Rivelo

𝐂𝐚𝐬𝐞 𝐬𝐭𝐮𝐝𝐲 – 𝐃𝐚𝐭𝐚 𝐪𝐮𝐚𝐥𝐢𝐭𝐲 𝐚𝐧𝐝 𝐠𝐨𝐯𝐞𝐫𝐧𝐚𝐧𝐜𝐞 𝐢𝐦𝐩𝐫𝐨𝐯𝐞𝐦𝐞𝐧𝐭
When Finance and the Business share the same numbers, decisions move faster.

In a mid-sized company, Finance and Business teams were using different definitions for key metrics (margin, revenue, cost-to-serve).
This led to:
- meetings focused on reconciliation rather than decisions
- loss of trust in reports
- delayed decision-making

The introduction of a single model, with shared and traceable definitions, reduced the time spent on reconciliation by 30%.
The most significant outcome was cultural: fewer discussions about the data, more focus on action.

How much time do you spend today aligning numbers before making decisions?

Forecast that changes every month (but never improves)

Problem
The forecast is updated frequently, but accuracy does not increase.
The CFO receives different numbers, with no gain in reliability.

Why it exists
* Models based on historical extrapolations
* Manual and reactive updates
* No distinction between structural variation and operational noise

Real consequences
* Loss of trust in the planning process
* Decisions made “by intuition”
* Ineffective short-term steering

Conceptual solution
Move from a static forecast to a causal, adaptive forecast based on real operational drivers, not just historical time series.

Truly digitising forecasting and control

Many organisations talk about digitalising forecasting.
Few manage to make it truly operational on the real business drivers.

This review highlights a key point: a single platform that connects forecasting, control, and key drivers, reducing fragmentation and manual work.

The value lies not only in the technology, but in the ability to make the process continuous, automated, and clearly readable for finance.

👉 Discover Rivelo Platform

Case study: reducing the close isn’t just about efficiency.
It’s about deciding earlier.

In a multi-entity company with a largely manual close, the monthly closing process took an average of 12 working days.

The main bottlenecks were always the same:
- repetitive manual reconciliations
- multiple versions of the same files
- controls performed “downstream” instead of “upstream”

We worked on two simple (but decisive) levers:
- process standardization
- finance data integration

Result: closing reduced to 7 days.
–40%.

But the most interesting part isn’t the number.
It’s that the data stopped being “just correct.”
They became useful.

Because they arrived while decisions were still open.

How much do closing timelines impact your ability to react today?