Forecasting in foodservice: model development, testing, and evaluation.
This study was designed to develop, test, and evaluate mathematical models appropriate for forecasting menu-item production demand in foodservice. Data were collected from residence and dining hall foodservices at Ohio State University. Objectives of the study were to collect, code, and analyze the data; develop and test models using actual operation data; and compare forecasting results with current methods in use. Customer count was forecast using deseasonalized simple exponential smoothing. Menu-item demand was forecast by multiplying the count forecast by a predicted preference statistic. Forecasting models were evaluated using mean squared error, mean absolute deviation, and mean absolute percentage error techniques. All models were more accurate than current methods. A broad spectrum of forecasting techniques could be used by foodservice managers with access to a personal computer and spread-sheet and database-management software. The findings indicate that mathematical forecasting techniques may be effective in foodservice operations to control costs, increase productivity, and maximize profits.
Miller
J L
JL
Department of Hotel, Restaurant, Institution Management and Dietetics, Kansas State University, Manhattan 66506.
Thompson
P A
PA
Orabella
M M
MM
D005518:Food Preferences; D005522:Food Services; D005544:Forecasting; D006801:Humans; D015233:Models, Statistical; D014495:Universities