An Investigation of Sustainability, Preference, and Profitability in Design Optimization

Abstract

Customer preferences for sustainable products are dependent upon the context in which the customer makes a purchase decision. This paper investigates a case study in which fifty-five percent of survey customers say they prefer recycled paper towels, but do not purchase them. These customers represent a profit opportunity for a firm. This paper explores the impact of investing capital in activating pro-environmental preferences on a firm’s profitability and greenhouse gas (GHG) emissions through a multi-objective optimization study. A product optimization is designed to include models of carbon dioxide emissions, manufacturing costs, customer preference, and technical performance. Because the optimization includes a tradeoff between recycled paper and performance, a model of customer preferences, and a market of competing products, the maximum GHG reduction occurs at less than 100% recycled paper. Also, the tradeoff between GHG reductions and profit is not dictated by the configuration of the product, but instead by its price. These results demonstrate the importance of including customer preferences with engineering performance in design optimization. Investment in the activation of pro-environmental preferences is high at all points on the Pareto optimal frontier, suggesting that further engineering design research into the activation of pro-environmental product preferences is warranted.

Market-System Design Optimization With Consider-Then-Choose Models

“…1 Contours of profits optimal solutions to Eqs. (15) and (16). Green denotes profits that are nearly, or exactly, zero.…”

“…In order to forecast profits engineering researchers have integrated generalized linear models that predict customer choice share for potential products. Discrete choice analysis (DCA) [5][6][7] is most commonly used, with implementations adopting a variant of the logit or probit models [4,[8][9][10][11][12][13][14][15][16][17].…”

A Machine Learning Approach to Customer Needs Analysis for Product Ecosystems

“…Burnap et al [25] made use of feature learning methods to improve design preference prediction and found that interpretation and visualization of these features augmented data-driven design decisions. MacDonald et al [26] examined customer preferences for sustainable products with a multi-objective optimization study. Long et al [27] presented a framework to link the must-be requirements with design in order to create more consumer-representative products.…”

Should Optimal Designers Worry About Consideration?

“…Conventional discrete choice models [1,2] have been applied in design for market systems [3,4,5,6,7,8,9,10] in the past decade. Generally, the choice model serves to forecast demand as a function of product features, thus enabling design decisions that maximize forecast profits.…”