Walmart Apollo, LLC

Lokesh Kumar Sambasivan

Jingying Zhang

Pushkar Pushp

Chuck E Tilmon

Manjunath Channappagoudar

Juan P Gomez

Mohit Choudhary

Abhishek Kushwaha

Ying Cao

Rajat Kumar Gupta

Sandeep Jaywant Tendulkar

Chuck S Rorie

Samrat Chowdhury

Soumabrata Arup Chakraborty

Anupa Saxena

Debdut Hazra Banerjee

Gayathri Sanjeev Somarajan

Daniel J Forsgren

Arvind Shyam Verma

Maxine Caballero Perales

Ray L Baber

Shivani S Naik

Nicholas G Van Voorst

Raghuram Sathyamurthy

Geronimo Valenzuela

Systems and methods for simulation forecasting in target networks including dynamic realignment are disclosed. A set of target nodes including at least one demand node realignment representative of a demand volume shift for at least one corresponding distribution node in a predetermined time period is received. When the demand volume shift is equal to or above a predetermined threshold, an estimated volume feature for the at least one corresponding distribution node is generated. When the demand volume shift is below the predetermined threshold, an actual volume feature for the at least one corresponding distribution node is generated. The generated one of the estimated volume feature or the actual volume feature is provided to a trained forecasting model to generate a demand forecast data structure based on the generated one of the estimated volume feature or the actual volume feature.

Systems and methods for simulation forecasting including dynamic realignment

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 and methods for assessing quality of and detecting defects in retail products include a housing having an interior, a product holding surface, an image capture device configured to capture an image of the product, a lighting element configured to provide lighting within the housing, an electronic database configured to store electronic data including reference image data associated with the product and representing the product in various sizes and various quality states, and a processor-based control circuit in communication with the first image capture device and the electronic database. The control circuit is configured to obtain image data representing the image of the product, obtain the reference image data, analyze the image data and the reference image data to identify the product and identify a size of a defect present on the product, and output a notification indicating whether or not the product is of acceptable quality.

Systems and methods for assessing quality of retail products

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Tirupati, India

Lokesh Kumar Sambasivan