Profitability Enhancement for Fuel Delivery Vehicles, Using Geoanalytics, Data Mining and Kafka Streams


Authors : Elham Shakeel Inamdar, Aashna Sanjay Sethi, Anaisa Sam, Swamy, Alaukik Pawan Chauhan

Volume/Issue : Volume 4 - 2019, Issue 10 - October

Google Scholar : https://goo.gl/DF9R4u

Scribd : https://bit.ly/37OHSBj

Fuel tanker trucks carry petroleum in its crude oil form from refineries to Retail Outlets (RO) when no other means of transport such as pipeline or rail is available or feasible. These trucks transport fuel regularly. The regular routes being followed by these trucks are inefficacious and obsolete. The truck driver can have a considerable impact on fuel consumption. On average, fuel equates to about 30% of the total operating costs. With reduced fuel usage by optimal routing, operating costs and travel time can be reduced. Also, these trucks require fuel for transportation, which is not readily available at the RO stations for refueling. Possible high Retail Selling Prices (RSP) at certain ROs may affect the revenue overall. In this paper, we present a strategy to implement profitability enhancement for these customers (fuel tanker trucks) with the use of GeoAnalytics, Kafka streams, and Data mining. From the dataset acquired we provide the truck drivers the shortest and most efficient route, via a portal. Observing the previously recorded fueling patterns of these trucks, the RO stations are alerted, via email, to maintain enough fuel for re-fueling these trucks, which is not readily available. The truck drivers are informed which RO station is the nearest available for refueling according to varying RSPs. This strategy results in saving fuel, revenue and travel time of these truck drivers.

Keywords : Retail Outlets(RO), Geoanalytics, Retail Selling Prices (RSP), Kafka Streams, Optimal Routing, Fueling Patterns.

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