For the controlling team at a leading Spanish brewer, every month end close started the same way: dozens of emails, dozens of files, and no way to process any of it automatically. With over 50 distributors sending sell-out data in completely different formats, the team faced a monthly flood of reports that had to be handled one by one - manually parsed, manually harmonised, manually loaded into the BI system.
It wasn’t a resourcing problem. It was a structural one.
The Challenge
Distributor reporting sounds straightforward until you’re doing it at scale. Each of those 50+ distributors had their own template, their own column names, their own quirks. Some sent Excel files. Some sent CSVs. Field names didn’t match. Date formats varied. There was no standard, and there was no shortcut.
Before a single number could reach the BI system, someone on the four-person controlling team had to open every file, figure out what they were looking at, and manually map it to the right structure. Then check it. Then load it.
That process consumed more than 120 hours every month - roughly 25% of the team’s total capacity. Not 25% of one person’s time. A quarter of the entire team’s available hours, every single month, spent wrangling spreadsheets instead of delivering insight.
The consequences were predictable. Reports were delayed. Visibility into real-time sales performance was limited. Management got numbers late, sometimes inconsistent, and always slower than the business needed them. The team knew the problem - they just couldn’t solve it without fundamentally changing the process.
This kind of bottleneck sits right at the intersection of two critical finance functions: the month-end close process and distributor sell-out data management. When one is broken, the other suffers.
The Solution
With Transformance, the brewery replaced the manual pipeline entirely. Instead of emailing reports and waiting for someone to parse them, distributors now upload directly through a secure portal. From there, AI-driven parsing takes over - automatically recognising and mapping fields regardless of how each file is structured.
The harmonised data flows directly into the brewer’s BI system. Built-in quality checks validate data integrity before anything gets published, so the team isn’t just saving time - they’re also getting cleaner data with fewer errors slipping through.
The first version was live in two weeks. Not two months. Two weeks - integrating the top 20 distributors and automating over 95% of the manual process from day one.
“Transformance was ultra fast at solving our financial data challenges.” - Commercial Finance Controller
Onboarding additional distributors now takes hours, not weeks. The framework is built to scale.
The Impact
Automating the data pipeline freed up 120 hours per month. For a four-person team, that’s not a marginal improvement - it’s a fundamental shift in what the team can do.
Time that used to go into spreadsheet wrangling now goes into actual analysis. Management gets faster access to consistent sell-out insights. And the financial case is clear: estimated productivity gains of €40-60k per year.
Plans are already underway to extend automation into rebate calculations and full profitability waterfalls - areas where the same data-harmonisation challenges tend to appear.
What This Means for O2C and AR Teams
Sell-out data automation isn’t just a reporting efficiency story. It sits directly inside the order-to-cash cycle.
Distributor sell-out data is the foundation for trade promotion validation, revenue recognition, and deduction management. When that data arrives late or inconsistently, everything downstream gets delayed - including AR processes that depend on accurate, timely revenue figures.
Faster sell-out processing means a faster month end close. It means deductions can be validated sooner, revenue can be recognised on the right timeline, and AR teams aren’t waiting on controlling to finish reconciling distributor files before they can close their own books.
For O2C teams managing complex distributor networks, this kind of sell out data automation isn’t a nice-to-have. It’s infrastructure.
Frequently Asked Questions
What is the biggest bottleneck in the month end close process?For most enterprise finance teams, the biggest bottleneck is manual data collection and reconciliation. When data arrives in inconsistent formats from multiple sources - like distributor sell-out reports - the time spent normalising it before it can be used delays every downstream step in the close.
How does distributor reporting affect month-end close timelines?Distributor sell-out data feeds directly into revenue reporting and trade promotion reconciliation. If that data is late or requires significant manual processing, it pushes back the entire close cycle - including AR reconciliation and management reporting.
Can AI really handle unstructured financial data from multiple sources?Yes. AI-driven document parsing can recognise and map fields across different file types and formats without requiring distributors to change how they send data. The brewery in this case study automated over 95% of its manual process within two weeks using this approach.
What’s the ROI of automating month-end close tasks?ROI varies by team size and process complexity, but productivity gains tend to be significant. For this brewery, automating distributor reporting delivered an estimated €40-60k per year in productivity savings - on top of faster close cycles and better data quality.
Ready to automate your distributor reporting and month-end close? Book a demo to see how Transformance can deliver similar results for your team.
