Rushabh Vedia
Application of Analytics in the Transportation Industry’s Fleet Management Abstract The reference article, ‘Leveraging Analytics in Transportation to Create Business Value’, discusses problems concerning some disciplines in the transportation industry and showcases how analytics could be used to create business value by citing real life examples. The article focuses on how analytics derive business value concerning the transportation industry by studying cases pertaining to rail, trucking and airline. This paper is aimed to discuss an industry’s problem and how application of analytics provides a feasible solution to the problem. The Industry & Analytics Relevance The transportation Industry, with the ever changing technology, has to build systems and networks that eases current as well as nearby future commutes. This Industry has to pay a huge price for exploiting the current obsolete technology to avoid huge capital expenditures for better equipment. However, with the help of business analytical tools, this industry could progress without having much to worry about huge costs. Factors such as deriving customer value, optimizing resources, minimizing operating costs and maximizing profits are also discussed in the article in order to explain how analytics create business value. The Problem The article states the challenges faced by the industry and in acquiring accurate data. The industry is growing rapidly and will nearly double by 2035. The pressure of having lower operation costs, being environmentally friendly and improving reliability will be borne by transportation providers. For the providers, not only will such a situation lead to having higher capacities management issues but will also lead to having higher expenditures on the maintenance of these capacities. Such costs will make scrutinizing plans and models, build to cater to the discussed problems, a very essential part of this Page 1 of 3
Spring 2014
Business Analytics
Rushabh Vedia
industry and it would be applications of analytics that would help companies fragment vital and complex data in to simple business models. For simplicity, the article contains itself to discussing 9 operational cost involved in maintain fleets used in rail, trucking and airline. The purpose of doing so stems from the fact that transportation companies invest billions of dollars to make, repair and maintain fleets. It also gives a huge opportunity for applying analytics in order to reduce such explosive operational costs. One of the important data that the article cites is the scheduled maintenance of the fleets for the transportation industry using applications of analytics. The Cost Drivers In order to evaluate how analytics could provide a system to reduce the operational costs, it is important to understand cost drivers that run in maintenance of these fleets. First, all metal and metallic components used in making the fleet undergo wear and tear due to numerous factors such as climate, usage and age. Fleets with engines will have higher wear and tear compared to fleets without engines. Unhearing to handle the repair on time would lead to breakdowns and eventually replacing one or many parts in conjunction with that part. Second, the lengthy time frame developed in order to have routine checkups of the fleet leads to higher involvement of costs as necessary components would be avoided if faults are not accurately checked. Third, to avoid the earlier problem many companies schedule maintenance cycles very frequently, a major drawback in reducing maintenance costs for such fleets. The Application of Analytics Analytics application accurately indicates the mix in which the maintenance of fleets could be conducted. This benefits transportation providers to have optimum cycle of maintenance, proportions of cost involved and tracking system of conditions of fleets overall. A simple model in analytics would just require data of the equipment currently used or would be purchased to keep a track of their lifespan. The model will help answer problems such as overdoing or underdoing of the maintenance for each specific component. The business value that will be derived from using analytics in such cases will range from increasing the component life, reducing overall repair spend to having a higher degree of reliability. Page 2 of 3
Spring 2014 A few Criticisms
Business Analytics
Rushabh Vedia
The application of analytics fosters overall profitability for the fleet provider but there are some critical views that arrest the absolute success for such analytical models. Of the many critical issues some major ones are discussed in the following: The purpose of the data - whether the data collected was intended to answer accounting issues and not operational issues. The quality of the data – whether the data is accurate or updated to obtain correct maintenance cycles of the components. The big data – whether does the data auto corrects itself as many equipment having in built auto detection and repair technology induced and this deletes any repair work scheduled in the model.
The Conclusion It is very essential for companies to make optimal use of analytics to reduce the cost involved in the fleet industry and to increase efficiency of maintaining those fleets. Companies that don’t cater to such solutions will be less competitive and will have adverse effects in the long run. Analytics help manage data, ensure quality, streamline processes and recover costs. Fleet providers using analytics would have an upper hand in focusing on new business opportunities because of the steep reduction in time which was earlier directed towards maintenance of fleet data. It is worthy for industries of similar scope to initiate analytics into their core business models.
Reference
Kaduwela, V., &Kaduwela, R. (2012).Leveraging analytics in transportation to create business value.
