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Maximizing fleet utilization and eliminating unused railcars is one of the easiest ways to lower costs, but many companies lack the advanced technology and optimization techniques to effectively gauge and improve utilization. While some have the tools, they still don’t understand how the metrics are calculated and how they should be used. For example, in order to understand how to fleet size, you must understand and incorporate all of the variables in cycle time. By measuring variability and using it to drive fleet sizing decisions, managers can begin to identify lanes that are forcing excessive fleet requirements and focus efforts to reduce variability.
If you want to have better performance, search for your weaknesses, which often times are limited visibility of your assets and a lack of measurement and rigorous process. Clearly define the business process, use the right metrics and make sure they are used consistent across the company.With these in place, look for gaps in key processes and hand-offs between departments that may have different objectives. Move away from a siloed approach and work toward an integrated platform approach that facilitates integrated processes, common measurements, and cross-training of employees. Design and reengineer your processes to continually take advantage of new data. In an advanced business process, there is continuous leveraging of shared data and metrics between analysis and forecasting, strategic planning, and execution.
GVP provides continuous, real-time visibility of railcar activity and location. To properly analyze your organization’s history, it is imperative to capture the right data at the right granularity. For example, railcar velocity may vary by railcar type, lane, operating railroad, and season. Missing any of these data elements would result in a blind spot that could have significant impact on forecast accuracy. Railroad data is difficult to work with. There are millions of daily events. All the data going into the system is external and from a variety sources in many formats. You have to reprocess in order to find out what happened, and really understand the message sequence.
In the new Big Data environment, many organizations are striving to be more analytics-driven. With improved data quality and asset control, Intellitrans is now in a position to make use of optimization, the harnessing of advanced mathematics, and special software that makes recommendations about assets. We have developed an optimization model to improve railcar utilization across the North American rail network. Tactically, the model advises IntelliTrans and the railroads on daily redistribution of empty railcars. Strategically, the model can run complex “What-If” scenarios, to help them analyze the impact of changes to supply, demand, network, and even distribution methodologies.