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Related Experiment Videos

Forecasting in foodservice: model development, testing, and evaluation.

J L Miller1, P A Thompson, M M Orabella

  • 1Department of Hotel, Restaurant, Institution Management and Dietetics, Kansas State University, Manhattan 66506.

Journal of the American Dietetic Association
|May 1, 1991
PubMed
Summary
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Mathematical models accurately forecast foodservice demand, improving efficiency. These menu-item forecasting techniques help control costs and maximize profits in food operations.

Area of Science:

  • Operations Research
  • Foodservice Management
  • Predictive Analytics

Background:

  • Foodservice operations face challenges in accurately predicting menu-item demand.
  • Current forecasting methods may lack the precision needed for optimal inventory and production management.

Purpose of the Study:

  • To develop, test, and evaluate mathematical models for forecasting menu-item production demand in foodservice settings.
  • To compare the accuracy of developed models against existing forecasting methods.

Main Methods:

  • Data collection from university residence and dining hall foodservices.
  • Forecasting customer counts using deseasonalized simple exponential smoothing.
  • Predicting menu-item demand by integrating customer count forecasts with preference statistics.

Related Experiment Videos

  • Model evaluation using mean squared error, mean absolute deviation, and mean absolute percentage error.

    • Main Results:

    • Developed mathematical models demonstrated higher accuracy than current foodservice forecasting methods.
    • The proposed models effectively forecast both customer counts and specific menu-item demand.
    • Forecasting techniques were evaluated using established statistical error metrics.

    Conclusions:

    • Mathematical forecasting models are effective tools for foodservice operations.
    • Implementation of these techniques can lead to cost control, increased productivity, and maximized profits.
    • Foodservice managers can utilize these forecasting methods with accessible personal computer software.