A Bayesian Approach for Predicting Food and Beverage Sales in Staff Canteens and Restaurants

TL;DR

This paper introduces a Bayesian-based forecasting method using generalized additive models to accurately predict daily sales of menu items in restaurants and canteens, accounting for complex seasonalities and outliers.

Contribution

The paper presents a novel Bayesian approach with generalized additive models tailored for restaurant sales data, enabling interpretable and accurate demand forecasts.

Findings

Models effectively capture seasonal patterns and outliers.

Outperforms traditional forecasting methods in predictive accuracy.

Applicable to diverse restaurant settings with complex data features.

Abstract

Accurate demand forecasting is one of the key aspects for successfully managing restaurants and staff canteens. In particular, properly predicting future sales of menu items allows a precise ordering of food stock. From an environmental point of view, this ensures maintaining a low level of pre-consumer food waste, while from the managerial point of view, this is critical to guarantee the profitability of the restaurant. Hence, we are interested in predicting future values of the daily sold quantities of given menu items. The corresponding time series show multiple strong seasonalities, trend changes, data gaps, and outliers. We propose a forecasting approach that is solely based on the data retrieved from Point of Sales systems and allows for a straightforward human interpretation. Therefore, we propose two generalized additive models for predicting the future sales. In an extensive evaluation, we consider two data sets collected at a casual restaurant and a large staff canteen consisting of multiple time series, that cover a period of 20 months, respectively. We show that the proposed models fit the features of the considered restaurant data. Moreover, we compare the predictive performance of our method against the performance of other well-established forecasting approaches.