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 associated with determining a quantity of an item to inventory may be described. In particular, statistical data about variables associated with the item may be accessed. The statistical data may be generated based on historical realizations of the variables. The quantity may be computed based on this data. For example, a plurality of regrets corresponding to inventorying a plurality of quantities of the item may be generated based on the statistical data. The quantity may be determined from the plurality of quantities as one corresponding to an acceptable regret from the plurality of regrets.

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 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 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: Methods are disclosed that teach two simulators used with demand response management systems. The first simulator generates load patterns from historical customer consumption data and generates customer loads from the generated load patterns. The second simulator generates customer response to changing electricity prices using an econometric characterization of the customer response.

Abstract: Methods are disclosed that teach two simulators used with demand response management systems. The first simulator generates load patterns from historical customer consumption data and generates customer loads from the generated load patterns. The second simulator generates customer response to changing electricity prices using an econometric characterization of the customer response.