Thursday, February 2, 2012

Cash Conversion Cycle – A Good Measure?

If you google “Cash Conversion Cycle Calculation”, you will find many, many websites that provide the equations, give some simple examples, and send you forth with encouragement about using this fantastic tool in your work.
However, in real-life it is not really all that simple as it sometimes sounds. In this post we investigate some of the “potholes in the road” you may encounter as you attempt to employ the Cash Conversion Cycle calculation.

Cash Conversion Cycle – Intent and Calculation
The Cash Conversion Cycle (CCC) is one of the metrics that firms can use to track working capital efficiency in their operations. Once we come up with the Cash Conversion Cycle number and its components, we can compare the results against 1) accepted rules of thumb or standards, 2) industry averages, 3) our own prior period experiences as a trend or time series analysis.
The measure also helps us develop balance sheet elements in pro forma financial statements for valuation, growth investment scenarios, or merger and acquisition prospects.
Finally, another common usage is that of comparing the working capital efficiency of different firms with each other.
The Cash Conversion Cycle calculation equations are as follows:
                                              
As an example, let’s assume our firm has the following income statement and balance sheet:
From the above, the Cash Conversion Cycle calculation is as follows:
This is the point where reality intervenes.
The Cash Conversion Cycle, like all metrics, requires some thought as to the measure’s usage, appropriateness, and limitations. We must understand how the “black box” operates in order to determine whether we are achieving the objectives of the investigation.

Cash Conversion Cycle – Revenue Potholes
Each business has its unique sales and revenue profile. Difficulty with the Cash Conversion Cycle calculation can appear when different business lines are blended together.
For instance, if we calculate DSO for McDonald’s (see McDonalds financial statements if interested) , it is 17.9 days. When thinking about converting accounts receivable to cash, that is a great number!
However, I cannot remember the last time I got a Big Mac and told them to send me an invoice!
McDonalds operates company stores and franchises stores to others - two separate businesses (even though they both have to do with restaurants). The issue here is that the line of business related to the accounts receivable balances primarily pertains to the franchising activity, which involve rent, royalties, and percentage of sales payments. These will be periodic payments, giving rise to accounts receivable balances. Using only the franchise revenues as the figure in our DSO calculation, we arrive at 54.9 days.
What if we are performing an industry comparison instead?
If our comparison companies have 100% company-owned stores, then the 17.9 days we calculated will look high compared to theirs. Yet is it reasonable to conclude we are performing less than they? In theory, we should be about the same, because on a company-store only basis both firms will be “cash on the barrelhead” businesses.
What if the comparison companies are 100% franchise? Then our 17.9 days will look low, and we will pat ourselves on the back for doing a great job, even though this might not be the case on an apples-to-apples basis. Fortunately, in this case we have the information to get to the 54.9 day number.
But what if the comparable companies have a mix of company-owned and franchise operations as well, but in different ratios to each other, and they are not separated as on the McDonalds statements? Then we will not be able to make a truly valid comparison at all.
Thus, the objective of the analysis might determine what is better to use. Are we performing the Cash Conversion Cycle calculation in order to pinpoint opportunities to reduce it? Then we should focus on the 2nd version of DSO, as that is likely where we might be able to make inroads.

Cash Conversion Cycle – Inventory and Cost of Goods Sold Potholes
Accounting rules allow us to use four different methods to value inventory – average cost, specific cost, first-in-first-out, and last-in-first-out.
This being the case, any DIO calculation will import these differences into the figure and render a comparison to another firm as apples-to-oranges. Sometimes, the financial statements might provide the data to get the figures comparable (such as a LIFO to FIFO reconciliation), but not always.
In a manufacturing setting, the value of the goods produced includes the raw material cost, direct labor, other direct costs, and allocated manufacturing overhead.
There are different methods a firm can use to allocate overhead. One might do it on the basis of labor time, another on machine time, another based on space occupied.
In addition, the method used to determine the amount to be allocated will also vary. One firm might use a “pool” using balances from one or more financial accounts in the books. Another firm might use a standard cost estimate that was established as part of a study.

