Using forecasting techniques to predict meal demand in Title IIIc congregate lunch programs

The purpose of this study was to determine which forecasting model would most accurately predict meal demand in Title IIIc congregate lunch programs designed for serving older adults. Forecasting techniques including naı̈ve, moving average (three versions) and simple exponential smoothing were appli...

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Bibliographic Details
Published inJournal of the American Dietetic Association Vol. 104; no. 8; pp. 1281 - 1283
Main Author Blecher, Lee
Format Journal Article
LanguageEnglish
Published New York, NY Elsevier Inc 01.08.2004
Elsevier
Elsevier Limited
Subjects
Aged
Biological and medical sciences
community feeding centers
community nutrition programs
estimation
Food programs
Food Services - trends
Food Services - utilization
Forecasting - methods
Forecasting techniques
Health Services Needs and Demand - statistics & numerical data
Health Services Needs and Demand - trends
Humans
Mathematics
Medical sciences
Middle Aged
Models, Statistical
number of meals
Older people
prediction
Predictive Value of Tests
Prevention and actions
Public health. Hygiene
Public health. Hygiene-occupational medicine
Sensitivity and Specificity
Specific populations (family, woman, child, elderly...)
California
United States
North America
America
Human
Demand
Nutrition
Meal
Urban area
Forecast model
Elderly
Public health
Lunch
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Summary:The purpose of this study was to determine which forecasting model would most accurately predict meal demand in Title IIIc congregate lunch programs designed for serving older adults. Forecasting techniques including naı̈ve, moving average (three versions) and simple exponential smoothing were applied to data collected over a 4-month period from seven meal sites located in a large urban area. An analysis of the forecasting models using mean absolute deviations and mean squared errors indicated that simple mathematical forecasting techniques provided better predictions of meal demand than did the naı̈Adive method for all sites. In four of the seven sites, exponential smoothing was the best forecasting model, whereas in the remaining sites, moving average models provided the best forecast. Implications are discussed.
Bibliography:ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
content type line 23
ISSN:0002-8223
2212-2672
1878-3570
2212-2680
DOI:10.1016/j.jada.2004.05.209