A Machine Learning Approach to Shipping Box Design
This addresses a critical operational challenge for eCommerce businesses by optimizing box selection to reduce costs and improve customer experience, representing an incremental application of existing methods to a new domain.
The paper tackles the problem of selecting an optimal assortment of shipping box sizes for eCommerce fulfillment by formulating it as a generalized weighted k-medoids clustering problem, resulting in a more than 10% improvement in box utilization rate when tested on Walmart U.S. eCommerce data.
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 $10\%$.