Title: A Machine Learning Approach to Shipping Box Design,
Abstract: Having the right assortment of shipping boxes in the fulfillment warehouse to
pack and ship customer's online orders is an indispensable and integral part of
nowadays eCommerce business, as it will not only help maintain a profitable
business but also create great experiences for customers. However, it is an
extremely challenging operations task to strategically select the best
combination of tens of box sizes from thousands of feasible ones to be
responsible for hundreds of thousands of orders daily placed on millions of
inventory products. In this paper, we present a machine learning approach to
tackle the task by formulating the box design problem prescriptively as a
generalized version of weighted $k$-medoids clustering problem, where the
parameters are estimated through a variety of descriptive analytics. We test
this machine learning approach on fulfillment data collected from Walmart U.S.
eCommerce, and our approach is shown to be capable of improving the box
utilization rate by more than $10\%$.