case study

Optimising Vebego's service level to increase travel experience of NS customers

Facility services
Real-time reporting of train arrival and departure delays

Just like any other facilities services provider, Vebego is committed to achieving an agreed upon service level (SLA) to best serve their clients. One of their clients is NS, the Dutch railway corporation. Vebego takes control over the cleaning of trains on behalf of Vebego on a daily basis. Having clean trains is a basic hygienic need that makes or breaks convenience and experience for travellers.

Increase in SLA reaching 99%
Monitoring train scheduling every 5 minutes
From 48 hours to 10 minutes response time
Automated cleaning scheduling
Error-free scheduling
Optimal, real-time allocation of cleaners
Ensuring pleasant traveling experience

The challenge The challenge The challenge

the challenge

At any time of the day trains may experience sudden delays, meaning that cleaning schedules need to be changed unexpectedly. In order to allocate cleaners most efficiently, Vebego staff members have to be as responsive as possible on those delays. Acting on changing data on such a short notice is, however, practically impossible for humans as any second counts. Therefore, train schedules were delivered two days in advance by NS to base cleaning schedules on. Because of this suboptimal resource allocation cleaners have to wait for a train to arrive in some cases. As a result, less trains are cleaned which negatively impacts travel experience, apart from the fact that cleaners are likely less productive and satisfied.

The before The before The before The before The before

The before

Before Yara was implemented, Vebego had to accept that delays would disturb cleaning schedules to a certain extent. This process looked similar to the following steps:

This process contained the following steps:
Checking for any delays in the train schedule data provided by NS.
In case of a delay, selecting the relevant timeslot in the scheduling system.
Adjusting the timeslot to account for the delay, after which the system generated a newly optimised cleaning schedule.

The after The after The after The after The after The after The after


With help of Yara's real-time insights, Vebego is now able to allocate cleaners in a more optimal way. As no humans are involved in the process, Yara responds to delays in a blink of an eye without missing anything. This has enables Vebego to clean 4% more trains, boosting the satisfaction of travellers.     

Our Software robot Yara continuously checks the real-time arrival and departure data provided by NS for any delays, taking the following actions:
Reviewing data.
Yara reads each update regarding arrival and departure times to look for delays.
Spotting delays.
Yara spots a delay as soon as it occurs in the data delivered by NS.  
Processing delay.
 In the scheduling system, Yara navigates to the relevant timeslot to account for the delay.