Think out of the package: Recommending package types for e-commerce shipments

TL;DR

This paper presents a scalable, multi-stage algorithm for recommending optimal package types in e-commerce, balancing shipment and damage costs, leading to significant cost savings and damage reduction in real-world deployment.

Contribution

It introduces a novel multi-stage, linear-time algorithm with a hyper-parameter tuning method for optimizing package type selection in e-commerce shipments.

Findings

Achieved tens of millions of dollars in cost savings.

Reduced damage rate by 24% in live deployment.

Modified package types for over 130,000 products.

Abstract

Multiple product attributes like dimensions, weight, fragility, liquid content etc. determine the package type used by e-commerce companies to ship products. Sub-optimal package types lead to damaged shipments, incurring huge damage related costs and adversely impacting the company's reputation for safe delivery. Items can be shipped in more protective packages to reduce damage costs, however this increases the shipment costs due to expensive packaging and higher transportation costs. In this work, we propose a multi-stage approach that trades-off between shipment and damage costs for each product, and accurately assigns the optimal package type using a scalable, computationally efficient linear time algorithm. A simple binary search algorithm is presented to find the hyper-parameter that balances between the shipment and damage costs. Our approach when applied to choosing package type for Amazon shipments, leads to significant cost savings of tens of millions of dollars in emerging marketplaces, by decreasing both the overall shipment cost and the number of in-transit damages. Our algorithm is live and deployed in the production system where, package types for more than 130,000 products have been modified based on the model's recommendation, realizing a reduction in damage rate of 24%.