Cash Conversion Cycle – Accounts Receivable Potholes
When companies report Accounts Receivable, this number needs to contain an estimate of uncollectible or bad debt expense within it. This amount of uncollectible can often be picked up in the footnotes of the financial statements, and sometimes it is identified on the balance sheet.
So long as we can find the number, we can compare this category with other firms if we are going to view the DSO measure on a grossed up basis.
However, if we are looking on a net basis we need to be careful. If you put the same receivable and sales data in front of five companies, you will get five different bad debt figures. One company will calculate it based on aging buckets, another percentage of sales, one will be aggressive, one will be conservative, etc.

Cash Conversion Cycle – Accounts Payable Potholes
Accounts Payable is a very active one on the balance sheet. Expenses for cost of goods sold are credited to this account.
Hmmm…but so are sales, general and administrative expenses. So are purchases related to capital projects.
The following example takes two companies, who have identical cost of goods sold and sold the same number of units. However, one company is making capital expenditures as part of a growth plan. With the payables portion of these cap ex payments mixed in with the other AP data, we arrive at a different DPO, even though by design we established that they had an identical Cash Conversion Cycle for the current product mix!
Sometimes we can overcome this. We can get a capital expenditure figure from the Statement of Cash Flows. We could make an assumption about the impact this has on payables (e.g. 30 days worth of capex is in AP). Or we could run a linear optimization program to calculate an equilibrium point where AP related to COGS and AP related to Cap Ex is the same number of days outstanding. We will have varying degrees of confidence as to the results this generates, however.

What is the Use?
With all these potholes in the road, we may reconsider the question as to whether we want to make this journey. It can be somewhat tempting to throw our hands up in the air and walk away – what’s the use?
That is certainly an option, but it is not the only one.
If we want to use the Cash Conversion Cycle to compare our own performance over-time, a lot of these potholes do not matter so much. If our methodology of calculating it is consistent from period to period, we will make an apples-to-apples comparison for our own case, using whatever level of detail is appropriate. This is also appropriate if we want to assign additional working capital requirements in a valuation pro forma.
If we want to use the Cash Conversion Cycle to set a benchmark to shoot for, we might use the basic formula, with all its problems, as a first pass, identify the top three or four firms, and do a deeper-dive calculation with just those three or four in order to set the bar for our objectives.
Finally, we can always use the basic Cash Conversion Cycle calculation, but exercise restraint in the conclusions we draw due to our awareness that many potholes are in the road. Do the companies all cluster within 10-15 days of each other? Or do they cluster at different parts of the range? Is the absolute value of the range very wide or close? This information tells us something, it just doesn’t do it to the precision of a soccer score, where we know with absolute certainty team A got one more goal than team B.
Few things in life are that certain anyways. Why should the Cash Conversion Cycle be any different?

Key Takeaways
Financial data gets to the financial statements in a myriad number of ways that can significantly impact the Cash Conversion Cycle calculation. For this reason, we need to temper our conclusions when viewing an analysis of results based on it. If we want a more definitive conclusion, we need to be willing to spend a good amount of time and effort in order to achieve it.

Questions
·       What has been your experience with using the Cash Conversion Cycle calculation?
·       Can you identify other factors in AP, AR, COGS, Sales, and Inventory that might make comparison of two organization’s numbers apples-to-oranges?

Add to the discussion with your thoughts, comments, questions and feedback! Please share Treasury Café with others. Thank you!

3 comments:

  1. The wisdom of having a Composite and uniform scorecard for cashflow rating; while intrinsicly makes sense, and has short term benefits I believe the flaws outweight them on a long term basis. Primarily because one ends us chasing numbers when what is needed is a sense of direction. I have used several methods and the banking industry will continue to use them in some form. Ultimately good businesses and businesses that survive can not let a composit factor as CCC because it gives tends to communicate the wrong single when economics are changing

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    1. I think that is a great point that a sense of direction is what is most helpful regarding the CCC, chasing numbers to hit metrics often seems to backfire, even if one doesn't notice it right away. Thank you for adding to the discussion!

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