How generative design could reshape the future of product development

Most product-development tasks are complex optimization problems. Design teams approach them iteratively, refining an initial best guess through rounds of engineering analysis, interpretation, and refinement. But each such iteration takes time and money, and teams may achieve only a handful of iterations within the development timeline. 

​Today’s technology offers an alternative. Digital simulation and analysis are now so fast that designs can be evaluated in seconds—or even less. Algorithms can automatically adjust the geometry of a part between simulations, with no manual refinement required. Using artificial-intelligence techniques, these new generative design systems can explore a much larger universe of possible solutions, comparing the results of thousands of simulations to close in on a design that delivers the most favorable combination of attributes.

For some types of engineering problems, generative algorithms already outperform human engineering teams. Furthermore, they can produce non-intuitive solutions that may never have been found using traditional processes.

The most common use for generative design algorithms today is structural optimization: creating parts that provide sufficient strength, stiffness, and fatigue resistance with the minimum of material. Such applications are common wherever weight is a primary consideration, such as in the design of internal structural parts for handheld tools (to improve ergonomics), sports equipment (to enhance performance), vehicles and aircraft (to reduce fuel consumption or increase payload), or any product where shipping weight is a significant cost driver. When material is a primary cost driver, greater structural efficiency can lead to substantial savings both from a cost and a sustainability perspective.

Across industries ranging from automotive to aerospace to sporting goods, generative algorithms have reduced part cost by 6 to 20 percent, part weight by 10 to 50 percent, and development time by 30 to 50 percent (exhibit). A power-tool manufacturer, for example, reduced a die-cast support bracket’s part weight by 26 percent and its cost by 8 percent, without affecting the interface between the part and the larger assembly. For a large, die-forged component, generative optimization yielded a weight reduction of around 40 percent—subtracting a full kilo from the finished product.
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Reference: mckinsey.com