Imagine a team spending months developing a new product. The design looks perfect on paper, but the first physical prototype reveals a serious performance problem. The team now must redesign, rebuild, and test again. Each change adds time and cost.

This is where Digital Twins transform product development. Instead of waiting for physical prototypes to reveal problems, teams can create a virtual version of a product and use it to explore designs, test performance, and identify potential issues earlier.

Many organizations, for example, NASA uses digital twin technology to expand testing and support development before physical hardware is ready.

The value is already visible in industry. A Siemens survey found that 94% of digital twin users said the technology better informed new product development.

The value of digital twins is becoming clearer as product development becomes more complex. Research from NIST shows that digital twins can support product design, manufacturing optimization, performance monitoring, and predictive decision making. NIST estimates that their potential impact across US manufacturing could reach $37.9 billion annually.

In this article, we will look at the seven stages of product development, the challenges at each stage, and how a digital twin development company can help businesses use virtual models to build better products faster and with less risk.

What Is a Digital Twin in Product Development?

A digital twin is a virtual representation of a real product, system, or process. It can combine product data, engineering models, simulations, sensor data, and other information to understand how the physical product may behave.

In product development, a digital twin can be created as early as the concept and design stage. For example, an automotive company can create a virtual vehicle and use computational simulations to study aerodynamics, thermal performance, structural behaviour, component interactions, and different design configurations before building a physical prototype.

The key difference is that a digital twin goes beyond visualization. A 3D model represents the geometry of a product, while a digital twin connects that geometry with engineering models, simulation data, system behaviour, and, where available, real-world data.

This allows engineering teams to run what if scenarios, identify potential performance issues, compare design alternatives, and understand the impact of changes before committing to physical development.

As a result, digital twins can support the entire product development process, from idea validation and design to prototyping, testing, refinement, and product launch. NIST also highlights simulation, monitoring, prediction, and optimization as important applications of digital twin technology.

The 7 Stages of Product Development and Where Digital Twins Fit

Product development does not end with creating a design. It moves through several stages, starting with an idea and ending with a product that is ready for customers. Each stage has its own decisions, risks, and development challenges.

The seven stages we will look at are Idea Generation, Idea Screening and Validation, Product Planning and Specification Development, Design and Prototyping, Product Development, Testing and Refinement, and Product Launch.

Let us look at each stage and understand where digital twins can add value.

Phase 1: Idea Generation

Idea generation is where a product development journey begins. Teams identify customer problems, market opportunities, new technologies, and possible improvements to existing products. The goal at this stage is to create product ideas that could solve a real problem or meet an unmet need.

Key Challenges

Teams may have many ideas but limited resources to explore all of them. An idea may also look promising from a business perspective but create technical challenges when engineers start working on it.

How digital twins help

Digital twins can help teams create early virtual representations of product concepts. Engineers can explore different configurations and simulate basic product behaviour before investing heavily in physical development.

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

Phase 2: Idea Screening and Validation

At this stage, teams evaluate the ideas generated earlier. They assess factors such as market demand, technical feasibility, development costs, expected benefits, and potential risks before deciding which ideas should move forward.

Key Challenges

A product idea may have strong market potential but still be difficult or expensive to develop. Teams need better information before committing significant engineering resources.

How digital twins help

A digital twin can help teams test different product concepts virtually. Engineers can compare configurations, run what if scenarios, and identify potential performance issues before building physical prototypes.

This gives decision makers more technical evidence when deciding which product concepts deserve further investment.

Phase 3: Product Planning and Specification Development

Once an idea is approved, teams define what the product needs to achieve. This includes product requirements, technical specifications, performance targets, resources, timelines, and other development requirements.

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

Key Challenges

Product requirements do not always work well together. Improving one feature can create problems in another area. For example, making a component lighter may reduce its strength, while making it stronger may increase its weight and cost.

Hence, the teams need to find the right balance between performance, cost, durability, and other product requirements.

