Data foundations and a single version of the truth
One definition for each number, one source, and a data model finance and operations both use.
You get
- A KPI dictionary with owners
- A clean master data plan (customers, products, costs)
- A reporting layer people stop arguing with
Systems and ERP value
Get the value you paid for from the systems you already have, before buying more.
You get
- A review of how the ERP is really used
- A short list of fixes, ranked by £ and hours saved
- Clear advice on when a new system is worth it
Automation
Take the rekeying and copy-paste out of the month, so skilled people do skilled work.
You get
- Automated reconciliations and data feeds
- A pack that builds itself
- Hours saved, measured each month
AI in finance, used sensibly
AI where it saves real time today, on clean data, with a person signing off and controls your auditors accept.
You get
- Drafted variance commentary
- Matching and anomaly flags in reconciliations
- Invoice and document extraction
FP&A, forecasting and S&IOP (sales, inventory and operations planning)
A rolling forecast built from the sales pipeline and the operations plan, so the numbers and the factory tell the same story.
You get
- A driver-based rolling forecast
- Forecast accuracy, tracked each month
- Scenarios the board can use
Business partnering and MI
Finance people working alongside sales and operations, with MI built for the decisions each team makes.
You get
- A partnering model with clear roles
- Daily, weekly and monthly MI
- Product and customer margin on demand