How Analytics can help Freight Railroads
The Railways have come a long way from coal engines and paper plans. They have been adapting at a phenomenal rate. When it comes to Freight Railroads, the mechanisms are a bit more complex and tedious as compared to passenger railroads. With passenger railways, the passengers can be grouped and sorted as per their destinations. This process, however being a tedious task for those who plan it, is relatively simple as compared to the calculations that go through in case of freight railways.
The freight cars are coupled and sorted into blocks as per the destination but and they are routed in a hub and spoke pattern. However, these blocks have to be kept splitting and resorting as per the requirement, until they all reach their required destination. There is a large amount of routing, sorting and blocking cars, scheduling, assigning locomotives, and dispatching activities undertaken for the freight cars. Until very recently, these activities were undertaken through complex spreadsheets that helped sort these details. But the number of freight cars and the frequency of trains kept growing. And the Railways were forced to adapt to analytics.
Another requirement of railroads is to calculate the required locomotives for the estimated tonnage. These plans are now beyond human calculation as the calculations alone spread out into multiple inputs and outputs. All of these conundrums that freight railroads have faced for years are now being taken care of by simple analytics based operating plans. Analytics are increasingly streamlining the processes by making data collection and sorting easier, and allowing non-technical staff to handle the technical activities. The calculations required for freight railroads are easily taken care of along with the requirement estimation by the analytics platforms in use. The need in the freight railroad industry is to encompass all aspects of the Big Data and give out precise results in form of routing and blocking plans, locomotive assigning, and dispatching details.
The traffic density of can increase economies of scale in the rail freight industry. Data Analytics can help create the optimum route in and through any area. Another aspect to be considered is that real time information and telemetry can help with predictive analytics. This helps the railways operate more efficiently, save on fuel consumption, predict shipments, and keep the trains running on time. This real time information can also be captured through evolved analytics platforms.
Analytics is already proving to be an inevitable resource for freight railroads in various parts of the world. But the major reason, why analytics is being deemed as indispensible is the fact that no plan can be stringent. : All plans needs to be elastic. No amount of data and analysis can truly cover all contingencies; hence, any plan based on collected data needs to have built in room for correction. Data analytics provide that flexibility to recalculate instantly and change plans according to the requirement. This one factor alone result in massive savings.
IBM Global Services estimates that there would be around $148 billion in new investment in freight railroads by 2015 due to increased demand. If we go by the estimate, the freight railroads industry is about to grow tremendously just by streamlining the processes.