A personalized garment and clothing design system and a personalized garment and clothing design process are disclosed. The personalized garment and clothing design system provides a user-accessible, interactive website that is configured to allow a user to design personalized garments and clothes. The personalized garment and clothing design system hosts a website that is interactive and provides several personalized garment and clothing design web user interface (UI) tools through which the user can provide personalized body type and sizing information. The user can utilize the UI tools to customize and personalize styles, fabrics, trims, sizing, colors, patterns, clothing and garment features, etc. The user can also interact with a customized digital mannequin to view personalized garment and clothing designs in progress. When finished, the garment or clothing design is physically produced and sent to the user to wear or start their own line and sell their custom designed garments.

A system is provided for netting obligations between entities of a cycle of obligations. Each obligation specifying a quantity of an item that a from-entity of the cycle is obligated to provide to a to-entity of the cycle. For each entity, the system determines whether the sum of its inventory of the item and the quantity of the obligation on which it is a to-entity is sufficient to satisfy the obligation on which it is a from-entity. When sufficient, the system identifies one or more netting transactions to settle the obligations of the cycle. The system then directs the execution of the netting transactions and settlement transactions to effect and record the settling of the obligations.

This disclosure discloses an order reviewing method, device and system and a rack, and relates to the field of warehousing. The method includes determining an order number of an order to which a commodity belongs; prompting a location on a rack corresponding to the commodity according to a correspondence between an order number and a location number of a location; in response to the commodity being placed on the location, determining whether the order has been reviewed; and if the order has been reviewed, sending a flipping instruction to a flipping system corresponding to the order according to a correspondence between an order and a device number of a flipping system, wherein a flip assembly of each flipping system corresponds to one location and is configured to close an inlet of the one location.

This disclosure discloses an order reviewing method, device and system and a rack, and relates to the field of warehousing. The method includes determining an order number of an order to which a commodity belongs; prompting a location on a rack corresponding to the commodity according to a correspondence between an order number and a location number of a location; in response to the commodity being placed on the location, determining whether the order has been reviewed; and if the order has been reviewed, sending a flipping instruction to a flipping system corresponding to the order according to a correspondence between an order and a device number of a flipping system, wherein a flip assembly of each flipping system corresponds to one location and is configured to close an inlet of the one location.

An online concierge system displays an ordering interface to users that displays items offered by various warehouses. The online concierge system includes machine learning availability model that estimates an item's availability and visually distinguishes items offered by a warehouse having less than a threshold availability from other items. Because information from a warehouse that an item that was out of stock is now in stock is often delayed, the online concierge system transmits a request to a shopper fulfilling an order to check for an item's availability at a warehouse. For example, the online concierge system allows users to include a request for an indication of an item's availability when placing an order. When the online concierge system receives a threshold number of requests for the item, the online concierge system prompts a shopper fulfilling an order including items near the item for the item's availability.

An online concierge system displays an ordering interface to users that displays items offered by various warehouses. The online concierge system includes machine learning availability model that estimates an item's availability and visually distinguishes items offered by a warehouse having less than a threshold availability from other items. Because information from a warehouse that an item that was out of stock is now in stock is often delayed, the online concierge system transmits a request to a shopper fulfilling an order to check for an item's availability at a warehouse. For example, the online concierge system allows users to include a request for an indication of an item's availability when placing an order. When the online concierge system receives a threshold number of requests for the item, the online concierge system prompts a shopper fulfilling an order including items near the item for the item's availability.

A computer system performs automated inventory management. A service level is determined, using a predictive model, for a count of inventory units for one or more sites of a plurality of sites, wherein the predictive model is provided with a reorder point and reorder quantity as input. In response to determining that the service level does not satisfy a threshold value, the reorder point is adjusted. The adjusted reorder point is determined to yield a service level, according to the predictive model, that satisfies the threshold value. Instructions are automatically transmitted to cause a reorder of inventory units in response to determining that a count of inventory units is below the adjusted reorder point. Embodiments of the present invention further include a method and program product for performing automated inventory management in substantially the same manner described above.

A computer system performs automated inventory management. A service level is determined, using a predictive model, for a count of inventory units for one or more sites of a plurality of sites, wherein the predictive model is provided with a reorder point and reorder quantity as input. In response to determining that the service level does not satisfy a threshold value, the reorder point is adjusted. The adjusted reorder point is determined to yield a service level, according to the predictive model, that satisfies the threshold value. Instructions are automatically transmitted to cause a reorder of inventory units in response to determining that a count of inventory units is below the adjusted reorder point. Embodiments of the present invention further include a method and program product for performing automated inventory management in substantially the same manner described above.

A warehouse picking system and a warehouse picking method are provided. The warehouse picking system includes multiple cargo management stations and a management device. The management device receives multiple orders including at least one cargo, calculates an order score of each order by using an order score function; selects at least one order as a target order according to the order scores so as to determine a target cargo management station adapted for processing the target order; selects a cargo picking box storing at least one cargo in the target order from the cargo picking boxes of the target cargo management station to serve as candidate cargo picking boxes; and determines a target cargo picking box adapted for picking the cargo in the target order according to a workload of cargo picking equipment, a workload of cargo transportation equipment, and a disposition of each candidate cargo picking box.

A warehouse picking system and a warehouse picking method are provided. The warehouse picking system includes multiple cargo management stations and a management device. The management device receives multiple orders including at least one cargo, calculates an order score of each order by using an order score function; selects at least one order as a target order according to the order scores so as to determine a target cargo management station adapted for processing the target order; selects a cargo picking box storing at least one cargo in the target order from the cargo picking boxes of the target cargo management station to serve as candidate cargo picking boxes; and determines a target cargo picking box adapted for picking the cargo in the target order according to a workload of cargo picking equipment, a workload of cargo transportation equipment, and a disposition of each candidate cargo picking box.