These forty factors were used in developing the forecasting model based on the artificial neural networks (ANNs) technique discussed in Leksakul and Sopadang (2012), but the disadvantage of this model is that when updated data are available, the model based on ANNs needs to be re-generated for more accuracy in its forecasting performance. The application of the ANNs forecasting model can be also found in Kasemset et al. (2012). Due to the disadvantage posed by ANNs, this research work took it up as its aim the creation of a forecasting model that will predict the quantity of supply of off-season longan, using the multiple regression technique. The multiple regression technique is a statistical technique used for predicting the unknown value of a variable from the known value of two or more variables. The general formulation of the forecasting model, based on the multiple regression technique, is given in Equation (1):