Service Station Planning Optimization in UAE - 2

This company is the national oil company of Abu Dhabi, the state-owned oil company of the United Arab Emirates, with access to the country’s oil and gas reserves, considered to be the fourth largest in the world. In this case, they were looking forward to optimizing their current network, as much as expanding it around UAE. Locatium offers its Omnichannel Retail Network Optimization Solution to cover the specific needs of them and propose them giving recommendations with a high level of effectiveness.


We began by knowing we had to plan and optimize their networks before they make investments by answering the following questions:

  • Where to open new service stations in the UAE?
  • What is the potential of having extra services like convenience stores, car washes, and lube in addition to fuel?
  • What are the catchment areas for the current network?
  • Capacity of each market: how many more stations to open in each Emirate?
  • What is the impact of using mobility data in addition to internal client data?

Tell us the particularities and needs your project needs to be able to implement a plan specially designed for you.

Project development and the final score

To figure this out we bing into Locatium’s Machine Learning Model Algorithm based on data and artificial intelligence, our data repository, and internal sales of our client data. After, we separate the retail potencial model in to different part, in the road and large strees, and in the commercial zones with high level of activity/dwelling. Through this, we could get predictions about customers’ behavior.

Choosing the right data is essential for getting effective results to solve the questions correctly. That’s why we based our model on:

  • Tracking of billions of mobile devices (which we use to do dwell analysis, home location identification, visitation analytics, etc.)
  • Tracking of billions of vehicles (which we use to do traffic on the road network, vehicle home location identification, etc.)
  • Consumer Data (purchasing power, consumer profiles by category, etc.)
  • Advanced Demographics (gender, income, education, nationality, etc.)
  • Real Estate insights.
  • Credit Card insights.
  • Social Network Insights.
  • Points of interest, including the location of competitors.

Finally, our clients could see the value of our tool and the importance of the recommendations we offered them. Also, they could evaluate the quality of our data sets through the results we offered, and the adequate mathematical and statistical combination of those data sets because we answered all the initial questions they were interested in knowing.

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