Abstract: A technology for managing space utilization using self-adjusting inventory slots is described. In an example embodiment, a system comprises a shelving unit including a shelf, and a shelf adjustment system including an electronic display strip extending parallel with the shelf of the shelving unit. The electronic display strip may display a first visual representation of a first adjustable inventory slot horizontally adjacent to a second visual representation of a second adjustable inventory slot, the first visual representation indicating a first portion of the shelf as the first adjustable inventory slot for a first item, and the second visual representation indicating a second portion of the shelf as the second adjustable inventory slot for a second item.

Abstract: A method determines a processing cluster including one or more stock keeping units (SKUs); divides the processing cluster into a first cluster and a second cluster based on SKU affinities between the one or more SKUs in the processing cluster; determines a first SKU of the first cluster to be replicated to the second cluster based on a demand correlation between the first SKU of the first cluster and a second SKU of the second cluster; replicates the first SKU of the first cluster to the second cluster; responsive to replicating the first SKU of the first cluster to the second cluster, determines whether the first cluster and the second cluster satisfy a defined constraint; and responsive to determining that the first cluster and the second cluster satisfy the defined constraint, assigns the first cluster to a first physical location and assigning the second cluster to a second physical location.

Abstract: In an example embodiment, a method determines a container containing one or more items associated with one or more item types, the container located at a current position in a storage facility; determines one or more order likelihoods of the one or more item types contained in the container; determines a container utilization likelihood of the container based on the one or more order likelihoods of the one or more item types contained in the container; determines an optimal position for the container in the storage facility based on the container utilization likelihood of the container, the optimal position being different from the current position of the container; and instructing an automated guided vehicle (AGV) to transport the container from the current position of the container to the optimal position of the container in the storage facility.

Abstract: A hybrid modular storage fetching system is described. In an example implementation, an automated guided vehicle of the hybrid modular storage fetching system includes a drive unit that provides motive force to propel the automated guided vehicle within an operating environment. The automated guided vehicle may also include a container handling mechanism including an extender and a carrying surface, the container handling mechanism having three or more degrees of freedom to move the carrying surface along three or more axes. The container handling mechanism may retrieve an item from a first target shelving unit using the carrying surface and the three or more degrees of freedom and place the item on a second target shelving unit. The automated guided vehicle may also include a power source coupled to provide power to the drive unit and the container handling mechanism.

Abstract: A hybrid modular storage fetching system is described. In an example implementation, the system may include a warehouse execution system adapted to generate a picking schedule for picking pick-to-cart and high-density storage items, and an AGV dispatching system adapted to dispatch a cart automated guided vehicle and a modular storage fetching automated guided vehicle based on the picking schedule. The cart automated guided vehicle may be adapted autonomously transport a carton through a pick-to-cart area and to a pick-cell station. The modular storage fetching automated guided vehicle may be adapted to synchronously autonomously transport a modular storage unit containing items to be placed in the cartons from a high-density storage area to the pick-cell station.

Abstract: A hybrid modular storage fetching system is described. In an example implementation, the system may include a warehouse execution system adapted to generate a picking schedule for picking pick-to-cart and high-density storage items, and an AGV dispatching system adapted to dispatch a cart automated guided vehicle and a modular storage fetching automated guided vehicle based on the picking schedule. The cart automated guided vehicle may be adapted autonomously transport a carton through a pick-to-cart area and to a pick-cell station. The modular storage fetching automated guided vehicle may be adapted to synchronously autonomously transport a modular storage unit containing items to be placed in the cartons from a high-density storage area to the pick-cell station.

Abstract: A system and method for automated preparation of deliveries in delivery vehicles using automated guided vehicles is described. In an example implementation, the method may determine a future delivery in a set of deliveries assigned to a delivery vehicle, the future delivery including a first item and determine a storage location in the delivery vehicle of the first item. In some implementations, an AGV may, during transit of the delivery vehicle along a route for delivering the set of deliveries, navigate to a point proximate to the storage location of the first item in the delivery vehicle, and retrieve the first item from the storage location in the delivery vehicle. The AGV may then deliver the first item to a delivery point.

