How Digital Twins Transform Product Development

Imagine a team dedicating months to developing a new product. The design seems flawless on paper, but the initial physical prototype exposes a significant performance issue, necessitating redesign, reconstruction, and further testing. Each modification prolongs the timeline and increases costs.

This is where Digital Twins revolutionize product development. Instead of relying on physical prototypes to uncover problems, teams can create a digital version of a product to explore designs, evaluate performance, and identify issues early on.

Numerous organizations, including NASA, utilize digital twin technology to enhance testing and development before the physical hardware is ready.

The benefits are evident in the industry. A Siemens survey revealed that 94% of digital twin users felt the technology improved new product development insights.

The significance of digital twins is becoming more pronounced as product development grows increasingly complex. Research by NIST indicates that digital twins can aid product design, manufacturing optimization, performance monitoring, and predictive decision-making. NIST estimates their potential annual impact on US manufacturing could reach $37.9 billion.

This article will explore the seven stages of product development, the challenges at each phase, and how a digital twin development company can assist businesses in leveraging virtual models to create superior products more swiftly and with reduced risk.

What Is a Digital Twin in Product Development?

A digital twin is a virtual representation of a real-world product, system, or process. It integrates product data, engineering models, simulations, sensor data, and other information to predict how the physical product may perform.

In product development, a digital twin can be established as early as the concept and design phase. For instance, an automotive company might create a virtual vehicle to study aerodynamics, thermal performance, structural behavior, component interactions, and various design configurations before constructing a physical prototype.

Unlike a mere 3D model, a digital twin goes beyond visualization by connecting geometry with engineering models, simulation data, system behavior, and real-world data, where available.

This connectivity allows engineering teams to simulate scenarios, identify potential performance issues, compare design alternatives, and understand the implications of changes before committing to physical development.

Consequently, digital twins can support the entire product development lifecycle, from idea validation and design to prototyping, testing, refinement, and product launch. NIST also emphasizes simulation, monitoring, prediction, and optimization as key applications of digital twin technology.

The 7 Stages of Product Development and Where Digital Twins Fit

Product development extends beyond design creation, progressing through several stages from initial idea to market-ready product. Each stage involves distinct decisions, risks, and challenges.

We will examine the seven stages: Idea Generation, Idea Screening and Validation, Product Planning and Specification Development, Design and Prototyping, Product Development, Testing and Refinement, and Product Launch.

Let’s delve into each stage to understand how digital twins can enhance value.

Phase 1: Idea Generation

The journey of product development begins with idea generation. Here, teams identify customer issues, market opportunities, new technologies, and potential improvements to existing products. The objective is to create product ideas that address real problems or unmet needs.

Key Challenges

Teams often face an abundance of ideas but limited resources to explore them all. An idea might seem promising from a business standpoint but present technical challenges during engineering.

How digital twins help

Digital twins enable teams to create early virtual models of product concepts. Engineers can explore various configurations and simulate basic product behavior before committing significant resources to physical development.

For instance, an automotive company considering a new vehicle design can create a virtual model to study factors such as airflow, weight, and component placement during early development.

Phase 2: Idea Screening and Validation

During this phase, teams evaluate previously generated ideas by considering factors like market demand, technical feasibility, development costs, expected benefits, and potential risks before deciding which ideas to pursue.

Key Challenges

A product idea may have strong market potential but prove difficult or costly to develop. Teams need better information before committing substantial engineering resources.

How digital twins help

Digital twins allow teams to test various product concepts virtually. Engineers can compare configurations, run scenarios, and identify potential performance issues before building physical prototypes.

This provides decision-makers with more technical evidence when determining which product concepts warrant further investment.

Phase 3: Product Planning and Specification Development

Once an idea is approved, teams outline what the product must achieve, including product requirements, technical specifications, performance targets, resources, timelines, and other development criteria.

For example, a company developing a smart fitness watch might define requirements such as battery life, sensor accuracy, screen size, water resistance, and expected operating time before proceeding to detailed design.

Key Challenges

Product requirements may not always align well. Enhancing one feature can compromise another, such as making a component lighter but less strong, or stronger but heavier and costlier.

Therefore, teams must strike the right balance between performance, cost, durability, and other product requirements.

How digital twins help

Digital twins enable engineers to model these relationships and study the impact of different specifications. Teams can simulate various requirements to understand their effects on product performance.

This helps engineers identify unrealistic requirements and make more informed trade-offs before detailed design begins.

Phase 4: Design and Prototyping

In this phase, the concept is transformed into a detailed design. Teams develop engineering designs and prototypes to visualize how the product will look, function, and perform.

Key Challenges

Creating a physical prototype requires time, money, and engineering effort. The major challenge is that a single prototype may not reveal all issues. If a problem is identified during testing, the design may need to be altered, necessitating a new prototype.

