Position paper: Reducing Amazon’s packaging waste using multimodal deep learning

Since 2015, Amazon has reduced the weight of its outbound packaging by 36%, eliminating over 1,000,000 tons of packaging material worldwide, or the equivalent of over 2 billion shipping boxes, thereby reducing carbon footprint throughout its fulfillment supply chain. In this position paper, we share insights on using deep learning to identify the optimal packaging type best suited to ship each item in a diverse product catalog at scale so that it arrives undamaged, delights customers, and reduces packaging waste. Incorporating multimodal data on products including product images and class imbalance handling technique are important to improving model performance.