Abstract: An out-of-stock indicator is received that indicates a product is out-of-stock or believed to be out-of-stock. Information about the features of the products and store are obtained or determined. The features are applied to a first machine learning model, which yields a probability that the item is out-of-stock. The obtained probability is compared to a threshold, and if the probability value is above a threshold, then the PI value is adjusted. If not above the threshold, then scans are monitored for out-of-stock conditions, and some time later the features will be applied to a different model, and the above-process repeated. In aspects, this process occurs over a certain time period or until the PI is adjusted.

Abstract: An out-of-stock indicator is received that indicates a product is out-of-stock or believed to be out-of-stock. Information about the features of the products and store are obtained or determined. The features are applied to a first machine learning model, which yields a probability that the item is out-of-stock. The obtained probability is compared to a threshold, and if the probability value is above a threshold, then the PI value is adjusted. If not above the threshold, then scans are monitored for out-of-stock conditions, and some time later the features will be applied to a different model, and the above-process repeated. In aspects, this process occurs over a certain time period or until the PI is adjusted.

Abstract: An out-of-stock indicator is received that indicates a product is out-of-stock or believed to be out-of-stock. Information about the features of the products and store are obtained or determined. The features are applied to a first machine learning model, which yields a probability that the item is out-of-stock. The obtained probability is compared to a threshold, and if the probability value is above a threshold, then the PI value is adjusted. If not above the threshold, then scans are monitored for out-of-stock conditions, and some time later the features will be applied to a different model, and the above-process repeated. In aspects, this process occurs over a certain time period or until the PI is adjusted.