Abstract: Features related to systems and methods for predictive estimation for network inventory planning for items for distribution through a multi-echelon network are disclosed. The problem may be modeled to account for demand for an item that may be satisfied by fulfillment centers within the network. The model considers benefits to ensuring adequate inventory to meet expected demand streams along with any savings accruing from a lower transfer and salvage costs.

Abstract: Techniques are provided herein for utilizing an inventory engine to optimize the selection of a set of items to be stored as inventory at a storage location. A candidate set of items may be identified based at least in part on a selection model. In accordance with at least one embodiment, the selection model may be based at least in part on a capacity of the storage location and a threshold time duration by which purchased items of the set of items are to be transported from the storage location to a purchaser. A plurality of probability values corresponding to the candidate set of items may be determined. An optimal set of items may be determined based at least in part on the candidate set of items and the plurality of probability values.

Abstract: Techniques for computing a feature based on variables may be provided. For example, historical realizations associated with a first variable may be accessed. The first variable may be associated with an item. Realizations of the first variable may be based on one or more factors associated with the item. Historical realization of a second variable associated with the item may also be accessed. The historical realizations of the first and second variables may be analyzed to generate an expected realization of the first variable as a function of the second variable. The feature may be computed based on the generated function.

Abstract: Techniques for generating a decision to store units of an item may be provided. The units of the item may be offered from a network-based resource. For example, a first ordering channel and a second ordering channel may be provided to facilitate orders of the item from the network-based resource. The first and second ordering channels may allow random orders and deterministic orders of the item, respectively. Additionally, a value of a look ahead window associated with the second ordering channel may be accessed. The value may shift a time for an automatic generation of a deterministic order. Further, a first loss and a second loss may be computed based on the value. The first and second losses may be associated with losing a random order and losing the deterministic order, respectively. The decision may be generated based at least in part on the first loss and the second loss.

Abstract: Techniques for managing a removal channel may be described. In an example, a first estimate may be generated. The first estimate may be associated with removing a volume of items from an inventory space within a planning horizon through a removal channel. A decision to remove the volume of items may be accessed. The decision may be based on the first estimate and a second estimate associated with adding a capacity for inventorying an equivalent volume of items. Based on the decision, a constraint may be generated and imposed on an inventory level of an item during a time period of the planning horizon. A quantity of the item to remove within the time period through the removal channel may be estimated based on the constraint. Usage of the removal channel during the time period may be set based on the quantity of the item to remove.

Abstract: Techniques for determining a decision to acquire units of an item to be inventoried may be provided. For example, a demand for an item may be simulated to determine a consumption of a capacity for inventorying the item. A discrepancy between the consumption of the capacity and the capacity may be determined. An opportunity cost associated with the capacity may be updated based at least in part on determining that the discrepancy fails a convergence criterion. The opportunity cost may indicate a value associated with using the capacity. The consumption of the capacity may be simulated based at least in part on the updated opportunity cost. A resulting discrepancy may be determined. If the resulting discrepancy meets the convergence criterion, the decision to acquire the units of the item may be generated based at least in part on the updated opportunity cost.

Abstract: Described is an apparatus which comprises: a first memory unit having an input and an output, wherein the first memory unit operates on a first power supply which is operable to be turned off; a second memory unit having an input coupled to the output of the first memory unit, and an output, wherein the second memory unit operates on a second power supply which is always on; and a control logic coupled to the first and second memory units, the control logic to provide one or more control signals to each of the first and second memory units.

Abstract: Examples disclosed hereto relate to a computing device for providing cellular network access to a plurality of mobile devices. The computing device includes a wireless wide area network (WWAN) card. The WWAN card includes a controller to enable the WWAN card as a base station. A local area network (LAN) interface is provided to establish connection to a cellular backhaul network. The LAN interface is communicatively coupled to a LAN and to the WWAN card. The LAN is communicatively coupled to the cellular backhaul network.

Abstract: Described is an apparatus which comprises: a first memory unit having an input and an output, wherein the first memory unit operates on a first power supply which is operable to be turned off; a second memory unit having an input coupled to the output of the first memory unit, and an output, wherein the second memory unit operates on a second power supply which is always on; and a control logic coupled to the first and second memory units, the control logic to provide one or more control signals to each of the first and second memory units.

Abstract: A method and system for automated corrective and predictive maintenance is described herein. In one embodiment, a failure notification associated with a tool is received, wherein the failure notification indicates one or more failure events for the tool. Maintenance data associated with the tool is obtained, wherein the maintenance data includes one or more maintenance events for the tool. An optimal schedule is created for the tool by merging the failure notification and the maintenance data.

Abstract: A method and system for automated corrective and predictive maintenance is described herein. In one embodiment, a failure notification associated with a tool is received, wherein the failure notification indicates one or more failure events for the tool. Maintenance data associated with the tool is obtained, wherein the maintenance data includes one or more maintenance events for the tool. An optimal schedule is created for the tool by merging the failure notification and the maintenance data.