Foodservice Forecasting: A Comparative Study of Exponential Smoothing and Expert System Models

Abstract

The forecast is one of the most critical decisions in the foodservice industry because it is reflected in the material costs, labor costs, labor schedule, and overhead costs. The purpose of this study was to compare the purchasing forecasts using an expert system (knowledge base system) and an exponential smoothing model to the actual usage data in order to determine which one is best for the operation of a university dining center. This university dining center utilized a food court concept. Three of the four service lines were forecast, these lines included mexican, italian, classic, and deli menu options. An expert system previously developed was used. The expert system contained more than 1000 rules that replicate the expertise of the manager of the foodservice operation in forecasting. Data from the fall semester 1993 were used to forecast spring semester 1994 using the exponential smoothing. Different alpha values were tested to find the forecast value with the minimum mean squared error (MSB). The optimum value was α = 0.45. Results showed that in forecasting the classic and mexican lines the expert system model outperformed the exponential smoothing model because it had the lowest mean absolute deviation and mean squared error. However, for the italian line, the exponential smoothing forecast outperformed the expert system.