The Cash Conversion Cycle
The Cash Conversion Cycle (CCC) is a statistic that indicates how long it takes a business to turn its inventory investments into money. The conversion cycle formula calculates the amount of time it takes for a business to turn its resource inputs into cash, in days.
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The Cash Conversion Cycle formula is as follows:
| Cash Conversion = Days Inventory + Days Sales – Days Payable Cycle Outstanding Outstanding Outstanding |
Days Inventory Outstanding (DIO) is the total number of days the company keeps the inventory before selling it. Calculation of unpaid inventory days demonstrates how easily a business can turn inventory into cash. It is a liquidity metric and also a measure of the operational and financial performance of a business. DOI is expressed as:
DIO = (Average Inventory / Cost of goods sold) x (Number of days in a specified time frame)
The high DOI suggests that a company is not in a position to rapidly turn its inventory into revenue. This could be due to bad sales results or the acquisition of too many products. Having too much idle inventory is counterproductive to a company, as inventory may eventually become redundant and unsellable. Holding surplus inventory often adversely impacts cash flow.A low DOI means that a company is in a position to turn its inventory into revenue more quickly. Low DIO, therefore, translates into an effective company in terms of inventory management and sales efficiency.
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Days Sales Outstanding (DSO) reflects the total amount of days it takes for loan transactions to be turned into cash or how long it takes for a business to receive its accounts receivable. DSO can be determined by dividing the gross accounts receivable for a given period of time by the total net sales of the credit. This number is then multiplied by the number of days in the time frame.
DSO = (Account Receivables / Net Credit Sales) x (Number of days)
Days Payable Outstanding (DPO) refers to the total amount of days a company needs to settle its accounts payable. As a result, unpaid days payable assesses how well an organization handles its accounts payable. If the result is a low DSO, the business will take fewer days to collect the receivables. On the other hand, a high DSO means that it takes more days to collect receivables. High DSOs can lead to problems with cash flow in the long run.
DPO = ( Average Accounts Payable / Cost of Goods Sold ) x (Number of Days)
A low DPO business may mean that the company is not making good use of the credit period offered by the creditors. Alternatively, the company can only provide short-term lending agreements for its creditors. A high DPO is usually beneficial to a business. If the company takes longer to pay its investors, the extra cash on hand may probably be used for short-term investing operations. However, taking too long to pay the creditors can result in dissatisfied creditors declining to lend more credit or providing favorable credit terms. Moreover, if the DPO is too high, it may mean that the company is unable to raise cash to compensate its creditors.
Example:
CCC of Dynamic Mattress Company:
| Inventory at the start of the year | 130 |
| Cost of goods sold | 1644 |
| Receivables at the start of the year | 125 |
| Credit sales | 2200 |
| Payables at the start of the year | 110 |
DIO = ( 130 / 1644 ) X 365 = 29 days
DSO = ( 125 / 2200 ) X 365 = 21 days
DPO = ( 110 / 1644 ) X 365 = 24 days
CCC = DIO + DSO – DPO = 29 + 21 – 24 = 26 days.
Dynamic purchases raw materials on the 0th day. It will pay for these materials on day 24 (DPO). By day 29 Dynamic company has converted the raw materials into finished mattresses which are then sold (DIO). Twenty-one days later, on day 50, Dynamic’s customers pay for their purchases (DSO). So, cash went out the door on the 24th day and it didn’t come back in until the 50th day. This 26-day period is called the Cash Conversion Cycle.
Author: Abhay Kanodia
About the author: An undergraduate student from the Birla Institute of Technology and Sciences, Pilani(BITS Pilani). Exploring the fields of finance and data analytics and its applications in other different domains.
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