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Mr. Jean-Sébastien Matte, a doctoral student at Ï㽶ÊÓƵ in the area of Operations Management will be presenting his research proposal entitled:

ECOWISR (Excess Consumption, Overproduction and Waste: Impact Solutions through Reduction): The Case of the Fast Fashion Industry

Thursday, May 2, 2024 at 11:00 am – 1:00 pm

(The presentation will be conducted on Zoom)

Student Committee Chair: Professor Mehmet Gumus

Please note that the presentation will be conducted on Zoom and only the student and committee members may participate.

ABSTRACT:

There is an increasing call for the fast fashion industry to lower its environmental impact. My dissertation studies different aspects of this challenge from a behavioural operations perspective.

The first chapter studies how product variety and heterogeneity in preferences affect customer choices. Using a large, event-based clickstream dataset provided by one of Europe’s largest fast fashion retailers, the study characterizes and quantifies the effects of assortment variety on customer choice. The study proposes a novel definition and representation of assortment variety as a bipartite graph, which allows to define variety along three dimensions. Results show the dataset has three main segments, with contrasting utility for economic variables, variety, and propensity to convert to a purchase. The estimation results also show a combination of linear and nonlinear relationship between variety and customer utility. The findings highlight the inherent tensions of offering and efficiently managing large product variety to please different customer types.

The second chapter studies how fast fashion retailers can lower the environmental impact of their assortments by balancing profit maximization and impact minimization. The study uses an experimental and data-driven approach to answer this question by combining an incentive aligned choice-based conjoint (IA-CBC) experiment and in-between subject education manipulation (to study the effect of consumer education on environmental preferences), a Hierarchical Bayes Multinomial Logit (HB-MNL) estimation model, and a multi-objective product-line and pricing optimization. By progressively tightening the environmental impact constraints the study reveals the profit-environmental impact trade-off. The results offer novel insights into consumer preferences for three environmental attributes (Recycled Content, Durability, and Circularity Design), the effect of consumer education on environmental preferences, and the impact of environmental constraints on business performances. Furthermore, the study provides managers with a fully integrated decision-support tool.

The last chapter studies overproduction in fast fashion and examines whether the inefficacy in the industry comes from the inability in the models to address uncertainty in consumer demand, or from the lack of deterrents to overproduction (e.g., lenient regulations). The study uses a robust product-line and inventory model in which uncertainty in consumer preferences is de-aggregated into two components: heterogeneity in taste, and uncertainty in measurement. The former is captured with by parametrizing the demand function as a mixture of Multinomial Logit (MNL) models, while the latter is captured through one of the product attributes (Style/Trend). The study uses a synthetic dataset of choice data for products defined using 4 attributes and price.

The results from the studies provide several managerial insights and readily-implementable tools, as well as strong policy implications that can help in solving the many environmental challenges the fast fashion industry is facing.