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In this paper a demand time series is analysed to support Make-To-Stock (MTS)and Make-To-Order (MTO) production decisions. Using a purely MTS productionstrategy based on the given demand can lead to unnecessarily high inventorylevels thus it is necessary to identify likely MTO episodes.  This research proposes a novel outlier detection algorithm based on specialdensity measures. We divide the time series' histogram into three clusters. Onewith frequent-low volume covers MTS items whilst a second accounts for highvolumes which is dedicated to MTO items. The third cluster resides between theprevious two with its elements being assigned to either the MTO or MTS class.The algorithm can be applied to a variety of time series such as stationary andnon-stationary ones.  We use empirical data from manufacturing to study the extent of inventorysavings. The percentage of MTO items is reflected in the inventory savingswhich were shown to be an average of 18.1%.

Splitting hybrid Make-To-Order and Make-To-Stock demand profiles