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Spare part inventory will be forecasted at higher level if historical demand has spike in few of the months. These few months spiked order lines need to be removed or streamline if it is not genuine for better forecasting. But, in spare part inventory forecasting, number of parts & order lines are very high. It is not practical to remove these order lines one by one. These order lines have been generated mainly due to retro fitment, filed fix, one-time order etc. Means these demands are less probable to generate once again. In this paper, researcher has shown new method of demand normalization which help to streamline the data instead of removing spiked order lines one by one. New method of demand normalization helps to improve the forecasting. Researcher has applied new method on spare part inventory data which was received from one of the large automobile company & result of it shown improvement in forecast accuracy. Researcher mainly done the experiment on fast and medium mover parts as per company requirement.

New developed method of demand normalization improves spare part forecasting