Driver-based modeling is fundamentally a planning method where, instead of trying to predict or report the outcomes of a certain business path, you dig deeper into your data and understand what variables actively affect performance and KPIs. This accurate, agile planning method is also fully scalable. Compare that to the overly complex and time-consuming models that rely on historical data and assumptions alone, often resulting in tedious, extensive planning periods and forecasts that get thrown off by out-of-date assumptions as the business grows.
It’s important to truly understand your business model and what leads to success before identifying general drivers—but once you’ve figured that out, you have a scalable tool that works logically, moving from cause to effect.
Driver-based modeling is the closest thing to a universally applicable solution as you get in the finance world. The key to optimal success is to understand your business.
Driver-based planning isn’t a new concept, but most organizations don’t know where to start or how to select the right drivers. You must take the time to fully understand the variables that drive a cost or revenue stream. To pick the right drivers, finance teams should analyze the different variables that impact a specific area. Maybe your year-over-year growth could be used as a driver to streamline the base forecast for an expense account related to office expenses. Percentage of revenue could be used as a driver for a base forecast for various COGS accounts related to specific products or services.
Take the time to look at the data, better understand the business, and collaborate with your business partners to confirm your assumptions and findings.
If you don’t have the bandwidth or are on a deadline, your organization may benefit from enlisting outside help in dissecting your business processes to identify key drivers.
Once you’ve identified your drivers, you can see which drivers have high variability (that is, the ones where a small change will have a big impact) and which have low variability (the ones where you need to work harder to achieve the same result). Identifying these high and low variability drivers will help you to determine your business strategy and process improvements.
Think about the 80/20 rule. With this approach, if you can identify 80 percent of the inputs that drive 20 percent of your P&L, these accounts would be best managed using drivers. This reduces the time spent by the team on planning and allows your team to focus on the other 20 percent of your GL Accounts that impacts 80 percent of the business.
Using variance analysis in your planning process highlights areas that need attention and allows your team to review and adapt accordingly. As you begin your journey, your team should determine at what level of granularity you want to plan and which items have an impact on your bottom-line. Identify the key metrics and variables, and then build out driver logic to capture your findings.
You can still use drivers to help seed a forecast for that other 80 percent of accounts, since the streamlined planning model allows your team more time to research and analyze key metrics, drill into the detail, and align with the business on an approach for the next forecasting period. However, by using variances along the line of 80/20 rule, your team can focus their time where it will have the greatest impact, instead of spending the same amount of time across all accounts or cost centers.
Planners or stakeholders often assume they lose visibility into the important details with driver-based planning, but the opposite is true. While driver-based planning does reduce the complexity of forecasting at each level, it iteratively builds on your existing planning methodology, reducing your variances through time without dumbing the process down.
By introducing driver-based planning, you provide more time for your team to dive into the details and collaborate with their partners, rather than rushing to meet a deadline. With new cloud technology tools, organizations can pair this flexibility with the option to adjust forecasts based on analysis of real-time data.
Establishing drivers ultimately streamlines the forecasting process. The baseline forecast won’t be perfect on the first run, but over time, you can adjust your drivers to reduce the variances in your plan. With the core areas now in focus, your team can dedicate more time to analyzing the data to improve the business processes impacting performance.
There are many cloud solutions that can make your driver-based modeling process faster and more accurate, with access to real-time data and offering enterprise-wide transparency. Spaulding Ridge offers deep knowledge of not only the best platform for your organization, but also how to set up and implement the driver-based modeling process.
To start on the path to agile, accurate and scalable forecasting and planning, contact Rich Quevedo at [email protected].
