NON-DELIBERATE SHRINK PREVENTION WITH PRESCRIPTIVE RECOMMENDATIONS

NON-DELIBERATE SHRINK PREVENTION WITH PRESCRIPTIVE RECOMMENDATIONS

Features attributable to non-deliberate cashier shrink are identified within a given store's historical transaction data. A machine-learning model is trained on the features to predict shrink events over a future interval of time. Each prediction is also associated with a specific prescriptive...

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Bibliographische Detailangaben
Hauptverfasser: Marom, Shay, Laserson, Itamar David
Format: Patent
Sprache:eng
Schlagworte:
CALCULATING
COMPUTING
COUNTING
DATA PROCESSING SYSTEMS OR METHODS, SPECIALLY ADAPTED FORADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL, SUPERVISORYOR FORECASTING PURPOSES
PHYSICS
SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE,COMMERCIAL, FINANCIAL, MANAGERIAL, SUPERVISORY OR FORECASTINGPURPOSES, NOT OTHERWISE PROVIDED FOR
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Beschreibung
Zusammenfassung:Features attributable to non-deliberate cashier shrink are identified within a given store's historical transaction data. A machine-learning model is trained on the features to predict shrink events over a future interval of time. Each prediction is also associated with a specific prescriptive recommendation, which if followed, eliminates or otherwise mitigates the likelihood that the predicted shrink event occurs during the corresponding time interval. Each prediction can be specific to a given cashier for the future interval of time. The predictions and corresponding prescriptive recommendations can be provided to store managers in advance of a start of the future interval of time, which allows the manager to follow the prescriptive recommendations and potentially avoid the shrink events altogether.