Situation
- Client’s Financial Planning and Analysis (FP&A) team executed a monthly forecasting exercise largely dependent on qualitative inputs from sales and executive leadership
- Leadership was concerned with the poor forecast accuracy and wanted to identify opportunities for improvement
- The challenge for the forecasting team was identifying the operational factors driving revenue and costs across various data sets
- FP&A leadership asked SLKone for process and systems improvement opportunities to increase forecast accuracy
Bespoke Solutions
- Executed working sessions with key FP&A stakeholders to understand the process, systems, and data gaps
- Established baseline forecast accuracy metrics based on historical performance
- Aggregated operational data from across the business
- Developed methodology leveraging random forest to identify natural split points which can more confidently predict revenue and cost targets
- Trained a forecast model using vector auto regression based on the variables from the random forest exercise
Leading With Results
- 90%+ predictive accuracy at three months for a significant cost line item based on an operational metric allowing the management team to better control costs before they were incurred
- Defined limits and guard rails that would allow managers to view operational metrics and identify areas of concern for future financial performance