Sales forecasting plays an important role in business strategy. An appropriate demand forecasting model is necessary for reducing the cost of holding or carrying inventory. At a company level, lowering the warehouse cost and optimizing the value chain is a prominent requirement for an optimum stock management. At this paper, demand forecasting model is built to support stock management activity for medium enterprises by means of data mining algorithms. SQL server analysis service is used for implementing demand forecasting model. The lists of available algorithms that are offered by SQL server analysis service are studied. The performance of available algorithms is tested using cross validation feature that is provided by SQL server analysis service to optimize the model performance. We explore here also the ability of RMSE (Root mean Squared Error) to include time series algorithm in cross validation phase. The proposed model is tested using a dataset for timber Export Company and the output is used for analysing the proposed model performance.