Beets’ cultivation and sugar production represent one of the most important parts of Greek agricultural economy. A careful and well-organized planning of the production as well as the determination of an accurate safety stock is important for sugar industry, as for many other companies and organizations, in order to define the production quantity which leads to maximum revenues and profits. Forecasting, and especially widely used statistical forecasting techniques, is the best way for policymakers to organize their activities and company’s production and make the appropriate adjustments. Apparently, management information systems and forecasting support packages play a leading role in this area, since the amount of data under process is usually quite large and demands an automated procedure to effectively produce and evaluate forecasts. In this case study, “Pythia”, an expert forecasting platform developed by the Forecasting and Strategy Unit of the National Technical University of Athens, was implemented on a monthly data series regarding sugar sales of a Greek sugar factory for the years 2000-2005, bringing theory and practice together. Additionally, the methods or combinations of methods which are well suited for this time series are highlighted based on three error indices. Finally, the results of the study and conclusions are considered and perspectives of progress and development in the field of forecasting are contemplated.