For example, an automotive company might build a prototype vehicle only to discover during testing that its battery system generates excessive heat. Engineers would then need to adjust the design, build another prototype, and test again. Multiple iterations like this can increase development time and costs.

How digital twins help

Digital twins facilitate virtual prototyping by allowing engineers to test different design configurations before creating each physical prototype.

For example, engineers can simulate structural loads, temperature conditions, fluid flow, or component interactions to identify potential design problems.

While physical prototypes are still necessary, virtual testing helps teams reduce unnecessary iterations and focus physical testing on the most critical designs and conditions.

Phase 5: Product Development

The chosen design progresses into detailed development. Engineers develop components, integrate systems, finalize materials, and prepare the product for testing and production.

Key Challenges

Products often consist of numerous interacting components. A change to one part can affect other system components. Managing these dependencies becomes challenging as product complexity increases.

How digital twins help

Digital twins can represent both individual components and complete systems. Engineers can use simulations to study component interactions and evaluate the effects of design changes.

For example, an electric vehicle digital twin could integrate information about the battery, motor, thermal system, and other components. Engineers can then analyze how a change in one area affects overall vehicle performance.

Phase 6: Testing and Refinement

The product undergoes testing to verify it meets functional, performance, quality, and reliability requirements. Issues identified during testing are addressed through further design changes and refinement.

Key Challenges

Physical testing is crucial for product development but can be time-consuming and resource-intensive. It’s often impractical to test a product under every possible condition.

A product that performs well in standard conditions might behave differently when subjected to extreme temperatures, heavy loads, high speeds, or unusual usage patterns.

For example, an industrial machine might function normally during standard operations but experience excessive vibration when operating at maximum load for extended periods. Repeatedly replicating this scenario with physical equipment can be costly and time-consuming.

Teams may also need to test multiple combinations of load, speed, temperature, and operating time to understand real-world machine behavior.

How Digital Twins Help

Digital twins create a virtual testing environment where engineers can evaluate the product under various conditions. They can run multiple scenarios, compare expected performance, identify potential failure points, and assess design changes’ effects.

For instance, engineers developing industrial equipment can simulate how the equipment might behave under different loads before conducting selected physical tests.

Digital twins complement rather than replace physical testing, helping engineers prioritize critical physical tests and identify potential problems earlier.

Phase 7: Product Launch

After testing and refinement, the product is prepared for market launch. This stage involves introducing the product to customers and gathering feedback on its performance and adoption.

Key Challenges

Product development doesn’t necessarily conclude when a product reaches the market. Real-world usage can reveal issues or opportunities not apparent during development.

How digital twins help

By connecting a digital twin to real-world product data, teams can monitor product performance, comparing actual behavior with expected outcomes. They can identify unusual patterns, understand how products perform under various conditions, and use these insights to enhance operational efficiency and reduce downtime.

Product Development Stages: Digital Twin Technologies, Data, Outputs and Examples

Product Development Stage	Digital Twin Application	Key Technologies and Data	What Teams Can Evaluate	Example Outcome
Idea Generation	Create early virtual product concepts and explore different configurations.	3D modelling, CAD data, historical product data, engineering knowledge	Concept feasibility, basic performance, configuration options	Eliminate technically impractical concepts before detailed engineering
Idea Screening and Validation	Run virtual feasibility studies and compare alternative concepts under different conditions.	Simulation models, physics-based models, analytics, historical test data	Performance targets, operating limits, technical risks	Select the concept with the strongest technical and performance potential
Product Planning and Specification Development	Model relationships between product requirements and engineering parameters.	Requirements data, system models, simulation data, engineering specifications	Performance tradeoffs, component dependencies, design constraints	Determine whether target specifications can realistically be achieved
Design and Prototyping	Use virtual prototypes to test product configurations before building physical prototypes.	CAD, CAE, CFD, FEA, thermal models, material data	Stress, deformation, airflow, heat transfer, vibration, fluid behavior	Identify design weaknesses and reduce unnecessary prototype iterations
Product Development	Build a system level representation of components and their interactions.	PLM, CAD, IoT data, system models, software models, engineering data	Component interactions, system behaviour, configuration changes	Detect integration issues before final physical assembly
Testing and Refinement	Run virtual scenarios alongside physical testing and compare predicted and actual behavior.	Simulation, sensor data, test data, analytics, failure models	Reliability, failure modes, extreme conditions, performance variations	Identify high risk conditions and prioritize physical testing
Product Launch	Connect the digital twin with the deployed product to create a continuous product feedback loop.	IoT sensors, telemetry, cloud platforms, analytics, operational data	Real world performance, anomalies, degradation, usage patterns	Feed field performance data into future product improvements

From Idea to Launch: How Digital Twins Connect the Entire Product Development

A product continues to evolve even after reaching the market. Customer usage and product performance provide valuable insights that help teams refine the next version. When a digital twin is linked to real-world product data, this information can be reintegrated into the product development process.

