Digital Twins for improved product development

Digital twins are among the technologies that are transforming and will further transform the way we do business. Companies owe their success to their ability to adapt and develop new products, so relying on new technologies that can help them do so will be key to ensuring their survival.

We explain the keys to achieving this through the use of digital twins.

Digital Twins at present

In advanced industries, surveys show that almost 75% of companies that have adopted digital twin technologies have already reached at least medium levels of complexity.

However, the automotive, aerospace, and defense industries are the most advanced in the use of this technology today, while the logistics, infrastructure, and energy sectors are the most likely to grow.

Already, companies are developing ML-based geometry optimization systems that can simulate thousands of configurations to improve aerodynamics, performance benefits, cost savings, and so on. Also, simulations simulate the effect of design improvements before they are delivered to customers as physical upgrades. And even multi-physics turbine modeling to enable real-time wear prediction, performance tuning, and production optimization.

Companies pioneering the implementation of digital twins find four main motivations for this investment:

1. Digital twins provide a risk-free product development environment, allowing more design options to be explored without the cost associated with physical prototype production and testing.
2. They improve testing and validation by allowing new solutions to be evaluated in a wide range of real-world scenarios.
3. Give more detailed information about product behavior, making it easier to monitor the state of any part of the system and to track complex interactions between product elements.
4. Enable real-world data to inform product improvements by simulating the impact of proposed design changes using collected data.

The McKinsey study shows that digital twins are already making a significant difference in product development performance, reducing overall development times by 20-50%, the costs associated with pre-production prototypes, problem minimization, or a higher probability of commercial success. It also enables companies to offer a wider range of value-added after-sales services, including predictive maintenance and in-service performance optimization.

In fact, to those already mentioned above, digital twins bring even more benefits to product development:

• Improved understanding of product behavior and performance.
• Improved predictive capabilities to anticipate and prevent problems.
• Better decision-making based on real-time data and knowledge.
• Optimized product design, production, and maintenance.
• Reduced costs and risks associated with physical testing and prototyping.
• Increased agility and responsiveness to changing market and customer needs.
• Improved real-time monitoring and control of products and processes.
• Improved predictive capabilities to anticipate and prevent problems.
• Compliance with regulatory environments and standards.

Product development strategies with Digital Twins

Modern products are becoming increasingly complex, and customized and the competitive and technological advantages are becoming smaller. In this changing and challenging environment, the most important factors determining the success of a product depend on the quality of execution, the creation of a sustainable advantage, and competent cross-functional teams.

The key objectives for companies often translate into a strategy to increase development speed and product success rates, while reducing complexity and business risk from the outset.

Some companies focus more on standardization and modularisation programs to simplify manufacturing and design to reduce lead times and production costs. Others focus on better designs and sustainable supply chains and operations to deliver differentiated products and reduce operational risk.

However, in all of them, what is important is the search for better product communication as early as possible in the design phase, to shorten innovation cycles and create?more successful product designs through multifunctional solutions to market requirements.

The main problems faced by companies are:

• Modern product design and management is tedious due to the difficulty of collecting and connecting heterogeneous product data (such as risks, impacts, and costs) to designs, as each change in design changes the balance of all other criteria.
• Adaptability is another issue, as companies are forced to adapt to an increasing range of market requirements. To address this, many companies add additional specialized resources at the end of the design chain that are often not integrated enough to deliver significant value.
• Many of the tools that now exist are specialized for a single purpose or department, which typically do not meet the collaborative or cross-functional needs of today’s design challenges. Both adopting workarounds and procrastination delay the inevitable loss of competitive advantage to younger, more agile companies

Digital twins have the ability to connect data on costs, markets, risks, materials, regulations, environment, or suppliers to aid in product design or formulation. These tools can be integrated to create reports, applications, and maps to visualize the data that matter.

The dependencies between upstream ideas and their business performance become transparent, helping data to flow continuously from multiple sources into a single system. This allows teams to collaborate on the same project and understand the impacts of design changes on parameters outside their core expertise.

Use cases of Digital Twins in product development

As mentioned above, digital twins are more widespread in some industries, but their use cases are extensible to many more:

• Consumer electronics: digital twins can be used to improve the quality and resilience of the supply chain. They store detailed information about the content of products, enabling production processes to be optimized and waste to be reduced.
• Automotive: Digital twins are used in 3D automotive design, facilitating global collaboration and avoiding the delays common in standard 3D automotive rendering software.
• Industrial manufacturing: Many manufacturers are using this technology to improve product quality and reduce costs. The product can be traced from idea development to use, allowing quality problems to be identified early and reducing the likelihood of failure.
• Aerospace: can be used to simulate and optimize the performance of aircraft components and systems, as well as to reduce the time and cost of testing and certification processes.
• Energy: they are used to optimize the performance of power plants and other infrastructures. They can be used to simulate different operational scenarios and identify opportunities for optimization and cost reduction.

Digital Twin adoption

Setting up a digital twin platform is a complicated task that requires an expert partner to put the entire infrastructure in place.

One of the main challenges faced by early adopters is integrating the technology into their existing digital product development environment and broader IT infrastructure. In addition, digital twins require new ways of working, which means that a successful digital twins program involves a change management effort, requiring commitment and support from senior management and a strong management team to track milestones, develop new processes, and support organizational adoption.

To overcome these potential obstacles, one of the most widespread approaches is a phased format, where the technological challenges of the platform, architecture design, and integration are addressed in the first phases. Subsequent phases would focus on the organizational transformation required to support the new processes and work practices:

• The competitive intelligence and scoping phase identifies the types of digital twin solutions available in the sector and estimates the potential value offered by each. This provides a framework for each specific transformation process.
• In the software architecture design and definition phase, the specific software components, relationships, and interfaces that will be needed to meet the objectives are identified. Elements to be developed in-house or procured from a supplier are designed and decided.
• In the software development excellence phase, the processes and capabilities needed to develop, integrate, and launch the digital twin platform are developed.

These phases require a large staff with a wide range of specialised skills, so to integrate this technology successfully, the best option is to have an expert partner like Plain Concepts. We will bring speed from idea to PoC, and from PoC to final implementation. We run a repeatable process to generate, test and develop ideas that can lead to rapid innovations.

Our vision is built around the fusion of 3D generation and visualization capabilities. In addition, we apply artificial intelligence to create business value and position ourselves ahead of the competition.

We focus on the Digital Twin, with an approach based on 3 pillars: Acquisition, Understanding, and Visualization.

Within these pillars, we look at different interrelated capabilities, techniques, and enabling technologies that act as building blocks to conceive the Digital Twin.

Our vision of the Digital Twin envisages collaboration with different actors that support us in achieving it. We have an ecosystem of carefully selected partners that enable us to empower and accelerate the creation of the different building blocks that make up the Digital Twin. Our partner ecosystem is continuously evolving and growing as we strive to deliver the best solution.