waste, especially when several iterations are required. Scrap material, failed parts, and excessive energy consumption are all environmental concerns associated with physical prototype manufacturing. Additionally, transportation of parts, sometimes across global supply chains, contribute to the carbon footprint.

Being a virtual process, digital prototyping minimizes material waste and, to a degree, energy consumption. Engineers can validate designs thoroughly before committing to physical manufacturing, reducing the need for excessive iterations and material usage.

The key difference between digital prototyping and traditional prototyping lies in their approach to design, iteration, and testing. Digital prototyping offers faster iteration, cost savings, higher accuracy, and a more sustainable process. It enables manufacturers to avoid costly mistakes, optimize designs before they are physically created, and streamline collaboration across teams.

However, it is important to note that digital prototyping does not entirely replace physical prototypes; certain testing and validation processes still benefit from real-world interaction. Both approaches have their place in the modern manufacturing landscape, but understanding their unique advantages helps in leveraging each at the right stage of product development.

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What is Digital Prototyping?

Digital prototyping refers to the process of using computer-aided design (CAD), simulation and other digital tools to create a virtual prototype of a product. This prototype is a fully functional 3D model that can be analyzed and tested for performance, manufacturability and assembly before a physical prototype is ever made. By using digital prototypes, engineers and designers identify design flaws early in the process, significantly reducing the risk of expensive errors and delays in the product development process.

In traditional product development, physical prototypes are built for testing and evaluation, which can be expensive, time-consuming and resource-intensive. With digital prototyping, engineers test and modify designs in a virtual environment, allowing for quicker iterations and more informed decision-making. This process also facilitates better collaboration across teams and helps align the product design with manufacturing capabilities.

Key Components of Digital Prototyping

Computer-Aided Design (CAD): CAD software is the cornerstone of digital prototyping. It allows engineers to create precise 3D models of products and components. These digital models are then used to analyze the geometry, fit and function of the product. The ability to quickly create and manipulate 3D models helps identify potential design issues before any physical work begins.

Computer-Aided Engineering (CAE): CAE tools are used to simulate and analyze the behavior of a product under various conditions. These simulations can include stress analysis, thermal analysis, fluid dynamics and kinematic simulations, which help engineers understand how their design will perform in real-world conditions. By performing simulations early in the design process, engineers can identify potential failures and optimize the design for better performance and efficiency.

Finite Element Analysis (FEA): FEA is a specialized form of CAE that breaks down complex products into smaller, manageable parts (elements) and simulates how each part responds to different forces. This technique is particularly useful for analyzing the strength and durability of a product under load, predicting failure points and determining the most efficient use of materials. FEA is a vital tool in industries such as automotive, aerospace and electronics, where product reliability and safety are paramount.

Virtual Reality (VR) and Augmented Reality (AR): VR and AR technologies allow engineers to interact with digital prototypes in immersive environments. VR can provide a fully immersive experience, while AR can overlay digital models onto the real world. These technologies are particularly useful for visualizing how a product will function or look in a physical space, helping to catch design flaws that might not be apparent in 3D CAD models alone. VR and AR also enable collaboration with stakeholders, regardless of geographic location, further enhancing design reviews and decision-making.

Additive Manufacturing (3D Printing): While digital prototyping itself focuses on virtual design, additive manufacturing plays a crucial role in turning digital models into tangible prototypes. 3D printing allows engineers to quickly fabricate physical models directly from digital files, speeding up the prototyping process. This process is particularly useful for testing form, fit and function, as well as for producing low-volume parts and iterations of designs.

The Digital Prototyping Process

The process starts with conceptual design, where engineers and designers use CAD software to develop an initial 3D model of the product. This model includes the geometric details of the product, material properties and design intent. The focus at this stage is on form and functionality rather than on final manufacturing details.

Once the 3D model is created, engineers can use CAE tools to simulate how the product will behave under different conditions. This can include stress, strain, thermal, fluid, or motion analysis. The goal is to identify potential issues such as structural weaknesses, thermal inefficiencies, or improper fit. Engineers can make adjustments to the design based on the simulation results, allowing for optimization before any physical prototypes are made.

Based on the feedback from simulations and analysis, the design is refined. Engineers may adjust geometry, material choices, or manufacturing processes to improve the product’s performance or reduce costs. During this stage, iterative testing and fine-tuning are crucial. Digital prototypes allow for rapid modifications without the need for physical prototypes, which speeds up the design cycle.

After optimizing the design, the virtual prototype is validated using more advanced simulations and tools like FEA or motion analysis. Validation ensures that the design is manufacturable, reliable and safe. Additionally, virtual prototyping can include testing for manufacturability by evaluating how the product will be produced, assembled and maintained.

