Abstract: Product provisioning and recommender systems and methods are described for generating product-based recommendations for geographically distributed physical stores based on mobile device movement. In various embodiments, a server aggregates mobile device movement data originating from a plurality of mobile devices geographically distributed with respect to a physical store of a geographic trade area. A provisioning application (app) executing on the server and analyzing the mobile device movement data, assigns geographic region descriptors defining a geographical consumer region for each mobile device of the plurality of mobile devices. The provisioning app generates a network store graph model based on a set of regional probabilities and a set of trade area probabilities.

Abstract: Product provisioning and recommender systems and methods are described for generating product-based recommendations for geographically distributed physical stores based on mobile device movement. In various embodiments, a server aggregates mobile device movement data originating from a plurality of mobile devices geographically distributed with respect to a physical store of a geographic trade area. A provisioning application (app) executing on the server and analyzing the mobile device movement data, assigns geographic region descriptors defining a geographical consumer region for each mobile device of the plurality of mobile devices. The provisioning app generates a network store graph model based on a set of regional probabilities and a set of trade area probabilities.

Abstract: Included are embodiments for creating a forecast utilizing an ensemble forecast model. These embodiments include receiving a selection of a plurality of model families to utilize for forecasting, receiving a selection of a plurality of models to utilize for forecasting, and determining a variable of each of the plurality of models. Some embodiments include substantially simultaneously optimizing the variable of each of the plurality of models, combining each of the plurality of models into an ensemble model, the ensemble model comprising a plurality of ensemble model variables, and weighting each of the plurality of models according to a predetermined criterion. Still some embodiments may be configured to optimize the plurality of ensemble model variables and run the ensemble model to create a forecast.

Abstract: Multiple product, multiple step optimization methods useful for manufacturing products. The optimization methods comprise defining a first group and a second group. The first and second groups each comprise at least two subgroups. One or more of the subgroups for each group comprises one or more variables and one or more constraints. The variables are optimized to obtain final values for the variables based on a defined objective subject to the constraints.

Abstract: Multiple product, multiple step optimization methods useful for manufacturing products. The optimization methods comprise defining a first group and a second group. The first and second groups each comprise at least two subgroups. One or more of the subgroups for each group comprises one or more variables and one or more constraints. The variables are optimized to obtain final values for the variables based on a defined objective subject to the constraints.