Walmart Apollo, LLC

Jingying Zhang

Paridhi Kabra

Richard Ulrich

Dong Xu

Seon Ki Kim

Manjunath Channappagoudar

Kunlei Lian

Lokesh Kumar Sambasivan

Juan P Gomez

Jackie L Guan

Shuohao Wu

Arvind Shyam Verma

Ravi Shankar Karedla

Shilka Roy

Mohit Choudhary

Nitin Kapoor

Prateek Mishra

Jackie Lei Guan

Clifford Wayne Bolinger

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

Systems, methods, and computer-readable media are configured to auto-populate pharmacy SIG codes based on pattern matching model in a pharmacy system. The prescriptions including one or more fields of prescription information in natural language are received in the system. The prescription information can be parsed according to rule-based pattern matching model. The parsed prescription information can be mapped to a token SIG using rule-based pattern matching model dictionaries. A prescription SIG code based on each of the token SIG can be generated. A confidence value of the prescription SIG code based on character coverage of the prescription can be calculated.

Pharmacy SIG codes auto-populating system

Systems and methods for determining whether a particular feature or change implemented in at least one test store causes a significant change as compared to one or more control stores are discussed. More particularly, techniques for using a time-series clustering algorithm to identify comparable sister stores to a store in which a feature change is being considered are described. Once the sister stores are identified a testing module can perform an A/B testing so as to validate whether a particular feature change being implemented in the test store causes a significant change as compared to the control stores.

Systems and methods for measuring effectiveness of feature changes made within a store

This disclosure describes a graphics processing unit programmed to generate a sample path for the demand of the one or more products at a store based at least in part on data associated with a historical distribution of the variability of the demand of the one or more products. The graphics processing unit may generate a thread corresponding to a plurality scenarios. The graphics processing unit may execute the thread in parallel to determine one or more parameters for each of the plurality of scenarios for the one or more products. The graphics processing unit may select the one or more parameters generated from the execution of one of the sample paths to minimize the cost. The graphics processing unit may adjust an inventory management system to set an inventory management setting based at least in part on the selection of the one or more parameters.

Systems and methods for safety stock settings using a parallel processing computing architecture

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Beaumont, TX, United States of America

Jingying Zhang