Improving forecast accuracy through real-time scenario modeling

Cervello helped a global healthcare company redesign its financial and operational planning processes to capitalize on key metrics and data to produce insights, improve forecast accuracy, create flexible scenario modeling, and generate overall efficiencies.

400+

hours of FP&A time saved

25%+

improvement in planning process cycle time

1,000+

spreadsheets removed from the process

Situation

The company’s planning processes were being driven exclusively by Excel spreadsheets and were not standardized across organizational siloes. Supporting data was coming from multiple sources and was not consistently defined, and the planning processes did not share data, key metrics, business rules, logic, or assumptions. Forecasts were inaccurate and inflexible, offered limited visibility, and were based on historical dollars spent. There were no insights into customer and delivery volumes, and the process did not offer any guidance on cost growth allowance aligned with regional customer growth projections.

Approach

Cervello helped standardize and align the processes across business divisions, creating a central location for data, calculations, and assumptions for planning processes. After getting universal buy-in from key stakeholders, the company mapped the business drivers across the financial and supply chain planning processes. A new forecasting process was designed based on factors such as customer volume, delivery volume, miles traveled, fuel costs, and lane rates. Cervello also designed a connected planning architecture to support the processes at scale.

Impact

The new planning processes provided real-time updates, top-side adjustments, what-if scenarios, and standardized approaches to executive reviews. Forecast accuracy improved, and the organization was much more confident in the numbers. Rapid scenario modeling of unplanned business events allowed a range of assumptions to be tested and potential impacts to be understood and applied during executive-level decision-making. Drivers of variances can now be understood quickly, and the time to make a decision for corrective action has been drastically reduced.