Limousine Service Management: Capacity Planning with Predictive Analytics and Optimization

TL;DR

This paper presents a data-driven approach using predictive analytics and optimization to improve capacity planning for hotel limousine services, resulting in significant cost savings and better service levels.

Contribution

It introduces a novel, practical framework combining open-source tools and automation for effective limousine fleet scheduling and demand management.

Findings

Up to S$3.2 million annual savings achieved

Improved service levels during peak demand periods

Effective use of open-source analytics and automation tools

Abstract

The limousine service in luxury hotels is an integral component of the whole customer journey in the hospitality industry. One of the largest hotels in Singapore manages a fleet of both in-house and outsourced vehicles around the clock, serving 9000 trips per month on average. The need for vehicles may scale up rapidly, especially during special events and festive periods in the country. The excess demand is met by having additional outsourced vehicles on standby, incurring millions of dollars of additional expenses per year for the hotel. Determining the required number of limousines by hour of the day is a challenging service capacity planning problem. In this paper, a recent transformational journey to manage this problem in the hotel is introduced, driving up to S\$3.2 million of savings per year with improved service level. The approach builds on widely available open-source statistical and spreadsheet optimization tools, along with robotic process automation, to optimize the schedule of its fleet of limousines and drivers, and to support decision-making for planners/controllers to drive sustained business value.