Walmart Apollo, LLC

Juan P Gomez

Jingying Zhang

Seon Ki Kim

Manjunath Channappagoudar

Lokesh Kumar Sambasivan

Mohit Choudhary

Arvind Shyam Verma

Jackie L Guan

Described are methods and systems for estimating an optimal product pack size and on-shelf customer facing quantity of the products in view of shelf capacity limitations of the shelves at a given retail store/product distribution center, with the aim of maximizing the total net profit of the store/distribution center. The systems and methods obtain inventory-related, retail facility product storage capacity-related, and product storage/handling-related data from one or more databases and estimate the net profit of multiple variations of pack size and customer-facing quantity for each product. Then, these results are processed via various inventory optimization algorithms obtained from one or more databases to estimate the pack size and customer-facing quantity for each of the products to be stocked at the retail facility that would be estimated to result in maximum net profit for the retail facility within a given period of time.

Systems and methods of optimizing pack size and customer-facing quantity of retail products at retail facilities

Systems and methods of anomaly detection in time-series are disclosed. A time-series dataset is received and a set of segments is iteratively defined from the time-series dataset by identifying a set of changepoints in the time-series dataset based on a changepoint type and a sensitivity parameter, determining whether the set of segments defined by the changepoints satisfy at least one threshold criteria, and modifying the sensitivity parameter when the threshold criteria is not met or outputting the set of segments when the threshold criteria is met. A segment-specific anomaly detection threshold is determined for each segment in the set of segments and a set of anomaly-flagged segments is generated. The set of anomaly flagged segments are generated by an anomaly detection process based on the segment-specific anomaly detection threshold for a corresponding segment. An anomaly-flagged time-series is generated by combining the set of anomaly-flagged segments.

Systems and methods for automated anomaly detection in univariate time-series

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Bentonville, AR, United States of America

Juan P Gomez