Driver-Based Planning with SAP Analytics Cloud

The days when finance could afford to plan the business based solely on historical data and gut feelings are long gone.

There was a time when business environment and industry change was evolutionary. This gave businesses the luxury of time to react and course correct before it was too late. However, led by a perfect storm of societal forces and disruptive technologies, this rate of change has accelerated exponentially. Consider this: the average life span of a Fortune 500 company in 1935 was 90 years; in 2020, the life span had reduced to just 18 years. So, it’s not surprising that in an increasingly fast-paced and ever-changing business environment, the speed, agility, and flexibility of today’s planning and forecasting process are becoming necessary attributes for any organization to not only survive but also to grow and thrive. It’s imperative for a business planning framework to be able to answer two main questions:

• How are we going to grow as a business?
• Will our growth produce the desired results?

To answer these questions, it’s necessary to understand the key value drivers that impact your business.

Driver-based forecasting is one such business planning technique that focuses on these value drivers that impact business performance and then connects them to the financial results of the organization. It’s important because this type of forecasting emphasizes the relationships among the financials, operational metrics, and people. Metrics don’t exist in isolation on financial statements; they are determined by causes and effects within the entire business. Drivers will likely range from macroeconomic indicators to industry-specific metrics to unique ones for a specific company. The key here is that the driver data be measurable for use in mathematical models and reflect how an enterprise would respond to different core variables. External drivers are situations, events, and so on that occur outside of an organization and not under an organization’s control, whereas internal drivers are events that occur within an organization and basically under the organization’s control. The figure below depicts a few typical external and internal business drivers impacting different industries.

Driver-based planning is about modeling concepts. It’s based on the idea (or structure) that line items within a plan have an inherent units/rates/amounts architecture that is the basis for linking the activity drivers with the organization’s financial results. Thinking about driver-based planning using units, rates, and amounts makes it easier to grasp the causal relationship, as follows:

• Identify the drivers: Drivers are operating activities that you can measure and can be any number of things, such as units of product, customers, installations, deliveries, transactions and so on. If an activity can be thought of as a unit of something, then it’s a candidate to be a part of the activity driver model.
• Interrelationship between the drivers: Very often, the activity drivers of the business are related to each other. You could have a scenario where 70% of the customers who buy new bikes also end up purchasing helmets. The formula then for forecasting the units of helmets sold = 70% × number of bikes sold.
• Line items linked through the drivers: Driver-based modeling involves breaking down and identifying what the key contributing factors are to individual line items, whether they’re an expense, revenue, or cash flow. Very often, these contributing factors or activity drivers are the same across different line items.

To illustrate further, let’s consider an example of marketing forecast for a call center and see how the driver “number of new customers” is used to forecast multiple lines namely “operator salaries”, “operator payroll taxes”, “operator benefits”, and “operator workstation asset” on the profit and loss (P&L) statement:

• Number of new customers × Calls per customer = Total calls
• Total calls × Length of calls in hours = Total call hours
• Total call hours ÷ Operator utilization % = Operator hours
• Operator hours ÷ Hours per month = Number of operators
• Number of operators × Salary rate = Operator salaries
• Operator salaries × Payroll tax rate = Operator payroll taxes
• Number of operators × Benefits rate = Operator benefits
• Number of operators × Cost per workstation = Operation workstation asset

The entire premise of the driver-based framework lies in the causal relationship between the operational drivers and the financial line items connected via a simple mathematical expression or a more advanced logic. While such relationships can be modeled on a spreadsheet, it becomes tedious and difficult to maintain as the complexity of the logic and the size of the data set you’re working with increases. Enterprise performance management (EPM) tools such as SAP Analytics Cloud provide FP&A professionals with the arsenal to embed such mathematical expressions and advanced logic within the tool. This figure exhibits the options available in SAP Analytics Cloud for modeling such calculations.

Two broad categories of methods are available in SAP Analytics Cloud to apply logic to data:

• On-the-fly calculations: In this category, calculations are performed at runtime, and the calculation results aren’t persisted on the model. Account member formulas and calculated measures are two such options available in SAP Analytics Cloud to model calculations without persisting data. It’s important to note here that calculated measures are only available with the new model and not with the classic account model.
• Persisted logic calculations: Within this category, calculation results are persisted within the model. It’s often used for more data-intensive calculations. Data actions and allocations are two types of persisted logic calculations available in SAP Analytics Cloud. Copy, cross-model copy, and advanced formulas can be added as a step of a data action.

Editor’s note: This post has been adapted from a section of the book SAP Analytics Cloud: Financial Planning and Analysis by Satwik Das, Marius Berner, Suvir Shahani, and Ankit Harish.