Manufacturing

Why You Should Care About Generative Design

Why You Should Care About Generative Design

With PTC’s $70M acquisition of generative design software vendor Frustum announced today, and the continued focus on expanding generative design capabilities by Autodesk, Dassault Systèmes, and Siemens, manufacturers have multiple options for AI and machine learning-infused CAD and CAE (i.e. simulation).

Why does this matter?

AI (Artificial Intelligence) and machine learning (ML) are widely discussed as two of the most impactful technologies of the future.  For design, engineering, and R&D, the potential positive impacts of complementing the development process with AI and ML are astounding: lowering cost of quality (which is currently 20-25% of annual revenue at the average manufacturer), improving product success rate (which remains very low with 80%+ of products failing), and improving time to market and time to revenue by meeting customer needs accurately the first time.

The concept of generative design is gaining traction in engineering and R&D circles because of the need to optimize design topology and shape for manufacturing (particularly for 3D printing), as well as the enormous influx of data that product development teams are faced with. They realize there are golden nuggets of learning to be found in data from multiple sources such as IoT-connected product and assets, competitive and market information, manufacturing processes, as well as customer buzz about your product on social business networks.  It’s too much for any one director of product management, engineering, or R&D to process, which is where AI and ML make their entrance.

As designers, product managers, developers, and engineers brainstorm during the ideation process, iteratively model new products, or address the inevitable product and service changes that will come, a machine learning algorithm applied to a library of innovation data simply enhances the number of options considered — ultimately resulting in not only more rapid decision support but also more accurate decision making.  Digital twins of products and assets can further augment this ML-powered engineering/R&D process by providing visual context, which improves communication and collaboration across a team from products to the business and to supply chain, manufacturing, and service.

Generative design is one example of what “digital transformation” means to product management, R&D, Engineering, and Innovation – enabling augmented, rapid decision support during the New Product Introduction (NPI) process.  The fundamental aspects of generative design – big data analytics, machine learning, optioning, decision support, and problem solving – can be applied to other processes in your business across engineering, manufacturing, supply chain, and service. The ultimate result is an iterative, closed loop that leads to constant learning about products, processes, and customers, resulting in greater product success.

Throughout 2019, I’ll continue to write about generative design, and the broader transformation of product and service innovation.  As always, I look forward to staying connected with you throughout the year.

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