THE IMPACT OF FORECASTING METHODS ON DEMAND PROJECTION IN FAST FOOD RESTAURANT

Abstract

Forecasting involves the generation of set of numbers that corresponds to a future occurrence. This study investigates the various forecasting methods as well as how to choose the most effective forecasting method. After formulating the hypothesis: all forecasting methods have different effectiveness. Quantitative methodology was used and the impact of forecasting model selection was tested by using a computer simulation model. The data was obtained from a restaurant. Moreover, articles related to the research topic were analyzed. The results show how demand forecasting affects inventory replenishment decisions by the retailers, and production decisions by the supplier under different forecasting method. There are multiple models such as Naïve Approach, moving averages, weighted moving averages and exponential smoothing. Analyses of the simulation output indicate that the selection of the forecasting model influences the performance and demand patterns faced by retailers. The results also help manager’s select suitable forecasting models to improve performance. The results support the hypothesis of the study.