Facing increased volatility in their supply chain, our customer wanted better demand and supply predictions—primarily to help them improve service levels in the North American market.
Also, our customer’s bias towards carrying too much inventory was impacting margins by driving up inventory holding costs, working capital and product obsolescence costs.
Noodle.ai recommended that I reduce production for my product. I acted two weeks earlier than I would have with our traditional planning tool, which allowed me to eliminate planned production, and saved us $760K, all due to that single recommendation.
To improve fill rate, reduce inventory holding costs, and reduce obsolescence for our customer, Noodle.ai deployed three modules of the Inventory Flow Application from the Athena Supply Chain AI Suite.
In May 2020, Inventory Flow went live to 45 planners covering approximately 2,000 high-profile SKUs in the North American market. Designed to help companies quickly get started with supply chain AI, Inventory Sentinel leverages AI to sense emerging supply-demand imbalances in your network, recommending changes to the production schedule to better match inventory with demand.
In July 2020, we deployed the remaining Inventory Flow XAI modules. Inventory Precog leverages sophisticated AI inference engines that have been trained on thousands of supply chain variables to predict SKU/DC-level risks such as lost sales and inventory overages. Inventory Pathfinder recommends shipment-level allocation and deployment actions. The XAI modules all sit on the same AI-ready infrastructure, with each incremental module providing more robust, value-add functionality.
Each of Noodle.ai's risk predictions is tied to a dollar value, what we call our Value at Risk (VAR) metric. Not only does VAR enable planners to prioritize their work, it also provides managers and executives with a clearer picture of the total financial risk facing a brand, region, or business unit.
Inventory Flow provides planners with precise, action-ready recommendations to most efficiently mitigate the risks, such as how much (and when) to increase/decrease production, how much inventory to deploy across their DCs, and when (or when not to) expedite shipments.