Abstract: A system and method for automated loading of delivery vehicles using automated guided vehicles is described. In an example implementation, a loading coordination engine may determine a delivery destination of a first item based on item data associated with the first item, assign the first item to a location in a delivery vehicle, and generate a task list including an instruction to an automated guided vehicle to position the first item in the delivery vehicle based on the assigned location in the delivery vehicle. In some instances, the automated guided vehicle may navigate to a loading area to retrieve the first item from the loading area, navigate to a point proximate to the assigned location in the delivery vehicle, and place the first item at the assigned location based on the task list.

Abstract: A hybrid modular storage fetching system with a robot execution system (REX) is described. In an example implementation, a REX may induct, into the hybrid modular storage fetching system, an order identifying items to be fulfilled by automated guided vehicles (AGVs) at an order fulfillment facility. The REX may generate at task list including tasks for a first and second AGV, instruct the first AGV to retrieve a first item in the order from a first storage area based on the task list and deliver the first item to a pick-cell station. The REX may also instruct the second AGV to retrieve a second item of the order from a second storage area and deliver the second item to the pick-cell station. The REX may communicate with other components of the hybrid modular storage fetching system to coordinate the paths of the AGVs to fulfill the order.

Abstract: A hybrid modular storage fetching system is described. In an example implementation, an automated guided vehicle of the hybrid modular storage fetching system includes a drive unit that provides motive force to propel the automated guided vehicle within an operating environment. The automated guided vehicle may also include a container handling mechanism including an extender and a carrying surface, the container handling mechanism having three or more degrees of freedom to move the carrying surface along three or more axes. The container handling mechanism may retrieve an item from a first target shelving unit using the carrying surface and the three or more degrees of freedom and place the item on a second target shelving unit. The automated guided vehicle may also include a power source coupled to provide power to the drive unit and the container handling mechanism.

Abstract: A hybrid modular storage fetching system with a robot execution system (REX) is described. In an example implementation, a REX may induct, into the hybrid modular storage fetching system, an order identifying items to be fulfilled by automated guided vehicles (AGVs) at an order fulfillment facility. The REX may generate at task list including tasks for a first and second AGV, instruct the first AGV to retrieve a first item in the order from a first storage area based on the task list and deliver the first item to a pick-cell station. The REX may also instruct the second AGV to retrieve a second item of the order from a second storage area and deliver the second item to the pick-cell station. The REX may communicate with other components of the hybrid modular storage fetching system to coordinate the paths of the AGVs to fulfill the order.

Abstract: In an example embodiment, a method determines a container containing one or more items associated with one or more item types, the container located at a current position in a storage facility; determines one or more order likelihoods of the one or more item types contained in the container; determines a container utilization likelihood of the container based on the one or more order likelihoods of the one or more item types contained in the container; determines an optimal position for the container in the storage facility based on the container utilization likelihood of the container, the optimal position being different from the current position of the container; and instructing an automated guided vehicle (AGV) to transport the container from the current position of the container to the optimal position of the container in the storage facility.

Abstract: A technology for virtualized container opening and optimized item placement, and in an example includes a receiving, from an augmented reality device, a first image of user view capturing a storing surface of a storage space; receiving an input identifying a first item; retrieving item data associated with the first item based on the input; determining, from the one or more placement regions, a set of one or more viable placement regions for placing the first item on the storing surface based on an arrangement model and the item data associated with the first item; generating overlay placement data describing the set of one or more viable placement regions; transmitting the overlay placement data to the augmented reality device; and instructing the augmented reality device to superimpose one or more virtual items in the storage space in the user view based on the overlay placement data.

