Case Study: a Fortune 500 shipping and logistics company optimizes package deliveries with Labelbox

a Fortune 500 shipping and logistics company cuts time and cost by 50% with Labelbox

A Fortune 500 shipping and logistics company wanted to use machine learning to assess package fill levels in its trucks to optimize shipping volume and unloading processes. The data science team faced a challenge as they lacked a scalable way to produce the high-quality training data needed for their computer vision models and had no software to visualize their unstructured image data. They turned to the vendor Labelbox and its Annotate, Catalog, and Boost products for a solution.

Using Labelbox, the company prioritized object detection to classify packages and fill rates. With Labelbox Boost, they imported expert human feedback from a small image set and propagated those labels across hundreds of thousands of images, creating an automated workflow. This solution from Labelbox saved roughly 50% in time and cost, reduced workloads by thousands of hours, and enabled the company to ship production-ready models in just five months.