When creating a driver-based model it is important to produce one that is not overly detailed or complex, but one that is accurate and actionable. Avoid complexity by adding variables if they do not provide analytical benefit. Starting with the chart of accounts is not a good idea. The model should focus on key performance drivers. 

When I first came across the term driver based planning and forecasting I was confused. As an ex-investment banker having joined a Finance team the concept of drivers when talking about a forecast or plan was simply assumptions. Why was it not called just that? Assumptions! Investment bankers have been building models with assumptions ever since the first model was built and a corporate transaction was negotiated.

We are entering the era of digital FP&A where human and artificial intelligence work hand in hand in order to achieve better analytical results. The new world of FP&A requires on-demand continuous planning process where various business scenarios can be played almost in real-time. Both driver-based planning and FP&A predictive analytics are essential tools for implementing flexible dynamic planning and forecasting process.

Artificial intelligence is currently disrupting your job – whether you know it or not. For financial planning and analysis professionals, artificial intelligence is an opportunity to improve business models and offer better strategic insights.

Normally, integrating actuals into the planning cycle is not an easy task. Often financial and operating results are spread across multiple databases and actual results and plan detail are at different levels. All of this makes meaningful Performance Evaluation difficult. 

This case study helps understand why a driver-based analysis approach is one of the winning methods for the effective decision-making process.