This creates a continuous cycle:

Product idea → Design → Development → Testing → Launch → Real world data → Product improvement

Consider a company developing industrial pumps. Once deployed, sensors collect data on temperature, pressure, vibration, and energy consumption. The digital twin uses this data to illustrate the pump’s performance under various operating conditions.

If data indicates that the pump experiences higher vibration at a certain pressure, engineers can investigate the cause in a digital environment and test potential design changes. The findings can then inform improvements for the next pump iteration.

This approach fosters a continuous link between product development and real-world product performance. Instead of marking the product launch as the end of development, companies can leverage field insights to enhance future designs.

Real Life Examples of Companies Using Digital Twins for Product Development

Companies are increasingly adopting digital twins in product development to address practical challenges like reducing physical prototypes, testing complex designs, enhancing product performance, and accelerating validation.

Below are examples of how businesses globally employ digital twins at various stages of product development, from early design and virtual testing to product refinement.

Example 1: Airbus

Airbus incorporates digital twin technology as part of its comprehensive digital strategy for aircraft development. The company employs digital methods across design, manufacturing, and operations, with digital twins aiding engineers in simulating aircraft behavior and assessing designs before and during physical development.

For programs like Eurodrone, Airbus integrates physical testing with a representative digital twin during design reviews.

What was the challenge?

• Aircraft development involves complex interactions among aerodynamics, structures, systems, manufacturing, and safety requirements.
• Physical prototypes and testing can be costly and time-consuming.
• Engineers need to evaluate aircraft behavior under various operating scenarios.
• Design changes can impact multiple engineering disciplines and production processes.

How did digital twins help Airbus with product development?

• Engineers can simulate aircraft behavior under different real-world scenarios using physics-based models.
• Digital models can complement physical testing to validate product performance.
• Airbus employs detailed 3D models and digital representations of aircraft functions and behavior to support engineering decisions.
• Digital twins reduce reliance on physical prototypes during early product development.
• Digital continuity ensures information from design and manufacturing remains connected throughout the product lifecycle.

Example 2: BMW

BMW Group utilizes digital twins and advanced simulation to enhance vehicle and factory development. Collaborating with NVIDIA and Siemens, BMW leverages computational fluid dynamics and accelerated computing for automotive aerodynamics.

BMW also employs digital twins of its production facilities to assess how new vehicle models will interact with existing production systems before implementing physical changes.

What was the challenge?

• Vehicle development demands extensive testing of aerodynamics and overall performance.
• Physical modifications to production facilities can require weeks for implementation and testing.
• New vehicle models must integrate with existing production lines without causing collisions or workflow issues.
• Complex manufacturing systems encompass equipment, robots, logistics, buildings, and vehicle data.

How did digital twins help BMW with product development?

• BMW employs virtual vehicle and physics-based simulations to study aerodynamic performance and optimize vehicle design.
• Digital twins enable BMW to test how a new vehicle will navigate the production environment before making physical adjustments.
• Automated virtual collision checks identify potential interference between new vehicle models and production equipment.
• BMW reports that these virtual collision checks take about three days, compared to nearly four weeks of real-world testing previously.
• BMW has developed digital twins for over 30 production sites, integrating building, equipment, logistics, and vehicle data for virtual production planning.

Example 3: Daimler

Daimler utilized Unreal Engine development services to create a real-time 3D environment for its engineers through its subsidiary Daimler Protics. The platform enabled engineers to work with complex product data in an interactive virtual setting. Teams could review vehicle designs, explore engineering data, and conduct virtual reality walkthroughs before making decisions in the physical realm.

What was the challenge?

• Vehicle development involves vast amounts of complex product and engineering data.
• Engineers need to review and comprehend this data from multiple perspectives.
• Traditional physical reviews can slow down design changes and increase costs.
• Teams working in different locations require a shared environment for collaboration.
• Design and engineering issues need identification before becoming costly to fix.

How did digital twin help Daimler with product development?

• Daimler created a real-time 3D environment for engineers using Unreal Engine.
• Engineers could explore complex vehicle data through interactive visualization.
• Virtual reality walkthroughs allowed teams to examine vehicle designs at full scale.
• Multiple engineers could collaborate within the same virtual environment.
• The system facilitated quicker design reviews and engineering decision-making.
• Daimler reported that the real-time 3D approach helped reduce development time and costs while supporting higher quality products.

Digital Twin Use Cases Across Industries in Product Development

Digital twins can support product development in various ways depending on the industry, product complexity, and engineering challenges. The technology is applicable for virtual prototyping, system simulation, performance analysis, testing, and continuous product improvement.

