Hyper-local sustainable assortment planning
Machine Learning
2020-07-28 v1 Artificial Intelligence
Machine Learning
Abstract
Assortment planning, an important seasonal activity for any retailer, involves choosing the right subset of products to stock in each store.While existing approaches only maximize the expected revenue, we propose including the environmental impact too, through the Higg Material Sustainability Index. The trade-off between revenue and environmental impact is balanced through a multi-objective optimization approach, that yields a Pareto-front of optimal assortments for merchandisers to choose from. Using the proposed approach on a few product categories of a leading fashion retailer shows that choosing assortments with lower environmental impact with a minimal impact on revenue is possible.
Cite
@article{arxiv.2007.13414,
title = {Hyper-local sustainable assortment planning},
author = {Nupur Aggarwal and Abhishek Bansal and Kushagra Manglik and Kedar Kulkarni and Vikas Raykar},
journal= {arXiv preprint arXiv:2007.13414},
year = {2020}
}