How digital twins help

Digital twins allow engineers to model these relationships and study the impact of different specifications. Teams can simulate different requirements and understand how they may affect product performance.

This helps engineers identify unrealistic requirements and make better tradeoffs before detailed design begins.

Phase 4: Design and Prototyping

At this phase of product development, the concept is converted into a detailed design. Teams create engineering designs and prototypes to understand how the product will look, function, and perform.

Key Challenges

Building a physical prototype takes time, money, and engineering effort. The bigger problem is that one prototype may not reveal every issue. If the team finds a problem after testing, they may need to change the design and build another prototype.

For example, an automotive company may build a prototype vehicle and discover during testing that its battery system generates too much heat. Engineers then need to modify the design, build another prototype, and test it again. Several such iterations can increase both development time and cost.

How digital twins help

Digital twins can support virtual prototyping by allowing engineers to test different design configurations before creating every physical prototype.

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

This does not mean physical prototypes become unnecessary. Instead, virtual testing can help teams reduce unnecessary iterations and focus physical testing on the designs and conditions that matter most.

Phase 5: Product Development

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

Key Challenges

Products often contain many components that interact with one another. A change to one component can affect other parts of the system. Managing these dependencies can become difficult as product complexity increases.

How digital twins help

Digital twins can represent individual components as well as complete systems. Engineers can use simulations to study how components interact and evaluate the effect of design changes.

For example, an electric vehicle digital twin could bring together information about the battery, motor, thermal system, and other components. Engineers can then study how a change in one area may affect overall vehicle performance.

Phase 6: Testing and Refinement

The product is tested to determine whether it meets its functional, performance, quality, and reliability requirements. Problems identified during testing are addressed through further design changes and refinement.

Key Challenges

Physical testing is an important part of product development, but it can take considerable time and resources. It is also not always practical to test a product under every possible condition.

A product that performs well in normal conditions may behave differently when exposed to extreme temperatures, heavy loads, high speeds, or unusual usage patterns.

For example, an industrial machine may perform normally during standard operating conditions but experience excessive vibration when running at maximum load for several hours. Recreating this situation repeatedly with physical equipment can be expensive and time consuming.

Teams may also need to test several combinations of load, speed, temperature, and operating time to understand how the machine will behave in the real world.

How Digital Twins Help

Digital twins can create a virtual testing environment where engineers can evaluate the product under different conditions. They can run multiple scenarios, compare expected performance, identify potential failure points, and study the effect of design changes.

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

Digital twins therefore complement physical testing rather than simply replacing it. They can help engineers decide which physical tests are most important and identify potential problems earlier.

Phase 7: Product Launch

After testing and refinement are complete, the product is prepared for the market. The launch stage includes introducing the product to customers and collecting feedback about its performance and adoption.

Key Challenges

Product development does not necessarily end when a product reaches the market. Real world usage can reveal problems or opportunities that were not visible during development.

How digital twins help

When a digital twin is connected to real world product data, teams can monitor product performance and compare actual behaviour with expected behaviour. They can identify unusual patterns, understand how products perform in different conditions, and use these insights to improve operational efficiency and 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 does not stop evolving after it reaches the market. Customer usage and product performance can provide valuable information that helps teams improve the next version. When a digital twin is connected to real world product data, this information can be brought back into the product development process.

This creates a continuous cycle:

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

For example, imagine a company that develops industrial pumps. After the pumps are deployed, sensors collect data on temperature, pressure, vibration, and energy consumption. The digital twin can use this data to show how the pump is performing under different operating conditions.

If the data shows that the pump consistently experiences higher vibration when operating at a certain pressure, engineers can investigate the cause in the digital environment and test possible design changes. The findings can then be used to improve the next version of the pump.

This creates a continuous connection between product development and real-world product performance. Instead of treating product launch as the end of development, companies can use what they learn from products in the field to improve future designs.

Real Life Examples of Companies Using Digital Twins for Product Development

Companies are using digital twins for product development to solve practical problems such as reducing physical prototypes, testing complex designs, improving product performance, and accelerating validation.

