Old-School Metrics Don't Cut It

The standard, baseline metrics that most companies use to measure their working capital performance are days payables outstanding (DPO), days sales outstanding (DSO), and days inventory on hand (DIO). Figure 1, below, shows the calculations typically used to arrive at these metrics.

In aggregate, these measures are often referred to as the cash conversion cycle (CCC = DSO + DIO – DPO). The benefit of these calculations is that they are simple to perform. However, they are also imprecise and so can be quite inaccurate. The CCC is intended to measure the conversion cycle time from the point of receiving goods from a vendor to collecting cash from a customer. But the underlying calculations use single data points that are unable to isolate only the cash-related variables.

Consider DPO, which the typical company calculates using the month-end value for A/P and cost of goods sold. COGS includes non-cash costs, such as depreciation, amortization, and warranty reserves, as well as cash costs (such as wages) that are unrelated to vendor payments. Depreciation, for example, is usually associated with goods purchased many years prior to the current period, so this portion of a company's DPO figure has no impact on current-period vendor payables.

Thus, the ratio of A/P to COGS is not an accurate measure of the aggregate cash cycle time for payments to vendors. In addition, using the month-end value for A/P may distort the metric, depending on when during a particular month the company experiences its highest volume of transactions.

Truing up Working Capital Measures

A much better approach than the conventional CCC calculations is to use the company's actual transactional data to determine the "true" performance in each category of working capital (see Figure 2, below). An organization can determine the actual number of days in each transaction's cycle, such as the number of days from receipt of an order from a vendor to the date that payment is made to the vendor. Two decades ago, CCC calculations using a company's actual transactional data weren't feasible. Today they're within reach of most companies, even if those companies aren't yet taking advantage of them.

Consider how professional sports teams use metrics and data analytics for a huge competitive advantage. Today the NBA uses multiple cameras in every arena to capture player speed, distance traveled, and acceleration. New techniques go far beyond a tape measure to calculate not only how high a player can jump, but also how he lands. This data is used to predict the potential for injury. Recently the NBA created a position called the "director of basketball analytics," as a result of the growth in this area. There is strong evidence across all sports that new approaches to metrics and predictive analytics can improve performance and decision-making.

Likewise, modern technologies enable the corporate world to look at business metrics in a totally new light. Companies that effectively harness their actual transactional data can calculate their true days payables outstanding (TDPO) using the weighted value of each transaction during a particular month, quarter, or year, and the actual number of days the organization took to pay that transaction. This provides a reliable baseline, as well as the means to accurately measure change and to use predictive analytics to drive action plans and assess the results.

To find TDPO, you must have two numbers for each purchase the company has made over a given time period: You need to know the number of days elapsed between the invoice date and payment to the vendor. And you need the weighted value of each transaction, which is to say the proportion of the company's overall payables that is represented by the dollar value of that transaction. For example, if a company's total payables in a month were $100 and a particular payable had a value of $10, then the weighted value of that payable would be 10 percent.

When you have determined the weighted value of each transaction, multiply that number by the number of days elapsed in the transaction. This will give you the true days outstanding for a particular payable. The sum of days outstanding for every payable over a given time period is your TDPO. So, consider a company that has 10 payment transactions with a value of $10 each. The weighted value of each transaction is 10 percent. If the company took 30 days to pay on two of the transactions, took 45 days to pay on three of them, and took 60 days to pay on the other five, then the TDPO for this group of transactions would be 49.5 days—(30×0.2)+(45×0.3)+(60×0.5).

Payables in the Real World

Figure 3 illustrates how much difference there can be between reality and the standard calculation for a working capital metric. Suppose that Company X purchases $100 worth of goods. Company X pays the full $100 on the last day allowed by the contract terms, but because of the timing of the company's payment run, it actually issues payment a couple of days after it is due. Let's also assume that the cost of goods sold is $150 per month, or $1,800 for the year, and that the vendor requires payment 30 days past the invoice date. The payable on the balance sheet is $100 at the end of the first month and $0 at the end of the second month.

If the company had only this one transaction, then a conventional DPO calculation for the period of time during which this transaction impacted the balance sheet would give us a days payables outstanding of 20 days—average month-end balance of $50 divided by monthly COGS of $150, multiplied by 60 days because the transaction impacted the balance sheet for two months.

Now, suppose that Company X looked at this 20-day DPO figure and decided to negotiate with the vendor to extend its payment term to 45 days. The company would have payables of $100 on the balance sheet at the end of each of two months. This would double the average A/P on the balance sheet at month-end, and so would double DPO as well.

