Prediction-based Optimization of Service Capacity with Robotic Process Automation

Abstract

The research described in this paper concerns the automation of back-office workflows for the capacity management of hospitality services: continuously monitoring clients’ reservations and hotel registrations and automatically updating the decisions that establish optimal staffing levels to assure dependable and accurate services with minimum personnel cost, and maintaining a minimal stable workforce level. The service capacity management workflows connect multiple instances of online reservation and front-office customer registration processes (booking, check-in taxation, check-out invoicing according to the back-office strategy) with a combination of marketing- and operations-oriented back-office processes that: a) best match capacity and demand for quality services, and b) optimize the staffing levels and personnel workshift schedules. These workflows with many operations and complex timing are automated with the Robotic Process Automation (RPA) technology that uses AI techniques for seasonality prediction of confirmed service demand. A case study of optimal hotel staff assignment with RPA using the Blue Prism scheduler is included, and the gains of using RPA solutions are presented. DOI: 10.61416/ceai.v25i2.8439