A Machine Learning Approach to Shipping Box Design

TL;DR

This paper introduces a machine learning method to optimize shipping box design by formulating it as a clustering problem, significantly improving box utilization in eCommerce fulfillment.

Contribution

It presents a novel machine learning approach for selecting optimal shipping box sizes using a generalized weighted k-medoids clustering formulation.

Findings

Improves box utilization rate by over 10%.

Demonstrates effectiveness on Walmart eCommerce data.

Provides a prescriptive, data-driven solution for 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 $10\%$.