Using survival modeling for turn-time predictions in foodservice settings

Abstract

ABSTRACT Within the competitive foodservice industry, the ability to accurately measure the meal process known as turn-time is critical to the success of the firms in the industry. This is traditionally done through linear techniques such as multiple least squares (aka linear regression) or analysis of variance (ANOVA). However, linear techniques have theoretical properties that can potentially lead to bias in measurements of time duration variables, while survival models were designed for that purpose. This study utilized simulated data of a dine-in restaurant to test and compare the ability of linear regression to five survival models (proportional hazard models) for predicting the duration of turn-time. The results from the simulated trials show that while some of the survival models held incremental improvements, linear regression performed adequately for predicting the duration of turn-time even when taking the biased predictions into account. For operators who are in their infancy of developing restaurant revenue management systems, linear regression is recommended due to the practical ease of the models. On the other hand, operators who have well-established restaurant revenue management systems interested in incremental improvements should opt for survival models in predicting turn-time.