The examples below show how businesses are globally using digital twins at different stages of product development, from early design and virtual testing to product refinement.

Example 1: Airbus

Airbus is using digital twin technology as part of its broader digital approach to aircraft development. The company uses digital methods across design, manufacturing, and operations, with digital twins helping engineers simulate aircraft behavior and evaluate designs before and during physical development.

For programs such as Eurodrone, Airbus has combined physical testing with a representative digital twin during design reviews.

What was the challenge?

• Aircraft development involves complex interactions between aerodynamics, structures, systems, manufacturing, and safety requirements.
• Physical prototypes and testing can be expensive and take considerable time.
• Engineers need to evaluate aircraft behaviour under many different operating scenarios.
• Design changes can affect multiple engineering disciplines and production processes.

How did digital twins help Airbus with product development?

• Engineers can simulate aircraft behaviour under different real world scenarios using physics-based models.
• Digital models can be used alongside physical testing to validate product performance.
• Airbus uses detailed 3D models and digital representations of aircraft functions and behaviour to support engineering decisions.
• Digital twins help reduce dependence on physical prototypes during early product development.
• Digital continuity allows information from design and manufacturing to remain connected across the product lifecycle.

Example 2: BMW

BMW Group is using digital twins and advanced simulation to improve vehicle and factory development. In collaboration with NVIDIA and Siemens, BMW is using computational fluid dynamics and accelerated computing for automotive aerodynamics.

BMW also uses digital twins of its production facilities to test how new vehicle models will work with existing production systems before physical changes are made.

What was the challenge?

• Vehicle development requires extensive testing of aerodynamics and overall performance.
• Physical changes to production facilities can take weeks to implement and test.
• New vehicle models must fit existing production lines without creating collisions or workflow problems.
• Complex manufacturing systems involve equipment, robots, logistics, buildings, and vehicle data.

How did digital twins help BMW with product development?

• BMW uses virtual vehicle and physics-based simulations to study aerodynamic performance and optimize vehicle design.
• Digital twins allow BMW to test how a new vehicle will move through the production environment before making physical changes.
• Automated virtual collision checks can identify whether a new vehicle model will interfere with production equipment.
• BMW reports that these virtual collision checks can take about three days, compared with almost four weeks of real-world testing previously.
• BMW has also developed digital twins for more than 30 production sites, connecting building, equipment, logistics, and vehicle data for virtual production planning.

Example 3: Daimler

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

What was the challenge?

• Vehicle development involves large amounts of complex product and engineering data.
• Engineers need to review and understand this data from different perspectives.
• Traditional physical reviews can make design changes slower and more expensive.
• Teams working across different locations need a common environment for collaboration.
• Design and engineering issues need to be identified before they become expensive 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 in the same virtual environment.
• The system supported faster 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 different ways depending on the industry, product complexity, and engineering challenge. The technology can be used 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 what they want to improve.

A digital twin can support many areas of product development, but trying to model the entire product lifecycle from the beginning can make the project complex and expensive.

Here is a practical approach to getting started.

1. Identify a Specific Product Development Challenge

Start with a problem rather than the technology. Identify where your current product development process faces the most difficulty.

For example, you may want to reduce physical prototype iterations, improve product testing, optimize a component, or understand why a product performs differently in 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 makes it easier to determine 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 need to work together so that the digital twin accurately represents the product.

4. Build and Validate the Digital Twin

The digital twin can then be developed using the appropriate 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 determine whether its behaviour accurately represents the real product. Enterprises can follow a structured digital twin development process to bring these technologies together based on their specific product requirements.

5. Connect the Twin to the Product Development Process

The digital twin becomes more valuable when engineers can use it as part of 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 takes a simulation first approach to digital twin development, focusing on how products and systems behave in real world conditions rather than only creating visual representations. With experience across 7+ digital twin projects, the team develops behaviour driven twins for operational and planning requirements.

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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