eThesis - Kauppakorkeakoulun sähköiset gradut

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The objective of this thesis is to test existing forecasting models for intermittence demand SKU's and implement the best forecast model that suits the inventory control policy of the case company. The optimal forecasting model was selected based on the model that produces optimal performance in terms of customer service levels, inventory total cost and inventory value.

Intermittence demand type was categorized based on degree of lumpiness, erratic, smooth-intermittence and intermittent types. The quantitative data set comprised of historical demand information from 2010-2012 (36 months period) for sixteen thousand stock keeping units (SKU) in the three central distribution centers of the case company. Algorithms for the different forecasting models was developed using VBA programming in Excel 2007 and simulated against the demand data. Explorative approach was used to gather information regarding new material introduction process, forecasting parameters used in the software package (Servigistics) and how the results of the research can be implemented in the case organization.

The result of the analysis shows that traditional forecast accuracy measure is inadequate for selecting best forecast model. Nevertheless, our result shows that no forecast method (Simple Exponential Smoothening (SES), Croston and Modified Croston (SBA) explicitly showed superior performance in all the traditional measures utilized. When stock control measure was utilized Croston showed superior customer service levels of 1% to SES and 1.4% to SBA. The superior customer service levels come with a 1% increase in total cost. The findings of the thesis also suggest the need for amending the outlier management settings in the software system and to customized tracking signal in the forecast review board to enable the prioritization of review reasons in degree of descending order of stock value and tracking signal estimates