Explainable AI based Interventions for Pre-season Decision Making in Fashion Retail

TL;DR

This paper proposes an explainable AI tool for collaborative decision-making in fashion retail, aiming to optimize product development and reduce overproduction by integrating stakeholder insights and interpretability.

Contribution

It introduces a novel explainable AI framework with interventional analysis to facilitate collaborative pre-season decision-making among diverse fashion stakeholders.

Findings

Enhanced stakeholder engagement through explainability

Improved accuracy in product forecasting models

Potential reduction in overproduction and inventory costs

Abstract

Future of sustainable fashion lies in adoption of AI for a better understanding of consumer shopping behaviour and using this understanding to further optimize product design, development and sourcing to finally reduce the probability of overproducing inventory. Explainability and interpretability are highly effective in increasing the adoption of AI based tools in creative domains like fashion. In a fashion house, stakeholders like buyers, merchandisers and financial planners have a more quantitative approach towards decision making with primary goals of high sales and reduced dead inventory. Whereas, designers have a more intuitive approach based on observing market trends, social media and runways shows. Our goal is to build an explainable new product forecasting tool with capabilities of interventional analysis such that all the stakeholders (with competing goals) can participate in collaborative decision making process of new product design, development and launch.