How Railroad Performance Impacts Customer Experience and Freight Cost
Logistics professionals are under increasing pressure to manage costs. However, customer expectations are changing – we are accustomed to Amazon-like ETAs in our personal lives and expect similar experiences in our professional lives now.
Our last article discussed how end-to-end ETAs calculated by a third party provide a more accurate estimate. Shippers can then plan to communicate arrival dates to receivers and transloaders and plan pipelines for loading/unloading at facilities. In turn, transloaders and receivers can plan further in advance of railcars arriving, minimizing dwell time at destination yards. As a result, cycle times decrease, and shippers can move more products with the same railcars, lowering railcar lease or purchase costs.
None of these results can be achieved without consistent transportation performance. An accurate measurement of each railroad’s on-time performance is essential to maintaining the ETA’s consistency (and, in turn, accuracy). Shippers and railroads should meet regularly, if not monthly, for strategic partnerships to review the critical operating metrics over the road and the first and last mile and collaborate on any course-corrective actions needed based on the actual performance data.
How Railcar Tracking Software Can Improve On-Time Performance
Railroads keep a close eye on performance metrics because they directly impact their bottom line. They may provide shippers a quarterly or monthly performance deck, which is extremely helpful in understanding the trends and underlying context. Still, shippers need a control-tower type solution for monitoring performance across all the rail partners in real-time, so they can immediately understand changes in the trend and communicate the impact to their customers at any time.
An effective railroad on-time performance management tool should be included in any modern railcar tracking system. The most effective systems will have two reporting functions:
- End-to-end performance measurement across all railroads, from shipping origin to unloading destination, and the empty return. This automates the data gathering process to support fleet sizing analysis and helps the customer service or sales team communicate estimated lead times for the latest sales.
- Segment-level performance metrics for each railroad automate the data gathering process for the logistics manager to focus more time on analyzing the trends and working with each railroad to improve consistency of performance.
What to Look for in an On-Time Performance Tool from a Railcar Tracking System
The On-Time Performance tool should be able to be customized to different business needs, depending on the user and the business problem they are trying to solve. One-size-fits-all static reports provide an excellent KPI, but they do not help the data analysis that will have a transformative impact on the shipper’s rail network.
Some of these custom functions might include:
- Dynamic start/stop events for the trip or each railroad segment
- Ability to measure the transit time separately from the industry time at first/last mile
- Ability to measure loaded and empty trips separately
- Exclusion of bad orders or force majeure cars (for performance management or lead time prediction) or inclusion of these events (for cycle time and fleet size analysis)
- Superusers should be able to enter contractual shipment performance times and have the reports automatically calculate the specific rail shipments that were on time for the given time period being analyzed.
The data management process that powers these reports is equally important – if the data has gaps, the reports will not be meaningful for analysis. One critical feature is backup logic for when each segment is not reported. For example, suppose the delivering carrier does not report the interchange delivery. In that case, the receipt event from the receiving carrier can be used to stop the clock for the previous segment.
In any business case, these tools should allow users from divergent functions (logistics, sales, customer service, finance) to perform their own analysis without needing advanced data skills. Users should be able to filter, refine, and customize the data with the click of a few buttons instead of downloading to excel to create pivot tables or add formulas.