The Challenges of Creating a Driver-Based Planning System

As the office of finance has evolved in recent years, it has transitioned from being an isolated department that oversees the financial health of the business to serving as the steward of critical information about the company’s past, present, and future.


In years past, such data was stored in company databases and was difficult to access, let alone effectively synthesize and analyze. But technology now gives finance the ability to manage and harness such data in ways that are cost-effective and highly practical. As a result, we are now seeing rapid evolution of financial processes. One example is the incorporation of drivers into financial, operational, and strategic planning. Ideally, these drivers become the basis of important parts of the financial and operational plan and become important indicators of a company’s well being. But this approach does present a challenge, which I’ll explore in this blog post.


The overarching challenge of driver-based planning is to identify the most relevant drivers. One commonly accepted guideline is that 80% of results are correlated to 20% of identified drivers. The 80:20 rule gets applied to many things in life. In the case of finance, it comes from the understanding that even the most complex businesses are generally explained by only a handful of drivers. This is true in some obvious examples, such as low fuel prices explaining increased profitability of a logistic- focused business or newly hired staff completing an important set of tasks. But would these examples be among the 20% of drivers that explain the 80% of business results? Only if they have an impact on relevant results in multiple areas of a business or operations. Often, drivers are only significantly impactful on a single area of operations or the business.

So, the next challenge is to identify what drivers fit into that 20% category. Finding these drivers will require they possess some specific qualities. First, they must be correlated with the results they are seeking to explain. While any statistician would warn that a correlation does not necessarily mean a relationship (it may just be a coincidence or explain a different unrelated relationship), clearly a clear correlation is a good start.


You can determine a clear correlation by considering these two factors. First, you need to determine if there is enough reliable data to show evidence of the correlation. A key driver must be quantifiable at some level; ideally it should also be available or derivable in the current data repository. Second, experienced colleagues and stakeholders need to agree that the relationship between the driver and the area of the business is impactful and significant. If there is no agreement, then the driver should be considered only advisedly, pending further concrete evidence that it is significant to the business.


Another characteristic of a key driver is that its impact and use may be unique. Some drivers only explain the past. That is, they are an expression of a lag (such as how the average aging of accounts receivable reflects lower realizable sales and bad debt). Such drivers certainly provide useful insight, but only for the current and past business. Other drivers are leading. They anticipate results and are correlated with future outcomes over a range of time. For example, marketing events or new hires of sales staff will tend to impact future sales at a given time in the future. Expect that there are many such unique drivers. The challenge is not just to find them, but to then qualify the best ones. These drivers also must be seen as significant by knowledgeable colleagues who work within the organization (usually lines of business) and they must be accessible in existing data sets. Furthermore, business stakeholders must take ownership of the drivers, as they will likely evolve over time. For instance, think of how drivers anticipating sales based on periodical advertising have become eclipsed by new drivers related to sales generated through online advertising and social media.


Therefore, the development of driver-based planning systems is yet another example of the importance of strong finance and business partnerships. The identification and management of key drivers clearly requires the knowledge, cooperation, and support of business stakeholders.

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