Forecasts site demand and optimises pump prices
- Who we serve
- Private Sector
- Category
- Energy, utilities & resources
- Value
- Revenue
- Human role
- Human monitors, can halt
- Maturity
The problem
Without site demand forecasting & fuel pricing, fuel retail teams make these calls on averages and instinct, because POS, competitor prices are not joined up, and the value in the data goes uncaptured.
How it works
Site demand forecasting & fuel pricing forecasts site demand and optimises pump prices. The approach relies on ML, pricing. It draws on POS, competitor prices and is typically owned by fuel retail teams. The main constraint is fair-pricing regulation. Where the human comes in: automated within rules; pricing managers override. It is in production at multiple organisations; examples include Shell and BP retail; Kalibrate.
Where the human comes in
Automated within rules; pricing managers override
What it needs
POS, competitor prices
What to plan for
Fair-pricing regulation
In the field
Shell and BP retail; Kalibrate
Public deployments by other organizations, listed for context. Not Sphere projects.