AI and the Atomic Task System: Automating Supply Chain Data Processing for Efficiency

Supply chains generate vast amounts of data from diverse sources such as suppliers, customers, logistics providers, and internal systems. Often, this data is unstructured, coming in the form of emails or PDF documents. There is no “source of truth” in the supply chain. So companies face the challenge of connecting disconnected systems to get a clear view of their function.

At Loop, we use our AI to resolve supply chain data differences. Whether it’s from different taxonomies and terminologies or different sources across the logistics industry. A core piece that allows us to resolve these differences is the atomic task system.

Atomic Task System The atomic task framework is a system with the goal of breaking down the overall goal into smaller, manageable atomic tasks. Each atomic task focuses on extracting or normalizing a specific piece of information. A task is a simple piece of work that can be performed, whether it be extracting the carrier name off of an invoice, or normalizing that carrier name to its entity representation. 

Here at Loop, our atomic task system is built internally with the core philosophy that we can design tasks to suit the supply chain domain. This means some of the simpler tasks can be completed by individuals without much domain knowledge, while the more difficult tasks leverage Loop’s competitive advantage and are completed exclusively by domain experts. The atomic task system is designed to be flexible, tasks can be dependent on other tasks, and we can craft tasks as granular as we’d like.

Each task can be completed by humans or AI models. With each new task we design and roll out, it is initially completed by humans with high domain expertise, and once enough training data has been collected, we build multiple machine learning models