Abstract: Systems, apparatuses, and methods are provided herein for in-store production management. A method comprises training a probabilistic demand model using the training dataset based on a machine learning algorithm, determining a probabilistic forecast of a demand for a perishable product at a store location on a date based on the probabilistic demand model, applying an objective function to each of the plurality of demand values in the probabilistic forecast to determine an objective value for each of the plurality of demand values, determining a target supply value based on a select demand value with a highest objective value, determining a production plan based on the target supply value and a carryover value retrieved from the store data database, and providing a production management user interface that comprises a display of an identifier of the perishable product and the production plan associated the perishable product.

Abstract: Systems, apparatuses, and methods are provided herein for in-store production management. A method comprises training a probabilistic demand model using the training dataset based on a machine learning algorithm, determining a probabilistic forecast of a demand for a perishable product at a store location on a date based on the probabilistic demand model, applying an objective function to each of the plurality of demand values in the probabilistic forecast to determine an objective value for each of the plurality of demand values, determining a target supply value based on a select demand value with a highest objective value, determining a production plan based on the target supply value and a carryover value retrieved from the store data database, and providing a production management user interface that comprises a display of an identifier of the perishable product and the production plan associated the perishable product.