Abstract: A method can include upon receiving, from a policy update engine, one or more hold-time recommendations, selectively determining, based on one or more selection rules, one or more selected hold-time values of the one or more hold-time recommendations. The method further can include implementing the one or more selected hold-time values, as determined. The method additionally can include after implementing the one or more selected hold-time values, determining one or more effects associated with the one or more selected hold-time values. The method also can include transmitting the one or more selected hold-time values and the one or more effects to the policy update engine for retraining. Other embodiments are disclosed.

Abstract: Systems and methods for generating insights and recommendations to reduce waste in retail stores are disclosed. In some embodiments, a disclosed method includes: obtaining waste data of a plurality of stores; selecting, from the plurality of stores, at least one store based on the waste data; generating, based on the waste data and at least one machine learning model, recommendation data for the at least one store to take at least one action to reduce waste; and providing the recommendation data to the at least one store.

Abstract: A method can include upon receiving, from a policy update engine, one or more hold-time recommendations, selectively determining, based on one or more selection rules, one or more selected hold-time values of the one or more hold-time recommendations. The method further can include implementing the one or more selected hold-time values, as determined. The method additionally can include after implementing the one or more selected hold-time values, determining one or more effects associated with the one or more selected hold-time values. The method also can include transmitting the one or more selected hold-time values and the one or more effects to the policy update engine for retraining. Other embodiments are disclosed.

Abstract: Examples provide a multi-stage cluster component that performs a multi-stage clustering analysis on a plurality of items in a category associated with a selected item using a set of interrelationship factors. The multi-stage cluster component generates a cluster of non-substitute item-pairs, a cluster of traditional substitute item-pairs, and a cluster of variety item-pairs. The set of interrelationship factors includes at least one of measure of association, brand similarity, pack-size similarity, demographic similarity, item description similarity, lift, and/or percentage same-basket variable. A propensity score is generated for each item-pair. The propensity score is utilized to identify traditional substitute items and variety substitute items. Each substitute item is ranked based on the generated propensity score. The ranking is used to identify potential low-performance items for removal from inventory.

Abstract: Examples provide demand transference modeling for item assortment management. A demand prediction component analyzes item attribute data using a demand transference model to calculate a magnitude of demand transfer between items in a set of substitute items associated with a proposed item assortment. The proposed item assortment includes at least one assortment change. The assortment change includes a set of one or more items to be added to a current item assortment and/or a set of one or more items to be removed from the current item assortment. The demand prediction component generates a demand transference result including the calculated magnitude of demand transfer for each item in the set of substitute items and/or a predicted walk-off rate associated with lost demand. An assortment recommendation component generates an accept recommendation and/or a reject recommendation based on the demand transference result, the predicted walk-off rate, and/or a demand transference score.

Abstract: Examples provide a multi-stage cluster component that performs a multi-stage clustering analysis on a plurality of items in a category associated with a selected item using a set of interrelationship factors. The multi-stage cluster component generates a cluster of non-substitute item-pairs, a cluster of traditional substitute item-pairs, and a cluster of variety item-pairs. The set of interrelationship factors includes at least one of measure of association, brand similarity, pack-size similarity, demographic similarity, item description similarity, lift, and/or percentage same-basket variable. A propensity score is generated for each item-pair. The propensity score is utilized to identify traditional substitute items and variety substitute items. Each substitute item is ranked based on the generated propensity score. The ranking is used to identify potential low-performance items for removal from inventory.