Days sales outstanding (DSO) is an important cash flow metric often monitored by Chief Financial Officers (CFOs) and finance departments. Days sales outstanding represents the average number of days it takes a company to receive payment for a sale. A high DSO number suggests that a company is experiencing delays in receiving payments. If a company is spending money faster than collecting it, it could wind up with a cash flow problem. In general, it is best to reduce DSO and achieve the lowest possible number.

In my experience, CFOs and finance departments have a “feel” for what the appropriate level of DSO should be, based on historical context and experience.

## How DSO is Monitored and Calculated

DSO is often determined on a monthly, quarterly, or annual basis. DSO is calculated by dividing the total accounts receivable for a given period by the total sales for the same period. This result is then multiplied by the number of days in the period (i.e. 30 days if calculated monthly, 90 days if calculated quarterly, etc.).

Typically, most finance departments have DSO calculated on a spreadsheet or in an automated report or dashboard. Unless the DSO of that particular period seems out of the ordinary, the analysis ends there. As you'll see below, whether you’re looking at the number or a graph created on a spreadsheet, it doesn’t provide much helpful information.

## How Most Finance Departments “Monitor” DSO For Irregularities

I took some historical data from a real, publicly traded company and looked at the DSO data reported on a quarterly basis.  When looking at the data, it had a range of 57.905 to 70.680, which means that unless the next DSO was out of that range, it probably wouldn’t raise any questions. The problem with that is (as my boss says) “you can drive a truck through” that range, so being “in the range” is basically meaningless.

Above is a typical view in a spreadsheet (or KPI Dashboard). Can you spot a trend or an outlier?

## Minitab Can Provide a Better Way to Monitor and Analyze DSO

Using Minitab Statistical Software, I was able to take the exact same data set and run a Time Series Analysis (using the Decomposition command to account for quarterly seasonality). In doing so, I got two great insights: first, the Time Series Decomposition Plot highlighted a trend that DSO was being improved. The finance team may have been proactively implementing processes to collect faster or got lucky. Either way, the trend is showing DSO declining, which is positive.

Now compare the Minitab default graph below to a typical spreadsheet graph (that has even been edited to – unsuccessfully – paint a clearer picture). It’s clear the Minitab graph highlights a trend that might not be noticed on a spreadsheet graph.

The second insight is understanding the seasonality of collections. The graphs below clearly highlight that in the fourth quarter the company collected on receivables significantly faster than any other quarterPerhaps there is something that the team is doing that could be applied to the first three quarters of the year? Regardless, understanding the seasonality helps recognize when DSO is trending in the wrong direction before it’s too late. From a spreadsheet perspective, seasonality can be seen as well, but other than a clear indication that December has the lowest DSO, there is not much information about the first three quarters.

## Tell Me Something I Don’t Know: How Do I Prevent a High DSO

A CFO might read this and challenge me that CFOs should intuitively understand seasonality and which way DSO is trending. Perhaps. Going through this exercise not only confirms (or enlightens) the finance department, but also helps prevent failures in the future.

In addition to viewing the trend of DSO, the finance department should maintain a control chart of DSO.  Control charts, most commonly used in process monitoring, indicates when your process is out of control and corrective action is necessary. There are many different types of control charts for different types of data and processes, so I used an individual chart of my seasonally adjusted data because the quarterly DSO is effectively an average over the course of a quarter.

The control chart above highlights that observation point 7 (the September quarter where DSO was above 70) was “out of control.”  As we see by the trend, actions were clearly taken not only to mitigate the problem, but also make what appears to be long-lasting improvements.

The control chart also adds context. Early on, a rising trend signaled a problem that needed to be addressed.  Interestingly, after its lowest level (observation 10), the company once again saw a rising trend for DSO. The control chart demonstrates that this trend was moving towards the mean rather than signaling another risk.  If anyone were to ask about this new, rising negative trend (e.g. the CEO, investors, the CFO), the Control Chart is an excellent tool to demonstrate that there were no reasons to sound the alarm.

Once again, compare the control chart to the one created by a spreadsheet. Which one provides more insight?

## These Easy Analyses Took Less Than Five Minutes… And Can Be Applied to Many Other Metrics!

As finance teams tend to live in their financial systems and spreadsheets, there is likely some statistics anxiety (which is a real thing by the way!) holding you back. I can assure you that not only is the analysis quick and simple, it can also be set up and automated on a dashboard using Minitab Connect. It can also be applied to other key financial metrics, so you get leverage from your newfound statistical knowledge.

Whether you’re a start-up where cash flow is critical to surviving every day or you’re a large company with significant cash flows, catching trends and problems before they exist will surely save money and time.