Abstract: A system and method for controlling an automated process based on a time series of count data. The approach includes characterizing an observed time series of counts by modeling the underlying dynamics that give rise to the observed data, including evaluating the time series with a driver estimation process, wherein the driver estimation process identifies a model by an iterative process that includes: providing a probability model; and utilizing a combined MCMC and EM algorithm to determine a set of model parameters for the time series; outputting an estimated sequence of driver values based on the identified model; outputting a series of statistically plausible future counts, and controlling the automated process based on the estimated sequence of driver values.

Abstract: A system and method are presented for processing demand data for a set of resources in a technology platform. A method is provided that includes collecting demand profiles for the set of resources; reformatting each demand profile into a cumulative demand plot; calculating a distance metric for each pair of cumulative demand plots based on an area between the pair of cumulative demand plots; clustering the resources into a set of clusters based on calculated distance metrics; and generating a characterization for each of the clusters to facilitate management or control of the technology platform.

Abstract: A system and method are presented for optimizing choices of control parameters.

Abstract: A system, method and program product for cluster-based forecasting of intermittent demand. A computer system is disclosed for forecasting intermittent demand, having a data management system that provides access to historical demand data for a plurality of items; and a forecast system that generates a distribution of lead time demand predictions for a selected item having intermittent demand, and wherein the forecast system includes program code for performing the steps of: identifying a cluster from historical demand data, wherein the cluster includes items having an aggregated demand that defines a cluster driver; detecting if an association exists between historical demand data of a selected item and the cluster driver; and if the association is detected, utilizing the historical demand data of the selected item and the historical demand data to generate a distribution of lead time demand predictions.

Abstract: A system and method for forecasting intermittent demand. The forecasting technique utilizes sample reuse techniques to build a distribution of predicted cumulative lead time demand values that can be analyzed using statistical methods, be used as input for an inventory control system, be used as input for a sales planning system, etc.