Artificial Intelligence Can Be Used to Produce Better, More Cost-effective Product Designs

New research focuses on the automotive design process

Key Takeaways:

• AI can be used in both generative and predictive applications to streamline the aesthetic product design process.
• Although the proof-of-concept focused on automotive applications, the research applies across the board to all forms of aesthetic design of products, from home appliances to personal electronics or furniture.

BALTIMORE, MD, December 11, 2023 – Researchers have found that machine learning and artificial intelligence (AI) can significantly reduce cost and time in the product design phase, not only in the actual generative design of the product, but also in the predictive analysis of whether consumers will be attracted to certain designs.

The researchers focused their study on the automotive industry and revealed their findings in a peer-reviewed article, “Product Aesthetic Design: A Machine Learning Augmentation,” published in the INFORMS journal Marketing Science. The authors of the study are Alex Burnap of Yale University, John Hauser of MIT and Artem Timoshenko of Northwestern University.

“It’s well understood in the automotive industry that aesthetics are critically important to market acceptance. An improved aesthetic design has demonstrated that it can boost sales 30% or more,” says Burnap. “That’s why automakers are known to invest over $1 billion in the design of a single model.”

The current automotive design process relies mostly on the conventional human development of designs and prototypes, along with in-person testing of possible designs with actual consumers. These consumer evaluations feature the A/B testing of alternative designs in laboratory test markets. The industry calls them “theme clinics,” in which hundreds of targeted consumers are recruited and brought to a central location to evaluate aesthetic designs.

Consumers are asked to rate the designs based on established benchmarks, such as scales for “sporty,” “appealing,” “innovative” and “luxurious,” among other characteristics.  

Automotive manufacturers typically invest more than $100,000 per theme clinic for one new vehicle design. Because there are multiple aesthetic designs per vehicle, and more than 100 vehicles in its product line, General Motors alone spends tens of millions of dollars just on theme clinics.

“Through our research, we have found ways to augment the traditional product development process with machine learning tools that address both the generation of the design itself, and the testing of possible consumer acceptance or rejection of the design,” says Timoshenko.

Burnap adds, “We have developed a generative model that creates new product designs and allows designers a tool to morph potential designs more efficiently and effectively. And we have created a predictive model that helps identify those designs with high aesthetic scores. The predictive model is designed to screen newly proposed aesthetic designs so that only the highest-potential designs need to be tested in theme clinics.”

The study authors created their models using data from an automotive firm, using images of 203 SUVs that were evaluated by targeted consumers, and 180,000 high-quality unrated images.\

Link to Study.

About INFORMS and Marketing Science

Marketing Science is a premier peer-reviewed scholarly marketing journal focused on research using quantitative approaches to study all aspects of the interface between consumers and firms. It is published by INFORMS, the leading international association for operations research and analytics professionals. More information is available at www.informs.org or @informs.

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