PREVISÃO DE DEMANDA DE REFEIÇÕES EM RESTAURANTE UNIVERSITÁRIO COM OFERTA INSUFICIENTE

This paper aims to examine the meal demand forecasting in a University Dining Service (UDS) with short supply. The research derived from low productive capacity problems faced in some campus of the São Paulo State University (UNESP), which do not meet all demand. To estimate the pr...

Descripción completa

Guardado en:
Detalles Bibliográficos
Autores principales: Adriana Barbosa Santos, Melissa Galdino Martos, Julia Muchatte Trento, Natália Soares Janzantti
Formato: article
Lenguaje:ES
PT
Publicado: Universidade Federal de Santa Catarina 2017
Materias:
Acceso en línea:https://doaj.org/article/8ab3f67fd6b74aa79342d2879c6339ed
Etiquetas: Agregar Etiqueta
Sin Etiquetas, Sea el primero en etiquetar este registro!
Descripción
Sumario:This paper aims to examine the meal demand forecasting in a University Dining Service (UDS) with short supply. The research derived from low productive capacity problems faced in some campus of the São Paulo State University (UNESP), which do not meet all demand. To estimate the proportion of people truly interested in the dining services and to calculate the surplus of non - service, it was suggested a design covering a combination of statistical techniques suc h as multiple regression analysis, diagnostic tests measurements, ROC curve, supported by a market research with quantitative approach. With the utilization of these techniques combination, it was analyzed information based on socioeconomic profiles, menu requirements, reason for eating in the UDS, and the food habits of 544 academic people. After analysis, it was estimated a surplus of 311 daily non - services (78% over than offer). Most UDS users are undergraduate students in vulnerable financial condition s for food and residence; which use the UDS because of price; living near the campus; and are moderately demanding about the menu. The conclusions reinforce the relevance of contextual information about the service user in the demand forecasting model aimi ng to increase the estimate accuracy of quantity of non - service.