What's interesting is to see how these conventional DPO calculations compare with the actual number of days the payment is outstanding. In the real world, it is not easy to accurately measure the starting point or the impact of a change or group of changes. A company calculating TDPO would need to pull the actuals data from its transaction database, which often consists of thousands of records, and collecting this data can be complicated.

However, our example simplifies the process. For the original term of 30 days, the standard DPO calculation returns a performance measure that is off by 33 percent (20 days vs. the actual 30 days). And when the vendor increases payment terms from 30 to 45 days, the simplistic DPO calculation shows an improvement of 100 percent, from 20 to 40 days, whereas the actual improvement is 50 percent, from 30 to 45 days. In this example, getting the extension in payment terms may still be worthwhile, but Company X may not get a good deal if it goes into the vendor negotiations with false expectations about how much the change in terms will affect its working capital.

It's also interesting to consider what would happen to Company X's DPO calculations if, instead of changing the payment terms, it changed the day of the month on which it makes the purchase. Suppose that Company X makes its purchase on the 30th of the month, and that because of the timing of its payment run, it issues payment the day after it is due. This would mean the A/P line on the corporate balance sheet would have a value of $100 in both months, as Figure 4 shows. The actual number of days the company's payable is outstanding (i.e., its TDPO) has not changed at all, but the conventional DPO metric jumps by 100 percent (from 20 days to 40 days) at 30-day terms, when the only change has been the date of the delivery. In addition, this seemingly innocuous change in the timing of the order eliminates the appearance of any working capital benefit from extending payment terms to 45 days.

True Metrics for Sales and Inventory, Too

Conventional calculations of DSO and DIO have similar problems. The timing of invoices and the length of payment terms can swing A/R—and so DSO—in the same way that timing affects payables and DPO. DSO has the additional distinction of being distorted by any factors that alter the price of the transaction. When there is a difference between the value of an invoice and the sales reported for that same invoice, the ratio of A/R-to-sales does not accurately measure the aggregate cycle time for payments to be received or A/R to be cleared from the balance sheet.

Figure 5 provides a simple illustration of how this can happen. Let's assume that Company X makes a sale recorded as a $100,000 receivable on the 20th of the month. Company X extends 45-day payment terms to its customer, and the customer pays right on time. True days sales outstanding is 45 days, by definition. Conventional methods result in a DSO metric of 60 days, even when the reported sale price matches the invoice—average A/R of 100,000 divided by sales of 100,000, multiplied by the 60 days during which the receivable impacts the balance sheet.

And if Company X offers a 10 percent rebate to the customer, reducing the value of the reported sale to $90,000, it skews DSO even further—(100,000/90,000)x60=67. With no rebate, conventional DSO is 33 percent higher than the actual number of days the invoice is outstanding (60 days vs. 45 days). And with the rebate, the standard DSO metric is 49 percent higher than reality (67 days vs. 45 days).

Moving Toward More Accurate Metrics

The example in Figure 3 understates DPO by 33 percent, and the example in Figure 5 overstates DSO by 33 percent or more. These errors result from the simplistic methods of conventional calculations of working capital performance metrics. If a company is trying to drive meaningful improvements in cash flow, these distortions can lead to the establishment of unreasonable goals for cash flow improvement and can make it very difficult to track actual improvements. These problems may prevent an improvement initiative from achieving its goals and may also cause confusion about what went wrong.

For finance executives who understand how conventional metrics are calculated, it is very difficult to have confidence in the traditional CCC baseline or in the company's ability to set realistic working capital goals and then accurately measure improvements. When an initiative may offer the opportunity for a multimillion-dollar improvement in cash flow, accurate and reliable working capital performance metrics are crucial.

The key is to compile the full set of transactional data for a given period of time. Using actual data for vendor payments—including the product receipt date, invoice date, payment due date, and actual payment date—enables an A/P function to calculate a value-weighted aggregate payment term and the company's true DPO. Similarly, calculating DSO and DIO using the company's detailed transactional records gives a true DSO and a true DIO.

Having accurate numbers around these metrics enables the organization to analyze the impact of changes in a particular category of working capital—for example, early or late payments—on the company's actual cash flow. In the same way that the sports world has deployed innovative metrics and analytics, companies can use sophisticated working capital software to revolutionize the measurement and management of payables, receivables, and inventory.

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Bob Leonard is a leader in the field of working capital and corporate finance. He is the former assiatant treasurer at Kodak, where he led multiple working capital improvement projects. He is currently a product manager at Trufa Inc, where he is focused on enhancing the business functionality of the Trufa predictive analytics application. The Trufa solution is designed to optimize operations through the identification of business drivers that define performance (visit www.trufa.net/).