The Evaluation of Forecasting Methods at an Institutional Foodservice Dining Facility

Abstract

ABSTRACT The purpose of this study was to identify the most appropriate method of forecasting meal counts for an institutional food service facility. The forecasting methods analyzed included: naive model 1, 2, and 3; moving average, double moving average, simple exponential smoothing, double exponential smoothing, Holt's, and Winter's methods; and linear and multiple regressions. The accuracy of the forecasting methods was measured using mean absolute deviation, mean squared error, mean percentage error, mean absolute percentage error, root mean squared error, and Theil's U-statistic. The result of this study showed that multiple regression was the most accurate forecasting method, but naive method 2 was selected as the most appropriate forecasting method because of its simplicity and high level of accuracy.