Walmart Apollo, LLC

Jeffrey Kerbs

Greg A Bryan

Cristy Crane Brooks

David Blair Brightwell

Jesse Lee Eaton, Jr

Benjamin D Enssle

Matthew Allen Jones

Antonio Villagomez

Katherine A Worley

Ryan Kee

Greg Bryan

Jesse Lee Eaton

Matthew Jones

Examples provide a fruit ripening rack having a plurality of chambers for on-site produce ripening at a retail location. A controller component generates predicted demand for ripened fruit on a selected date at a selected location based on transaction history data and/or seasonal demand. The controller component calculates the quantity of fruit based on the predicted demand. The controller component determines conditions within each chamber to accelerate ripening of the calculated quantity of one or more types of fruit to one or more selected levels of ripeness on the selected date. The conditions include amount of ethylene gas in each chamber, length of exposure to the ethylene gas, temperature inside the chamber and/or humidity level inside the chamber. The fruit ripening rack controls internal conditions within each chamber to provide variable fruit ripening in accordance with per-chamber configurations to satisfy predicted daily demand at the selected location.

Predictive demand-based produce ripening system

Examples provide a system for cold-chain compliant item selection. A set of perishable items are selected for retrieval by a user based on item data, cold-chain compliance score(s), and/or cold-chain compliance restrictions associated with each user. The selected set of items are assigned to the user for retrieval in accordance with a set of cold-chain compliance parameters, including a per-item maximum dwell-time specifying a maximum time interval between removal of a perishable item from a temperature-controlled display area and receipt of the perishable item at an intake area. A dwell-time is calculated for an item received at an intake area. The item is accepted if the per-item maximum dwell-time exceeds the calculated dwell-time and the item is rejected if the calculated dwell-time exceeds the per-item maximum dwell-time. An incentive is provided to the identified user based on a cold-chain compliance score for the user.

System for crowdsourced cold-chain compliant item selection

In some embodiments, apparatuses and methods are provided herein useful to baling materials. In some embodiments, there is provided a system for baling recyclable materials including a baling a machine configured to compress materials into a shape suitable for at least one of storage and transportation; the materials comprising mixed type recyclable materials, the material not comprising added cardboard header materials configured to structurally enhanced the shape having been compressed; baling wire configured to twine and secure the materials compressed by the baling machine resulting in a bale comprising twined and compressed materials; and shrink film configured to wrap around the bale.

Systems and methods for baling materials

Systems, methods, and machine readable media are provided for image-based replenishment. In exemplary embodiments, a system periodically obtains an image from one or more imaging devices in a facility of a selected rack of items among a group of racks. The system determines, based on the image, a first volume indicative of a volume of items on the rack. The system retrieves a sales forecast for the items on the selected rack and determines an expected second volume of items on the rack based on the sales forecast for the item. The system then determines whether the first volume is within a predetermined threshold of the second volume, and transmits instructions based on whether or not the first volume is determined to be within the predetermined threshold of the second volume.

System and method for image-based replenishment

Examples provide a system for cold-chain compliant item selection. A set of perishable items are selected for retrieval by a user based on item data, cold-chain compliance score(s), and/or cold-chain compliance restrictions associated with each user. The selected set of items are assigned to the user for retrieval in accordance with a set of cold-chain compliance parameters, including a per-item maximum dwell-time specifying a maximum time interval between removal of a perishable item from a temperature controlled display area and receipt of the perishable item at an intake area. A dwell-time is calculated for an item received at an intake area. The item is accepted if the per-item maximum dwell-time exceeds the calculated dwell-time and the item is rejected if the calculated dwell-time exceeds the per-item maximum dwell-time. An incentive is provided to the identified user based on a cold-chain compliance score for the user.

Systems, devices, and methods include an electronic scanning device, an electronic database, and an electronic inventory management device for analyzing products at a retail store in order to detect products at the retail store that are associated with mis-shipments and to adjust perpetual inventory at the retail store in view of a detected mis-shipment.

Systems and methods for identifying and remedying product mis-shipments to retail stores

Examples provide a perpetual inventory (PI) reconciliation system. A PI controller analyzes item data using a set of PI purge criteria and a set of weighted prioritization variables to select a highest priority seasonal inventory item having a positive PI value a per-item threshold time after an end-of-season (EOS) date for PI purge analysis. A PI controller determines if the selected item is a high confidence item or a low confidence item based on a set of confidence rules and item data. If the selected item is a high confidence item, a PI purge component sets an inventory value for the selected item to zero to eliminate the positive PI. If the selected item is a low confidence item, a verification component verifies the number of physical instances of the selected item. The inventory value is updated using the verified number of physical instances.

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Bella Vista, AR, United States of America

Jeffrey Kerbs