Abstract: A method determines a processing cluster including one or more stock keeping units (SKUs); divides the processing cluster into a first cluster and a second cluster based on SKU affinities between the one or more SKUs in the processing cluster; determines a first SKU of the first cluster to be replicated to the second cluster based on a demand correlation between the first SKU of the first cluster and a second SKU of the second cluster; replicates the first SKU of the first cluster to the second cluster; responsive to replicating the first SKU of the first cluster to the second cluster, determines whether the first cluster and the second cluster satisfy a defined constraint; and responsive to determining that the first cluster and the second cluster satisfy the defined constraint, assigns the first cluster to a first physical location and assigning the second cluster to a second physical location.

Abstract: Technology for optimizing carton induction in an order fulfillment picking system is described. In an example embodiment, a method, implemented using one or more computing devices, may include receiving scan data reflecting status of cartons being conveyed by a conveying system, receiving confirmatory input reflecting pick completion for a subset of the cartons being conveyed by the conveying system, and generating an estimated time of arrival for one or more cartons not yet inducted into the conveying system based on the scan data and the confirmatory input. The method may further include generating a load forecast based on the estimated time of arrival and inducting one or more of the one or more cartons into the conveying system based on the load forecast.

Abstract: A technology for virtualized container opening and optimized item placement, and in an example includes a receiving, from an augmented reality device, a first image of user view capturing a storing surface of a storage space; receiving an input identifying a first item; retrieving item data associated with the first item based on the input; determining, from the one or more placement regions, a set of one or more viable placement regions for placing the first item on the storing surface based on an arrangement model and the item data associated with the first item; generating overlay placement data describing the set of one or more viable placement regions; transmitting the overlay placement data to the augmented reality device; and instructing the augmented reality device to superimpose one or more virtual items in the storage space in the user view based on the overlay placement data.

Abstract: An example system may comprise a docking station remotely located from a fulfillment center, a fulfillment system, an unmanned aerial vehicle (UAV), and an autonomous ground vehicle (AGV). The fulfillment system manages the fulfillment center and the docking station, receives a request to ship an item, determines an item transfer point based on a delivery point, and calculates a flight path to the item transfer point. The fulfillment system loads the item at the fulfillment center via a handling mechanism of the UAV and deploys the UAV to the item transfer point using the flight path. The AGV is coupled for wireless communication with the fulfillment system, the docking station, and the AGV. The item transfer point indicates a geographical location where the item is to be transferred from the UAV to one of the docking station and the AGV based on the delivery point.

Abstract: A technology for managing space utilization using self-adjusting inventory slots is described. In an example embodiment, a system comprises a shelving unit including a shelf, and a shelf adjustment system including an electronic display strip extending parallel with the shelf of the shelving unit. The electronic display strip may display a first visual representation of a first adjustable inventory slot horizontally adjacent to a second visual representation of a second adjustable inventory slot, the first visual representation indicating a first portion of the shelf as the first adjustable inventory slot for a first item, and the second visual representation indicating a second portion of the shelf as the second adjustable inventory slot for a second item.

Abstract: In an example embodiment, systems and methods are described for demand prediction and profitability modeling based on heterogeneous data and blended clustering models. Data for a plurality of items is received and differentiated into a first set of good items and a second set of bad items. Good items and bad items may be indicated by a threshold for a prediction accuracy metric, such as weighted Mean Average Percentage Error (MAPE). A first model for predicted demand levels of the good items is generated that excludes cross-cluster effects with the bad items. A second model of the bad items is generated that includes a residual correction and cross-cluster effects with the good items. A predicted demand of a particular item is generated based on a cluster-level regression model and at least one of the first model and the second model.

Abstract: Technology for optimizing carton induction in an order fulfillment picking system is described. In an example embodiment, a method, implemented using one or more computing devices, may include receiving scan data reflecting status of cartons being conveyed by a conveying system, receiving confirmatory input reflecting pick completion for a subset of the cartons being conveyed by the conveying system, and generating an estimated time of arrival for one or more cartons not yet inducted into the conveying system based on the scan data and the confirmatory input. The method may further include generating a load forecast based on the estimated time of arrival and inducting one or more of the one or more cartons into the conveying system based on the load forecast.