Abstract: A computer-implemented forecasting method is disclosed. The method may include collecting a plurality of historical demand streams. Each historical demand stream may include a time series of historical demand data for a target item at a product distribution node in a supply chain. The method may also include constructing a model tree for the plurality of historical demand streams based on a plurality of forecasting models. The method may further include forecasting a future demand stream of a target item to be distributed by a product distribution node by using a forecasting model that is associated with a specific subgroup that includes a historical demand stream of the target item at the product distribution node.

Abstract: The present disclosure is directed to a computer-implemented method for designing a supply chain for multiple objectives based on a supply chain model. The method comprises defining a plurality of objectives for the supply chain model. The supply chain model includes a plurality of edges connecting one or more demands with one or more supplies. The method further includes selecting a first one of the plurality of objectives and at least a second one of the plurality of objectives and configuring, using a controller, the supply chain model for the first objective. The method further comprises identifying, using the controller, a subset of the plurality of edges based on the supply chain model configured for the first objective and relationships between the plurality of edges and the at least second objective. The method further comprises operating the supply chain according to the identified subset of the plurality of edges.

Abstract: System and methods are provided for applying a dimensionality reduction process to a data set. The method includes obtaining a data set including a first set of variables and identifying collections of the first set of variables for replacement by a second set of variables. The method also includes replacing the collections of the first set of variables with the second set of variables and eliminating the first set of variables from the data set.

Abstract: System and methods are provided for applying a dimensionality reduction process to a data set. The method includes obtaining a data set including a first set of variables and identifying collections of the first set of variables for replacement by a second set of variables. The method also includes replacing the collections of the first set of variables with the second set of variables and eliminating the first set of variables from the data set.

Abstract: A method for simplifying a mathematical model is disclosed. The method obtains a data set and identifies a plurality of variables within the data set. The method also performs a clustering analysis by dividing the data set into groups, where each group has a cluster center. The method further replaces the plurality of variables with a plurality of cluster distances. The method also uses the plurality of cluster distances as a plurality of independent variables in a model creation process.