Once the digital prototype is validated, engineers can use additive manufacturing (3D printing) to create a physical prototype. This prototype allows for real-world testing of form, fit and function. It also enables stakeholders to evaluate the design before moving to full-scale production. Additive manufacturing is particularly useful for small-scale production runs, low-cost prototyping and designs that require complex geometries.

Benefits of Digital Prototyping for Manufacturing Engineers

Digital prototyping significantly reduces the costs associated with physical prototyping, which often involves expensive materials and labor. With digital models, engineers can simulate the functionality and manufacturability of a product without the need to build each prototype physically. The need for expensive and time-consuming physical testing is minimized.

By enabling rapid iteration and early detection of issues, digital prototyping accelerates the design process. Engineers can test multiple variations of a design in a virtual environment before deciding on the final solution. This shortens product development cycles and allows manufacturers to bring products to market more quickly.

The ability to simulate a product’s performance before physical testing helps ensure that it will meet the desired specifications. By identifying weaknesses and optimizing the design early in the process, engineers can produce more reliable and high-quality products. This leads to fewer product recalls and higher customer satisfaction.

Digital prototypes can be shared and modified across teams, enabling better collaboration between engineers, designers and other stakeholders. Virtual environments, such as VR or AR, allow people to interact with the design from different locations, making it easier to make decisions and ensure everyone is aligned on the project goals.

With the ability to test and iterate designs virtually, digital prototyping makes it easier to optimize products for performance, cost and manufacturability. Engineers can experiment with different materials, manufacturing processes and design configurations without incurring the costs of physical prototypes.

The ease with which designs can be modified and tested allows engineers to explore new ideas and push the boundaries of traditional manufacturing. Digital prototyping fosters innovation by removing many of the constraints imposed by physical prototypes, allowing for more creative solutions and advanced product designs.

Challenges of Digital Prototyping

While digital prototyping offers significant benefits, it is not without its challenges.

The software and tools used in digital prototyping, such as CAD, CAE and FEA, can be complex and require specialized training. Engineers need to have expertise in these tools to effectively create and analyze digital prototypes.

As digital prototypes become more complex, managing large volumes of data becomes a challenge. Ensuring data consistency, version control and secure storage are important aspects of using digital prototypes effectively.

Integrating digital prototyping into established manufacturing processes can be challenging. Many organizations may need to upgrade their infrastructure or rework their workflows to take full advantage of digital prototyping.

While digital prototypes allow for extensive simulation, the results depend on the accuracy of the input data and the assumptions made during the modeling process. Simulations may not always perfectly predict real-world performance and physical prototypes may still be needed for final validation.

Digital prototyping represents a significant advancement in product development, offering manufacturing engineers the ability to design, test and refine products in a virtual environment before physical production. As digital tools continue to evolve, the integration of technologies like additive manufacturing, VR and AR will further enhance the capabilities of digital prototyping, allowing manufacturers to innovate and optimize designs.

For manufacturing engineers, embracing digital prototyping is not just about adopting new tools, but about rethinking the entire approach to product design and manufacturing. By leveraging the power of digital models, simulations and virtual environments, engineers can build more reliable, cost-effective and innovative products.

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Obviously, additive manufacturing itself is not the sole driver of digital transformation, it is an essential part of a broader digital ecosystem that allows manufacturers to modernize their operations, optimize designs and streamline production workflows.

One key trend in this shift is the varying rates of additive manufacturing adoption across different regions. In the U.S., manufacturers tend to adopt new technologies like additive manufacturing at a faster pace due to their willingness to take risks. “Companies in the U.S. are more willing to take risks and adopt technologies that are new to them,” said Michael Wohlfart, Business Development Manager for EOS, an additive technologies company. “I see a lot more pragmatism there.”

In contrast, European manufacturers take a more cautious approach. “On the European side, we have to do a lot more convincing. It takes more iterations of business cases until companies finally buy in,” Wohlfart explained. While European companies are not slow to adopt, their process tends to be more deliberate, with a greater emphasis on validating the business case for any new technology before making significant investments.

Digital integration

At the heart of digital transformation is the integration of digital tools and technologies into the entire manufacturing process. Additive manufacturing fits into this transformation seamlessly because of its ability to generate digital data during production. Unlike traditional manufacturing, which often relies on physical drawings and paper-based processes, additive manufacturing produces a continuous stream of digital data that can be used throughout the lifecycle of a product.

Wohlfart mentions spare parts as an example—many companies still operate with outdated, non-digital drawings. “Since [additive] is a digital technology, anything you produce with our system automatically generates digital data, which makes it much easier to replicate those parts in the future,” Wohlfart said.

The challenge with spares is if a company needs to replace a part designed decades ago and they can’t find a supplier for it. The first step is digitizing the drawing for 3D printing. From there, you can build a comprehensive digital value chain because the additive manufacturing process generates a lot of digital data, such as quality assurance data.

