Track: Artificial Intelligence

Abstract

In this work, the demand pattern of readymade garments of a knit garments factory in Bangladesh was studied and factors influencing the demand pattern were identified. An accurate sales forecast is a prerequisite of achieving an accurate and effective supply plan of complete garments. A Fuzzy Inference System based algorithm was developed to predict the sales of garments according to the sales influencing factors. The developed algorithm is mainly a quantitative method of forecasting but also takes into account some qualitative issues. The developed model was compared with the actual total sales found by traditional seasonal forecast with trend adjustment. The seasonal factors for the two cyclic demand patterns, January – June, and July - December of each year were calculated and the Linear Regression trend was followed to calculate the traditional forecasting. The developed model showed better prediction as it matches closely with the actual sales. The Root Mean squared Error (RMSE) and Mean absolute deviation (MAD) are 3.605 and 3.081 in the developed fuzzy model which is within the standard limit. Thus the obtained result showed by the Fuzzy model yields better demand prediction taking into account the inherent uncertainties and with less error and thus the efficiency of the proposed Fuzzy model was verified.