Nutritional Sciences

The Korean Nutrition Society (한국영양학회)
권호 표지

Suggesting Forecasting Methods for Dietitians at University Foodservice Operations

  • Ryu Ki-Sang (Lester E. Kabowff School of Hotel, Restaurant, and Tourism Administration, University of New Orleans)
  • Published : 2006.08.01
Download PDF
⟨ Previous Next ⟩

Abstract

The purpose of this study was to provide dietitians with the guidance in forecasting meal counts for a university/college foodservice facility. The forecasting methods to be analyzed were the following: naive model 1, 2, and 3; moving average, double moving average, simple exponential smoothing, double exponential smoothing, Holt's, and Winters' methods, and simple linear regression. The accuracy of the forecasting methods was measured using mean squared error and Theil's U-statistic. This study showed how to project meal counts using 10 forecasting methods for dietitians. The results of this study showed that WES was the most accurate forecasting method, followed by $na\ddot{i}ve$ 2 and naive 3 models. However, naive model 2 and 3 were recommended for using by dietitians in university/college dining facilities because of the accuracy and ease of use. In addition, the 2000 spring semester data were better than the 2000 fall semester data to forecast 2001spring semester data.

Keywords

References

  1. Miller JL, Shanklin CW. Forecasting menu-item demand in foodservice operations. J Am Diet Assoc 88(4):443-449, 1988
  2. Messersmith AM, Miller JL. Forecasting in foodservice. John Wiley & Sons, New York. 1991
  3. Miller JJ, McCahon CS, Miller JL. Forecasting production demand in school foodservice. School Food Serv Res Rev 15(2):117-122, 1991
  4. Jang MS, Lee JM, Baek SY. Analysis of training needs with roles in college and university foodservice dietitians. J Kor Diet Assoc 11(4):462-472, 2005
  5. Miller JJ, McCahon CS, Miller JL. Foodservice forecasting using simple mathematical models. Hospitality Res J 15(1):43-58, 1991 https://doi.org/10.1177/109634809101500105
  6. Miller JJ, McCahon CS, Miller JL. Foodservice forecasting: Differences in selection of simple mathematical models based on short-term and long-term data sets. Hospitality Res J 16(2):95-102, 1993
  7. Miller JL, Sanchez A, Sanchez N. Forecasting food production: A comparative study of four models. Nat Assoc Col Uni Foodserv 18(1):65-71, 1994
  8. Repko CJ, Miller JL. Survey of foodservice production forecasting. J Am Diet Assoc 90(8):1067-1071, 1990
  9. Miller JL, McCahon CS, Bloss BK. Food production forecasting with simple time series models. Hospitality Res J 14(3):9-21, 1991
  10. Thompson GM. Labor scheduling, part 1, Cornell Hotel Restaurant Adm Q 39(5):22-31, 1998
  11. Pappas JM. Eat Food, not Profits: How computers can save your restaurant. Van Nostrand Reinhold, New York, 1997
  12. Hanke JE, Reitsch AG. Business Forecasting, 6th ed. Prentice Hall, Upper Saddle River, NJ, 1998
  13. Ryu K, Jang S, Sanchez A. Forecasting methods and seasonal adjustment for an institutional foodservice facility. J Foodserv Bus Res 6(3): 17-34, 2003 https://doi.org/10.1300/J369v06n01_02
  14. Theil H. Principles of Econometrics. Wiley, New York. 1971
  15. Song H, Wong KF, Chon KS. Modeling and forecasting the demand for Hong Kong tourism. lnt J Hospitality Manage 22:435-451, 2003 https://doi.org/10.1016/S0278-4319(03)00047-1
  16. Hanke JE, Wichern DW, Reitsch AG. Business Forecasting, 7th ed. Prentice Hall, Upper Saddle River, NJ, 2001