Product Digital Twins

Our approach involves defining your organization’s digital twin target-future state, developing and quantifying the business case, and creating and pressure testing your digital twin roadmap.

Our expertise supports the technical definition of the data architecture and tool chain for a broader product lifecycle management and digital twin transformation. We guide the development of machine-learning-based surrogate models to optimize product design, and partner with you to connect the multiple advanced simulation models that deliver a system twin.

We work with you to manage your program and steer your transformation and implementation of prioritized use cases. Maximum value can be delivered when we help you to build organization-wide digital twin capabilities, supported by change-management expertise to help users thrive in the new working model.

Helps you develop products tailored to your consumers’ needs for lighter, faster, lower cost, or sustainable products by supplementing your development efforts with real-time usage information. Product digital twins also address customer expectations for enhanced operation, such as live updating of functionality through over-the-air updates and connectivity to a broader ecosystem of related products and services.

End-to-end linking of all product data in one standard data repository across the full product lifecycle drives transparency across the entire value chain. Product digital twins combine part and material definitions with manufacturing and in-service data. This allows for higher levels of modularization and enables product managers to make data-driven decisions about product portfolios and lifecycles.

Accelerates the pace of future product development by automating the process of testing and iterating new designs and increases optimization capability by unlocking the potential to test new design frontiers.

We partnered with the technical team of a client in the energy space to establish a repeatable process for optimizing the design of custom turbines. By building and implementing a deep, learning-based surrogate model of the turbine, we enabled the team to test one million additional design iterations in 75 percent of the original time before selecting the most effective version.

A major automotive OEM was observing an increase in portfolio complexity due to a growing number of derivatives and variants. We helped them design and implement the concept of the digital twin, with a virtual replica of each product across the entire lifecycle. Core to this approach was a streamlined data layer, which increased the visibility across the value chain while also reducing the number of interfaces to internal systems. The result was a decline in portfolio complexity, the possibility to flash new functionality onto the cars in the field, and a decrease in data provisioning and usage costs. These changes translated to two percent EBITDA-cost improvements and a safeguarding and enablement of ten percent of revenue.

We helped a major aerospace company define a strategy and execution plan to build a multisystem digital twin that could simulate dynamic testing in various conditions. This resulted in a 12-month reduction in product verification and validation time along with a 15-25 percent decrease in overall time to market.