Analytics for labor planning in systems with load-dependent service times

Abstract

This paper presents a generalized framework for labor planning in systems with load-dependent service times. Our approach integrates customer arrival forecasting, service time estimation, and staffing into an end to end process. More specifically, (i) we propose a hybrid model to forecast customer arrivals, that combines a traditional time-series technique with a state-of-the-art machine learning algorithm, thereby allowing to incorporate a rich set of predictors; (ii) we develop a methodology to estimate the distribution of load-dependent service times from past transactions data; and (iii) we present a stochastic programming formulation to determine staffing levels under quality-of-service constraints. Finally, we develop a heuristic solution algorithm that utilizes an embedded discrete event simulation to evaluate system performance.To demonstrate the practical applicability of our approach, we conduct a case study at a franchisee of a major international fast food chain. Our data set includes the number of transactions, average order value, average service times, and staffing levels recorded at the restaurant on an hourly level, as well as information on marketing activities of the fast food chain on a daily level for nine consecutive years. We show that by applying our approach, the personnel expenses could be reduced by 4.4%, which would translate into an increase in earnings before interest and taxes by 13% at the collaborating restaurant.