Railway Use-cases

Rail Operations (Transportation):
Operations stream enabled analytics on key performance metrics including Train speed, on time performance, Terminal performance, Locomotive assignment.

Safety:
Safety stream enabled analytics on safety incidents, efficiency tests in conjunction with a regional safety performance down to an employee’s personal records

Finance:
Finance stream helped analysts, finance managers and executives to perform analysis on revenue and cost information

Assets:
Assets stream enabled analysis on key assets i.e. Cars and Locomotives.

HR:
HR stream enabled deep analysis on the people from and specifically integrated crew information that is critical for operations

Operations Dashboard:
A master dashboard that enable holistic analysis and quick transition between the dashboards of different stream to understand correlations between KPIs.

fyi @ChrisS

Transportation

Accepted answers

All comments

ChrisS

@williamchan @otto @jmt214 What do you guys think about some of these use cases? Should we incorporate these into some future meetings? 🙂

williamchan

Absolutely! Are these use cases readily available for presentation? Or are these use case ideas?

If they are ready, perhaps the Transportation + Logistics Community could vote on which ones they'd like to see!

Thanks for sharing!

PhilH

These use-cases are not readily available yet, but I have just discovered a great article online: https://www.railwayage.com/analytics/ns-analytics-boost-network-planning-optimization/?RAchannel=home

PhilH

https://www.railwayage.com/analytics/ns-analytics-boost-network-planning-optimization/?RAchannel=home

March 21, 2020

Analytics, Class I, Freight, News

NS: Analytics Boost Network Planning, Optimization

Written by Amber Huber, Manager, Network Planning Systems, Norfolk Southern

An inventor of the World Wide Web once said “Data is a precious thing and will last longer than the systems themselves.” Neither data analytics nor railroading are typically the most captivating topics at the gatherings I go to with friends and family, but to me, the juncture of the two is nothing short of fascinating. And using data analytics to help rail customers optimize the way they ship tons of materials—from processing facilities and ports to factories and back again—is critical to the efficient organization of our extended supply chains.

In my area, Norfolk Southern’s Network Planning & Optimization Systems, our primary responsibilities are:

Managing departmental analytics processes to create transportation metrics and reports.
Supporting dashboards to empower business users.
Delivering actionable insights regarding the operating plan.

NS photo

Some examples include train performance metrics, location performance metrics, operating plan compliance and adherence, and pipeline analysis.

Our goal is to develop and maintain an efficient and cost-effective operating plan that is reliably executable on a daily basis to balance the needs of the customer with asset efficiencies. We want to supplement and enhance this system to provide users more visibility into our network in a way that is flexible and can be altered with new business logic at any point. It’s the company’s vision to be the safest, most customer-focused, and successful transportation company in the world.

Railroads aren’t new, and the production systems that move traffic have been around for a long time. While this system is fantastic at supporting transportation in a reliable manner, it is somewhat inflexible at addressing the ever-changing needs of our business users. Let me put it this way: When I started this position, I didn’t expect to have to read COBOL.

Railway transportation is all about driving operational excellence by finding efficiencies in logistics. For our team, this means using Big Data and analytics to gain greater visibility into the network performance of its tracks and cars. The more visibility into our operations and customer demands, the greater our ability to drive transportation optimization and efficiency.

NS photo

Summer 2019 saw the rollout of the initial phase of TOP21, our PSR (Precision Scheduled Railroading) operating plan, which is focused on increasing operating efficiencies, enhancing customer service and driving long-term shareholder value. This plan supports five core principles of transportation: serve our customers, manage our assets, control our costs, work safely, and develop our people. Our focus on reducing circuity and train-miles while increasing train productivity allows us to reduce asset costs while maintaining reliable transit times.

Teradata, one of our data analytics partners, helps manage analytics produced by our department. We use Teradata to produce key metrics that measure system health, identify enhancements, manage assets, and create transparent visibility in previously unknown variables. We consistently search for potential improvements to our operating plan by slicing data in new and innovative ways. It is the single integration point for a majority of our business, supporting 5,000 internal users with more than three million queries on a given day.

