Understanding Temporal And Spatial Demand Fluctuations
This is an energy production, commercialisation and distribution company in the Iberian Peninsula, with more than 250 petrol stations, geographically distributed throughout the country and with an integrated value chain.
The challenge was to understand the business performance and the impacts of the geospatial interaction of the current petrol station distribution network. Four key concerns were highlighted for the purpose of value creation:
- Optimisation of a location-based pricing strategy
- Estimation of the competition and market cannibalisation effect on each of the petrol stations
- Sales forecasts, by fuel type, for a new service station to be placed anywhere on national territory for subsequent optimisation of the network's expansion
- Capacity to interact and visualise the solutions developed.
One of the main difficulties in solving the problems was the multitude of information sources which existed locally and in different formats - mostly unstructured.
The solution developed to meet the above challenges also has as a critical stage - the knowledge of the multitude of factors that condition the spatial behaviour of the fuel distribution network.
The first stage of the geospatial interactions of the fuel distribution network was to structure, organise and process the information, storing it in a database (e.g. differentiated performance of each service station, temporal and local evolution of prices, resources and external environment).
The solution was materialised through an interactive information visualisation application which allowed for the extraction of unique business insights (e.g. pandemic impact, seasonality and the effect of temperature on the consumption of certain products), and which introduced, in an integrated manner, the temporal and spatial domains.
Regarding the geolocation intelligence component, the solution presented allowed the team to cluster the different posts according to the offer, the location characteristics and the exogenous surroundings, estimating their catchment area and efficiency in relation to the estimated potential.
The model built allowed several relevant outputs to be obtained for the organisation and for the strategic development of the business. The client was therefore equipped with the capacity to forecast the sales potential (by type of product) of each location throughout the national territory, while identifying the effects of market cannibalisation and competition, and thus enhancing its expansion plan.
As for the pricing model, the critical aspects were identified, per location, to match supply to demand (e.g. traffic flows and types, competition characteristics, available supply characteristics), in order to optimise the pricing policy.
The solution developed allowed the company to understand, in detail, the effects of the interaction of the network with its surroundings and demand flows, according to the temporal and spatial domains.
The results produced have been integrated into a tool for visualisation and interaction, serving the purpose of supporting strategic decision-making, thus guaranteeing a differentiated capacity in an extremely aggressive market.
This makes the complexity of the problem tangible and easier to control for the organisation, allowing the impact of a decision on business performance to be assessed in real-time and according to its distribution throughout the country, thereby optimising the overall performance of the business.
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