A Machine Learning Approach to Shipping Box Design
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. 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. The ultimate assortment of box sizes is also well tested on both real and simulated customer orders before deployment into production. Our machine learning approach to designing shipping box sizes is adopted quickly and widely in Walmart eCommerce family. Within a year, the methodology has been applied respectively to jet.com, walmart.com and samsclub.com. The new box assortments have achieved 1%-2% reduction in number of boxes, 5%-8% increase in overall utilization rate, 7%-12% reduction in order split rate, and 3%-5% savings in transportation cost.
jet.com is competing as a 2019 Innovative Applications in Analytics Award Finalist, to see other finalists, click here.
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