Energy
Margin by territory, distribution and demand forecasting. A high-volume, thin-margin sector, where a percentage point turns into real money.
Talk to a specialistOn thin margins, half a percentage point decides the year.
Energy and fuel distribution operates on large volume and thin margin. That changes the nature of the problem: it is not about finding the one big loss, it is about seeing small variations across many breakdowns by territory, by product, by channel that together explain the result.
It is exactly the kind of analysis a spreadsheet cannot sustain. When the breakdown is fine and the volume high, the team ends up working with samples and averages, and the distortion lives precisely in what the average hides.
The questions we answer
- Which territory delivers margin and which is being carried by the others?
- What is the real cost to serve each channel, including logistics and payment terms?
- Is the demand forecast accurate enough to size purchasing and stock?
- Where is the pricing policy being applied outside the rules?
- Which customer concentrates volume and which concentrates risk?
The indicators of this industry
The ones that show up in most projects in this industry. The final set is always agreed with you an indicator nobody uses is dead weight on a dashboard.
- Margin. Gross margin by territory, product and channel, margin per customer after logistics, average realised price against policy, spread per transaction.
- Volume and demand. Volume by territory and channel, forecast versus actual, seasonality, price elasticity, market share by territory.
- Logistics and cost. Distribution cost per cubic metre, idle time of own versus contracted fleet, cost per route, transfer losses.
- Customer. Volume concentration, credit risk per customer, ABC curve, retention rate by territory.
The solutions we use most here
- Business Intelligence. Margin by territory, product and channel in a single model, without relying on samples.
- Data Analytics. Demand forecasting and price elasticity analysis by territory.
- Integration & AI Automation. Integrating the sales, logistics and pricing systems into an automatic flow.
How we run it
We start with the margin model: which costs go in and at what level of breakdown, a decision that has to be taken with the leadership before any dashboard. Then we integrate sales, logistics and pricing policy. With the base ready, the analysis by territory usually reveals quickly where the result is being carried by a few strong points.
How we measure results
The most frequent indicators are gross margin gained by territory, demand forecast accuracy and a reduction in out-of-policy pricing.
At a national fuel distributor, this path accompanied a 121% gain in gross margin over two years.
About this industry
Does very high data volume slow the dashboard down?
It does when the model is badly built and that is the most common reason for a slow dashboard in this sector. Correct dimensional modelling and an intermediate data layer solve it: the dashboard answers in seconds even at high volume.
Our pricing policy changes every week. How do we keep up?
The policy comes in as a parameterised source, not as a value hard-coded into the model. When it changes, the dashboard follows and shows where application diverged from the rule in force at that time.
Does demand forecasting work with our seasonality?
Seasonality is precisely the pattern a model learns well, provided there is history from previous cycles. The assessment measures whether the history is sufficient before any promise.
Do you serve generation, distribution or resale?
We work with distribution and resale operations, where the central question is margin by territory and channel. The method is the same; what changes is the cost model.
Which territory is carrying the others?
Thirty minutes of conversation is usually enough to know where to look.
We understand processes before recommending technology. Automation, data, artificial intelligence, and custom software for companies seeking efficiency, control, and scale.