Along with Teradata, we use multiple tools to better understand and analyze the data found within. Alteryx incorporates data from all sources and cleans and preps the data for analysis. Tibco Spotfire allows business users to easily create interactive dashboards with different views of the data, and allows for the end users to view on-demand details.

For greater visibility into the demand of our rail network and train traffic, an understanding of out-of-network track supports our ability to forecast demands based on customer shipments connecting to the network of interchange carriers. However, our legacy systems did not have the data and analytics tools necessary to accurately plan, improve, and optimize arrivals, departures, traffic management, overall system health and emergency monitoring at the speed required in today’s data-driven world.

For example, in the event of a flood, we are unable to move traffic over an out-of-service line. There is a short turnaround time for us to quickly redirect any incoming traffic, and to create contingency plans to address traffic at the location. Our department is responsible for bringing the data to the various internal customers quickly, and to adjust the visibility as the situation progresses. This greatly reduces the downstream effects and helps maintain velocity of shipments to customers.

Currently, our legacy systems do not easily highlight far away incoming traffic in a way easily digestible by the end users; they have to “hunt” to find the answers. The costs associated with unexpected traffic can be high due to the static nature of the physical railroad: We may not have enough power to handle the extra traffic or have a crew available. All of this adds processing time and costs to our bottom line. The earlier that our field users are aware of the situation, the lower the cost and time lost associated with the unexpected traffic.

Given this, we knew that we needed a new method of “producing” a traffic schedule, so we visited the field to get input on the business logic and gain a better understanding of the limitations of our legacy system. This resulted in the development of a traffic prediction process that predicts future train schedules at a location and identifies where traffic exceeds capacity. This allows for more advanced notification to end users, which helps maximize productivity, minimize operational expenses and improve customer satisfaction.

After TOP21 was implemented, one of our first projects was to create a holistic view of all train metrics. The end users wanted a “one stop shop” for all aspects of train performance with drill-down details. Compiling so many metrics can be challenging to do in a way that doesn’t overwhelm the end user. To address this, we created a dashboard that told a “story” about the selected train by presenting the metrics in steps.

NS photo

Trying to merge data from multiple systems and customers is an imposing challenge, regardless of your industry. Because our legacy system was not designed for ad hoc reports, we had poor visibility into repetitive corrections to shipment data. To better identify these types of repetitive errors, we created a process that compared millions of data points from Teradata to identify trends in repetitive corrections. These results, which were compiled and displayed to management, enhanced our legacy systems to correct errors and served as an opportunity to collaborate on solutions with the customer.

We use analytics from Teradata to unify internal and external data sources in a central data warehouse to gain an accurate picture of the future demands of the network. Now we can visualize exactly where the shipment is on a map. We’re combining our crew data and all of our accounting and asset cost information to easily provide a picture of the shipment as a whole. By integrating the data and performing transportation analytics, our new systems show more detail of our shipments earlier in the transportation process.

NS Network Operations Center. NS photo.

Analytics now empower our business users to identify, provide context, and justify changes in our operating plan. The system is easy to use and allows the quick turnaround time needed to bring key insights to the users when and where needed. It has given us invaluable opportunities to analyze our entire network rather than small segments, serves as an invaluable complement to our legacy systems, and permits us to keep moving forward.

Amber Huber

The bottom line is that we want to give customers a more precise time of when they can expect to receive their shipments. We want to run all of our trains to a more precise schedule and reduce the number of cars on our network, enabling us to move heavier trains at a faster rate. This helps to increase on-time shipments to customers. Our focus is on increasing asset efficiencies and making sure that when we run a train, it’s moving in the most efficient way possible to meet the supply-chain needs of our customers.

Categories: Analytics, Class I, Freight, News

jacob_kahn

@PhilH

These use cases are awesome. I do not work in the rail-industry; however, as an obsessor over Alteryx and analytics, I think it is great to show how our daily and routinely lives consist of so much data and analytics.

I'd be very interested in seeing how Alteryx can be used to verify and provide some of the information that you listed.

Please keep this chain updated with your feedback 🙂

Sincerely,

Jacob

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