How cloud and Big Data analytics is changing rail scheduling
There is a reason that the phrase “Making sure the trains run on time” is used to indicate successful management and efficiency around the world. Getting the trains to run on time is a complex process that involves sophisticated scheduling of crews, stock and schedules and so much more. Rail Scheduling is at the heart of a successful railway system and is often the difference between a profitable growing operation and a struggling, overwhelmed rail operator. This is one of the
Why is rail crew scheduling a problem?
Freight railroad crew scheduling comprises generation of crew duties for running trains on a schedule in a
Success can be measured against two key scheduling measures in a rail environment.
- Are we able to provide the number of trips that were agreed on with the rail authorities?
- And in doing so, are we minimizing the number of empty runs (“dead runs”) that take place, typically at the beginning and the end of a schedule?
An inability to meet either of those challenges poses a severe threat to the operator’s viability and profitability. But with more trains and passengers on the rails than ever before, in addition to a huge mobile workforce, the logistics of managing all those moving parts become harder and harder to get right. Cloud-based analytics has proven itself to be a vital tool in the fight for better rail scheduling.
Cloud Technology in rail scheduling
A premier Cloud & Big Data Analytics consultant, CloudMoyo, recently took on a big data challenge with a major North American rail operator, who deal with
The solution was to develop a cloud-native rail crew management system which can ensure that all available resources are optimally utilized to maximize existing investment. The key to successful rail scheduling is to look at the whole system. Not only management of the rolling
CloudMoyo’s Scheduling module is based on advanced algorithms that are used for determining the most optimal schedule based on various parameters that are configured in the system. It is based on history as well as traffic patterns. The planning output desires to satisfy
These types of interventions have been shown to produce a 5 -15% improvement in asset utilization, as well as up to 10% better driver utilization and ultimately a 2-5% reduction in overall costs and a massive leap of up to 30% in customer satisfaction.
If you would like to find out more about how Big Data Analytics can make an impact in your organization Book a demo of CloudMoyo Crew Management – a one-stop solution for the modern crew manager
Rail Scheduling and effective crew management make a dramatic difference