“We can pull sensor data from the machine, like the oxygen content during part production, or even more detailed information like turbine speed from the filter system,” he says. “All this data feeds into the manufacturing enterprise system and builds a digital twin of the part. If there’s post-processing involved, like CNC machining, you can still feed that data in—the core digital data comes from the 3D printing process itself.”

This shift to a more digitally integrated workflow allows for more efficient production, better data tracking and streamlined quality assurance, supporting the broader goals of digital transformation within manufacturing.

The evolving additive business case

While additive manufacturing has become a mainstream tool for many manufacturers, the business case for its adoption has evolved significantly over the years. Initially, the focus was on the technical feasibility of 3D printing. As the technology matured, it became clear that additive manufacturing offered a variety of business benefits, but these advantages had to be demonstrated through solid cost-benefit analyses and ROI estimations.

“In the beginning, most discussions were about technical feasibility. It was at the edge of the early adopter phase, where companies had a strategic interest in additive manufacturing but weren’t sure it was feasible,” he says. But today, the decision to use additive or traditional methods often comes down to a well-defined business case.

“In nearly every customer conversation, we make a cost estimation for the parts they intend to produce. If there’s already a conventional counterpart, companies compare it to that,” Wohlfart says. “Ideally, they take full advantage of additive, making parts that would be impossible to manufacture with conventional methods like CNC machining. But for most cases, the decision to use additive or conventional methods still comes down to the business case.”

A crucial element of this transformation is additive manufacturing’s ability to support future-proofing for manufacturers. By producing complex geometries and optimized designs, additive manufacturing enables companies to move beyond the limitations of traditional manufacturing methods like CNC machining and casting.

“I see this mainly on the design side. Certain applications can be optimized through simulations, especially parts that interact with fluids,” he says. “Heat exchangers, for example, are a good case. Simulations often create geometries that wouldn’t be feasible with traditional manufacturing, but additive is both cost-competitive and technically capable of producing those designs,” Wohlfart said, adding that in industries like aerospace and medical devices, this flexibility in design has become a major competitive advantage.

In aerospace, additive manufacturing allows the creation of lighter, more efficient parts that can reduce weight and improve performance. “The digital advancements allow you to design without being constrained by manufacturing limitations. You can take the optimal result from simulations, which are based on physical principles, and manufacture it,” Wohlfart says.

Additive for metal

As for metal additive manufacturing, in comparison to casting and CNC, the segment is relatively new. But the machines as they are today really started evolving around 2006-2007, and the big uptick in adoption came around 2015-2016, again mainly in industries such as aerospace, medical and automotive, where high-performance systems and materials are essential.

“The resolution has improved, but that’s not the most important factor driving adoption. The real advancements are in the productivity of the systems. We moved from single-laser printers to multi-laser printers, which reduces the cost per part and decreases print time. Another key driver is the adoption of new materials. Over time, we’ve developed more materials that are suitable for 3D printing,” Wohlfart said.

The trend is high-strength aluminum alloys, which are particularly relevant for the aerospace industry. In gas turbines, there are more advanced nickel-based alloys. “The challenge with these alloys is that they’re difficult to weld, but we’ve found ways around that. Now we can process materials that were not possible five years ago,” he says.

Despite these technological advancements, Wohlfart emphasized that metal additive manufacturing still faces challenges. For instance, while it is more flexible in terms of design, additive is more commonly used in prototyping or low-volume production rather than mass production.

Digital strengths

One of the reasons additive manufacturing fits so well within the broader push toward digital transformation is its ability to integrate seamlessly with digital tools like simulation, data collection, analytics and enterprise systems. “The technology itself allows you to build a digital twin and create a complete digital value chain. With additive, it’s easier to do this compared to more conventional manufacturing methods, like casting or forging, which don’t have any digital components.” Wohlfart said. This connection between digital design, digital production and data integration is at the core of Industry 4.0, where everything from machinery to raw materials is tracked, analyzed and optimized through data-driven insights.

This broader digital ecosystem makes it easier for manufacturers to adopt other advanced technologies, such as automation and artificial intelligence and to move toward more integrated, flexible and responsive production systems.

Importantly, additive manufacturing isn’t limited to industries like aerospace and medical devices. While these sectors have been early adopters, companies in fields like tooling and defense are also discovering the advantages of additive manufacturing. “We have a wide customer base in tooling, like die casting and injection molding. It’s also very relevant in the defense industry,” he says.

Additive technology allows for faster time to market. You don’t have to wait months for a foundry or overseas supplier. For smaller machine shops, additive complements their portfolio. It’s not meant to replace CNC, but it gives them more capabilities. Some have even turned into major additive manufacturing service providers.

As more manufacturers look to incorporate additive manufacturing into their operations, it is clear that the technology is part of a broader digital transformation. Additive manufacturing fits this niche by providing manufacturers with the tools to create highly optimized designs, improve production efficiency and integrate digital workflows.

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