An online system manages the availability schedules of fulfillment agents utilizing a favorite order prediction model to predict likelihood of receiving a favorite order. The system receives a request from a fulfillment agent to set an availability schedule for a forthcoming time period. The system applies a prediction model to each of a plurality of discretized time slots of the time period to predict the favorite order likelihood. The model may be trained by the system: retrieving a profile for the fulfillment agent comprising a list of requesting user(s) that have favorited the fulfillment agent, and training the model based on order histories of the list of requesting user(s). The system generates and provides an interface displaying the time slots with a visual indication for each time slot based on its predicted likelihood, e.g., a heat map of likelihoods of receiving a favorite order across the time slots.

Systems and methods for allocating optimal supply chain resources based on order data are disclosed. In some embodiments, a disclosed method includes: receiving, from a computing device, an allocation request associated with an order; determining a customer node associated with the order, wherein the customer node is in a supply chain network; determining, based on the order and the customer node, a sub-network of the supply chain network; determining, based on cost information associated with the sub-network, at least one path from at least one source node to the customer node in the sub-network; generating supply chain allocation data for the order based on the at least one path; and transmitting the supply chain allocation data to the computing device.

The embodiment of the present disclosure provides an order information management method, device, and system, an electronic apparatus and a storage medium, the method includes: in response to obtaining a message of a new order of a target user on at least one first-type information platform, synchronizing order information of the new order to a second-type information platform of the target user; in response to obtaining an order status update message of the new order, synchronizing updated order status information of the new order from a message source terminal on the first-type information platform and the second-type information platform to a non-message source terminal, so as to complete synchronous management of the order information, the first-type information platform and the second-type information platform are different order information sources and different information management platforms of the target user, the second-type information platform is a main order information summary management platform.

A robotic pack station includes a work cell that includes a robotic arm with an end effector coupled thereto and a plurality of pick sites radially disposed about the robotic arm. Each pick site includes packages arranged in a stack. An image capture system is configured to capture images of the pick sites and a container conveyor is coupled to the work cell and configured to move one or more containers into the work cell. An industrial control server in communication with the robotic arm and with the image capture system is configured to control movement of the robotic arm and actuation of the end effector to transfer selected packages from one of the plurality of pick sites to one of the containers conveyed into the work cell, based on the images.

A computing system is disclosed for generating fulfillment-ready coordination sessions based on tokenized item data and user-submitted participation parameters. The system ingests structured, semi-structured, or unstructured item data from merchant sources and applies schema-aligned transformation logic to normalize the data into structured item representations. The normalized records are tokenized into machine-readable item tokens that encode fulfillment constraints and canonical attributes. The system receives user item selections and associated participation parameters, encodes the item tokens and participation data into structured vector embeddings, and applies compatibility scoring logic using vector comparison and rule-based threshold evaluation. Compatibility scores are evaluated against session eligibility constraints derived from the item tokens. When eligibility conditions are met, the system generates a coordination session payload comprising match outcomes, proportional pricing, and fulfillment metadata, and issues orchestration instructions to external fulfillment systems for group-based delivery or preparation execution.

User input(s) indicative of a request to create a first storage compartment for an intelligent storage rack are obtained. The intelligent storage rack comprises physical storage space, and the first storage compartment comprises a representation of a portion of the physical storage space. Images captured from camera devices installed to the intelligent storage rack are received. Each of the images depicts the physical storage space from differing perspectives. Responsive to a second user input that selects a first image, the first image is processed with a machine-learned model to generate a predicted region of interest (ROI), wherein the predicted region of interest comprises a visual representation of the first storage compartment. A first data object is stored to a data structure associated with the intelligent storage rack descriptive of the predicted ROI, wherein the first data object associates the predicted ROI to the first storage compartment.

A system for, and method of, remotely operating one or more vehicles in one or more inventory management facilities including one or more assets. The system and method include vehicles configured to move one or more assets in one or more inventory management facilities from a first location to a second location and a control station in communication with the one or more vehicles. The control station is remote from the one or more vehicles and communicates at least one operational command to the one or more vehicles to operate the one or more vehicles to perform one or more assigned tasks and/or operational workflows for the one or more vehicles.

Disclosed herein are systems and methods for the automated ingestion and processing of orders for the food supply chain industry using artificial intelligence. An example method can comprise extracting order level information and item level information from a purchase order in the form of at least one of an email message, a text message, a voicemail audio file, an image file, a spreadsheet file, and a portable document format (PDF) file. The method can also comprise preprocessing the order level information and the item level information into a plurality of machine learning features and inputting the machine learning features into a machine learning model to obtain predictions concerning the purchase order. A sales order can then be generated based in part on the predictions outputted by the machine learning model.