1. A computer-implemented method for forecasting demand for an object in a managed supply chain, the computer-implemented method comprising: performing, by a computer processor, at least one forecast test on a first set of data representative of consumption of the object in the managed supply chain to identify significance levels of a plurality of forecast models to the consumption data; selecting, by the computer processor, at least one of the forecast models based on the significance levels; iteratively determining, by the computer processor, optimal forecast parameters correspondingly associated with the selected forecast models to identify an optimal combination of one of the selected forecast models and its corresponding forecast parameters, wherein the optimal combination yields a lowest forecast error; creating, by the computer processor, an optimal forecast profile comprising the optimal forecast model and the forecast parameters, the forecast parameters including the lowest forecast error as a predefined forecast error limit; assigning, by the computer processor, the optimal forecast profile to the object; and starting a forecast of the consumption of the object in accordance with the optimal forecast profile associated with the object; generating, by the computer processor, based on the optimal forecast profile, a forecast associated with a second set of data representing the consumption of the object in the managed supply chain, in response to receiving the second set of data; determining, by the computer processor, a forecast error for the forecast; accepting, by the computer processor, the forecast when the forecast error is within the predefined forecast error limit; and when the forecast error exceeds the predefined forecast error limit, automatically reselecting a different forecast model and different forecast parameters.