Dynamic anomaly detection for reduced perishable item wastage

A method comprising:

• training a machine learning model to detect anomalies for historical perishable items based on an amount of time the historical perishable items spent in transit to a distribution center (DC), age on arrivals (AoAs) of the historical perishable items at the time the historical perishable items were received at the DC, and estimated shelf lives of the historical perishable items at the time the perishable items were received at the DC;
• obtaining item-specific data associated with new perishable items received at the DC after the historical perishable items;
• detecting, by the trained ML model, an anomalous batch of perishable items within the perishable items based on the item-specific data;
• calculating age-related data associated with the anomalous batch of perishable items, the age-related data comprising AOAs of the anomalous batch of perishable items and an estimated remaining shelf life of the anomalous batch of perishable items at the time the anomalous batch of perishable items was received at the DC;
• estimating a future impact of the anomalous batch of perishable items using the calculated age-related data and an item-specific dynamic threshold, wherein the estimated future impact comprises an estimated quantity of throws due to wastage;
• generating a recommendation to accept or reject the anomalous batch of perishable items based on the estimated future impact, the recommendation including a recommended action to mitigate perishable item wastage;
• and
• surfacing the generated recommendation via a user interface (UI) device.

One or more computer storage devices having programming instructions stored thereon, which, upon execution by a processor, cause the processor to:

• train a machine learning model to detect anomalies for historical perishable items based on an amount of time the historical perishable items spent in transit to a distribution center (DC), age on arrivals (AoAs) of the historical perishable items at the time the historical perishable items were received at the DC, and estimated shelf lives of the historical perishable items at the time the perishable items were received at the DC;
• obtain item-specific data associated with new perishable items received at the DC after the historical perishable items;
• detect, by the trained ML model, an anomalous batch of perishable items within the plurality of perishable items based on the item-specific data;
• calculate age-related data associated with the anomalous batch of perishable items, the age-related data comprising AOAs of the anomalous batch of perishable items and an estimated remaining shelf life of the anomalous batch of perishable items at the time the anomalous batch of perishable items was received at the DC;
• estimate a future impact of the anomalous batch of perishable items using the calculated age-related data and an item-specific dynamic threshold, wherein the estimated future impact comprises an estimated quantity of throws due to wastage;
• generate a recommendation to accept or reject the anomalous batch of perishable items based on the estimated future impact, the recommendation including a recommended action to mitigate perishable item wastage;
• and
• surface the generated recommendation via a user interface (UI) device.