This study is conducted to facilitate the decision making process ofrnGule1e Soap Factory by developing an appropriate forecasting model. Itrnaims at providing accurate sales forecasts for future sales, which is arnvital input in decision-making. Particularly, the focus is given forrnforecasting monthly laundry soaps sold by the factory.rnTowards achieving its objective the study considered the Box-Jenkinsrnapproach to time series analysis. A total of 68 monthly sales data hasrnbeen taken for analysis or model building purpose. Moreover, additionalrn5 months sales data has been used for forecasting purpose.rnThe analysis of the data, which is carried out using S-Plus 2000rnpackage, suggested that ARIMA (3,1,0) model represents the pattern ofrnmonthly laundry soap sales data. According to this model, forecastingrncurrent sales essentially requires the inclusion or consideration of thernprevious four consecutive sales data occurring at the four successivernlags. Moreover, it is found that the sales data recorded in the first lag hasrngreater influence or contribution in forecasting current sales volume.rnOn the other hand, it is observed that the sales data involves a seasonalrncomponent that turns out to affect the sales volume approximately in 3.5rnmonths. In other words, the analysis indicated that there is a seasonalrncomponent that occurs with 3 or 4 months periodicity. This in turnrnresulted in attaching high importance to the third and fourth lagrncoefficients as compared to the coefficient of the third lag.rnThe results obtained led to the conclusion that the time factor IS thernmajor but not the only relevant factor in forecasting sales. Otherrnconsiderations in relation to promotional activities, competitors action,rnseasonal factors, etc should be kept in mind.