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