Rivelo

Endless variance analysis, little value.

Many FP&A teams spend weeks explaining variances.
The issue is—they often stop there.
They analyze what happened,
but rarely get to why it happened
and, more importantly, what to do next.

The result?
The same mistakes, limited learning, and constant pressure on the team.
The problem isn’t the analysis itself,
but an approach that is still too ex-post and descriptive.

What’s needed is a shift toward causal variance analysis, connecting numbers to operational drivers
and enabling concrete decisions.

That’s exactly what Rivelo Platform enables:
turning variances into actionable insights, not reports to archive.

Many companies still treat forecasting as a purely numerical exercise.

Numbers go up.
Numbers go down.

And when variances appear, the analysis often starts after the fact, trying to explain results that have already happened.

In one company we analyzed, this was exactly the situation.
Forecasts were updated regularly, but the drivers behind the numbers were not clearly structured. Variance analysis focused on *what changed*, not *why it changed*.

The shift came with the adoption of a driver-based planning model.

Instead of adjusting numbers manually, the financial model began linking results directly to operational drivers: volumes, pricing, cost structures, and productivity variables.

This allowed the FP&A team to:
• connect financial results to real business levers
• explain variances through clear cause-effect relationships
• simulate scenarios in minutes instead of rebuilding forecasts

The outcome?
A forecast that is no longer just a reporting exercise, but a real decision-support system.

This is exactly the type of approach that platforms like RIVELO enable: transforming planning models into structured, driver-based environments where variances become explainable insights, not just numbers to justify.

📌 In your organization today, are variances truly explained — or simply measured?

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.

The budget is approved, but after 3 months it’s no longer useful

Problem
The annual budget quickly becomes obsolete.

Why it exists
* Rigid top-down planning
* Assumptions made once a year
* Limited integration with operations and the market

Real consequences
* Budget used only as a constraint, not as a decision-making tool
* Continuous variances that are “explained” but not governed
* Waste of time on formal exercises

Conceptual solution
Evolve toward continuous planning: less emphasis on the annual number, greater focus on dynamic, continuously updatable scenarios.

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

𝐅𝐫𝐨𝐦 𝐜𝐨𝐦𝐩𝐥𝐞𝐱𝐢𝐭𝐲 𝐭𝐨 𝐫𝐞𝐬𝐮𝐥𝐭𝐬

When a Planning and Control project truly works, the clearest signal is time.
Not months. Not quarters.
But weeks.

This testimonial tells a concrete story: clear objectives, rapid implementation, and measurable results at group level.

It is in these contexts that a CPM platform proves its value:
not as a theoretical exercise, but as an operational tool that supports finance in everyday decision-making.

👉 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?

𝐈𝐧𝐜𝐫𝐞𝐚𝐬𝐢𝐧𝐠 𝐟𝐨𝐫𝐞𝐜𝐚𝐬𝐭 𝐟𝐫𝐞𝐪𝐮𝐞𝐧𝐜𝐲 𝐰𝐢𝐭𝐡𝐨𝐮𝐭 𝐢𝐧𝐜𝐫𝐞𝐚𝐬𝐢𝐧𝐠 𝐰𝐨𝐫𝐤𝐥𝐨𝐚𝐝
𝐢𝐬 𝐩𝐨𝐬𝐬𝐢𝐛𝐥𝐞, 𝐢𝐟 𝐭𝐡𝐞 𝐰𝐚𝐲 𝐭𝐡𝐞 𝐟𝐨𝐫𝐞𝐜𝐚𝐬𝐭 𝐢𝐬 𝐛𝐮𝐢𝐥𝐭 𝐜𝐡𝐚𝐧𝐠𝐞𝐬.

In many organizations, forecasts are updated only a few times per year
not by choice, but due to the effort required.
In this case, each forecast update required approximately 10 person-days.
An unsustainable process for a forecast meant to support timely decisions.

With the support of Rivelo, the company:
- automated repetitive activities
- introduced driver-based modeling
- transformed forecasting into a continuous and repeatable process

Outcome: Transition from quarterly to monthly forecasting, with a 25% reduction in total effort.
The main benefit was not only operational efficiency.
It was a tighter and more consistent alignment between plan and operational reality, enabling decisions based on always up-to-date figures.

How frequently are you truly able to update your forecast today?

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📊 Pianificare una volta l’anno non basta più.
Con Rivelo Platform, le aziende passano da budget statici a rolling forecast aggiornati in base a eventi reali.

Un cliente ha aumentato l’accuratezza previsionale dal 70 % al 92 %, riducendo del 25 % l’errore medio assoluto (MAE).
Forecast continuo significa decisioni tempestive e dati sempre aggiornati: l’unico modo per restare competitivi in un mercato che cambia ogni mese.

𝐁𝐞𝐧𝐜𝐡𝐦𝐚𝐫𝐤 𝐝𝐢 𝐬𝐞𝐭𝐭𝐨𝐫𝐞: 𝐝𝐢𝐟𝐞𝐧𝐝𝐞𝐫𝐞 𝐢 𝐦𝐚𝐫𝐠𝐢𝐧𝐢 𝐜𝐨𝐧 𝐝𝐚𝐭𝐢 𝐞𝐬𝐭𝐞𝐫𝐧𝐢

I CFO che integrano benchmarking di settore, dati macro e tendenze mercato nei loro modelli finanziari vedono meno scostamenti inattesi nel margine.

Con Rivelo Platform, puoi importare dati esterni di mercato, incorporare benchmark, confronti tra pari, e costruire dashboard che segnalano quando sei sotto la media di settore — così puoi correggere rotta.

Caso concreto: azienda tecnologica che ha scoperto di avere costi di licenza software superiori del 15 % rispetto a benchmark, ha negoziato nuovi contratti, guadagnando margine senza ridurre qualità.