DBM provides FP&A teams with 10 key benefits over typical forecasting methodologies:
1. Better accuracy. DBM improves the accuracy of forecasts, allowing the organization to ignore unnecessary items and focus on material drivers. Companies can also take the next step and run predicative analytics by looking at different inputs to the model. They can figure out what factors are going to move the business in either direction. According to David Mann, director of financial planning and performance at insurer Tufts Health Plan, that’s particularly helpful in areas of great volatility.
2. Data integrity. The number one benefit of DBM is trust in the numbers, explained Pras Chatterjee, senior director of product marketing at SAP for Enterprise Performance Management. “Amongst the worst things for finance is explaining numbers in absolute terms without further details,” he said. “With driver-based models, finance can say not just what’s happening but also why it is happening.”
3. Higher frequency. DBM makes it easier to forecast and plan more frequently by using technology to input KPIs into models that generate financial forecasts. It’s tied back to KPIs and strategy, thus it’s a unified way that gets people to be more focused on key drivers of business success.
4. Speed of decision-making. DBM lets companies make decisions, fast, by providing visibility into what’s moving financial results. Companies can begin to understand what levers to pull and their likely effect. If companies see a change in a driver, they can buy the most valuable thing: time to respond.
5. Better business support. Driver-based modeling is the best way for finance to support the business. Increasingly, finance is becoming a better partner to the business. The focus on business drivers allows finance to invert the pyramid—instead of spending two-thirds of their time on transactions and one-third on value-added analysis to support the business, FP&A can spend two-thirds on value-added analysis.
6. Ability to plan around key drivers. According to Philip Peck, vice president financial transformation at Peloton, focusing on the operational drivers enables an organization to understand, plan around, and influence the critical elements that have the greatest impact on financial performance.
7. Higher efficiency. Adopting and implementing driver models significantly increases both the efficiency and effectiveness of planning, reporting, analysis and driving improved business performance. “From an efficiency perspective, the focus on the critical few drivers enables organizations to move away from the very detailed accounting-centric G/L account mindset that often consumes significant time, effort and cycle time with limited business value,” Peck said.
8. Getting everyone on the same page. DBM gets all the different functional leaders on the same page; finance, marketing, sales, manufacturing, distribution, etc., can see the impact of their activities on financial results.
9. Developing a rolling forecast. Driver models are also an essential foundational component for establishing a rolling forecast framework and significantly reducing the time and effort associated with the traditional annual planning process. As more companies peek around the corner at 12 months, that’s becoming an essential factor.
10. Changing the conversation. DBM forces the discussion to turn to activities and operational driver-based conversation, i.e., focusing on the value chain of the business since drivers are tied to that value. According to Peck, “The conversation completely changes, from the typical financial results discussion to what is impacting those drivers and how we can improve those opportunities.”
Get the right tools
Technology is by no means the only step companies need to take to implement DBM. But experts and practitioners agree it’s a critical one. It can make a big difference in the sophistication of the models and how frequently they can be run for optimal results. While many companies can and do build models and run scenario analysis in Excel, newer technologies increase the accuracy and sophistication of the model and thus the confidence in the forecast.
According to Chatterjee, [more advanced] technology enables deeper modeling. Companies can store their drivers and data elements that are part of each scenario analysis, which allows FP&A to calculate the results on the fly. Plus the models need to be dynamic. The business is changing, drivers are changing, and mathematical relationships are changing. The planning model must be able to keep up. It also needs to be able to incorporate much more, and much more varied, types of data.
While sophisticated modeling capabilities may not be a prerequisite, “companies that don’t have a data warehouse will find that it takes a lot more time and they’ll fall behind the competition because they won’t be able to keep up and make decisions and change course quickly enough,” concluded Vic Datta, CEO of consulting firm Resilicore.