Industry	Product Development Use Case	Key Challenge	How Digital Twins Help
Automotive	Virtual Vehicle Development and Testing	Modern vehicles combine mechanical, electrical, electronic, software, battery, and thermal systems. Testing every configuration physically can be expensive and time consuming.	• Create a virtual vehicle using CAD and engineering models.  • Simulate aerodynamics, battery performance, thermal behaviour, and vehicle dynamics.  • Test different driving conditions and vehicle configurations.  • Compare simulation results with physical test data.
Aerospace	Virtual Aircraft Design and Validation	Aircraft involve complex systems and strict performance and safety requirements.   Physical testing is expensive and many operating conditions are difficult to reproduce.	• Model aircraft structures, aerodynamics, propulsion, and systems.  • Simulate different flight and environmental conditions.  • Analyze structural loads and system interactions.  • Compare virtual results with physical test data.
Medical Devices	Medical Device Design and Testing	Medical devices must meet performance, safety, usability, and regulatory requirements.     Physical testing can become complex when devices interact with the human body.	• Create virtual models of devices and relevant environments.  • Simulate loads, movement, pressure, and other conditions.  • Study device behaviour before physical testing.  • Refine designs based on simulation results.
Consumer Electronics	Product Design and Component Testing	Products contain many components that can affect thermal performance, power consumption, reliability, and overall product behaviour.	• Model components and their relationships.  • Simulate heat, airflow, power consumption, and component interactions.  • Compare different component configurations virtually.  • Use product data to improve future designs.
Industrial Equipment	Equipment Performance and Optimization	Equipment operates under changing loads, temperatures, pressures, and speeds. Testing every operating condition physically can be difficult.	• Connect engineering models with sensor data.  • Simulate different operating conditions.  • Identify abnormal vibration, temperature, or pressure patterns.  • Test potential design improvements virtually

How to Get Started with Digital Twins for Product Development

Before investing in a digital twin, companies need to clearly define their improvement goals.

A digital twin can enhance various aspects of product development, but attempting to model the entire product lifecycle from the start can make the project complex and costly.

Here is a practical approach to getting started.

1. Identify a Specific Product Development Challenge

Begin with identifying problems rather than focusing on technology. Determine where your current product development process encounters the most difficulty.

For example, you may aim to reduce physical prototype iterations, enhance product testing, optimize a component, or understand why a product performs differently under real-world conditions.

2. Define the Digital Twin Scope

Decide what the digital twin needs to represent. It could focus on a single component, subsystem, complete product, or product lifecycle.

Starting with a well-defined scope simplifies determining the required data, simulations, integrations, and development effort.

3. Connect Engineering and Product Data

A useful digital twin depends on reliable data. Depending on the product, this may include CAD models, engineering specifications, simulation results, material properties, test data, IoT data, sensor readings, and product lifecycle information.

These data sources must work together to ensure the digital twin accurately represents the product.

4. Build and Validate the Digital Twin

The digital twin can be developed using a suitable combination of 3D modeling, physics-based simulation, data analytics, IoT connectivity, cloud infrastructure, and real-time visualization.

The model should be validated against physical test results to verify its behavior accurately reflects the real product. Enterprises can follow a structured digital twin development process to integrate these technologies based on their specific product requirements.

5. Connect the Twin to the Product Development Process

The digital twin becomes more valuable when engineers can incorporate it into their existing workflow. Teams should be able to run simulations, compare design alternatives, analyze test results, and make engineering decisions without creating a separate process around the twin.

How Can MindInventory Help with Digital Twin Product Development

MindInventory adopts a simulation-first approach to digital twin development, prioritizing how products and systems behave under real-world conditions rather than solely creating visual representations. With experience in over seven digital twin projects, the team develops behavior-driven twins for operational and planning needs.

We also bring strong expertise in Unreal Engine, NVIDIA Omniverse, and Cesium, enabling the development of high fidelity, real time digital environments at asset, system, and large-scale levels.

Our modular architecture supports rapid prototyping, with functional digital twin MVPs possible within 2 to 6 weeks for faster validation and stakeholder feedback.

MindInventory also has experience delivering large scale digital twin solutions for smart city environments. These solutions can support infrastructure mapping, emergency simulations, mobility modelling, and civic data layers for government and enterprise use cases. One example is its Digital Twin Platform for Smart City Management, designed to provide a connected digital view of urban infrastructure and operations.

In addition, for enterprises and government organizations, we focus on scalability, digital twin security, data governance, compliance, and long-term usability. Our architectures can evolve from individual assets to larger connected ecosystems while adding new data sources, simulations, and intelligent capabilities as business requirements grow.

This allows enterprises to build digital twins that can adapt as their products, operations, and technology environments become more complex.

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