# Gross margin return on working capital: a project management technique.

Gross Margin Return on Working Capital: A project Management
Technique

Every business needs to understand the contributions various products make to the company. Measurement varies, but can generally be cataloged into estimates of margin generation (gross margin, contribution margin, etc.), measures of asset usage (turnover, days sales outstanding, etc.), and measures of return (ROI, RONA, ROE, etc.). These return measures are especially difficult to implement on a product-by-product basis. It is relatively easy to find the margin a product generates by looking at its gross margin, but the issues get very thorny when one needs to look at fixed cost and asset allocation. In practice, it is virtually impossible to come to schemes that are readily accepted across organizations. There are simply too many valid points of view on the most appropriate method, and each different method tends to produce a different answer. However, there is considerable motivation to continue the search. Gross margin and turnover simply do not tell the whole story. They do not relate well enough to an ultimate return-on-asset type of success criteria.

Many businesses are composed primarily of working capital: inventory, receivables, payables. It is not uncommon to find more than 80 percent of the company's assets in working capital. In these instances, a measure of return on working capital would provide a pretty good approximation of return on assets.

From a product management perspective, GROWC is still not a very handy measure with which to work. It requires data on a product-by-product basis which is not readily available. More commonly, product managers utilize devices such as "Health Index" (HI; often also known as GIMROY) which is simply gross margin percent multiplied by turnover. This data is easily accessible and the computation is simple.

The problem with HI is the lack of a clear relationship with the company's financial success criteria and little knowledge of how the measure behaves. Even given these criticisms, it is widely used. There is a very strong motivation to have data that relates the performance of products to each other and to the company's goals. GROWC has the potential of meeting these needs using simple, readily available data and a minimum of simplifying assumptions.

It is possible to relate GROWC to gross margin percent and inventory turnover for each specific product. Three significant assumptions are:

* Selling price can be approximated by grossing up average inventory cost using gross margin percent.

* An individual product's days sales outstanding (DSO) is known or is approximated by the DSO of the company.

* An individual product's days payables outstanding (DPO) is known or is approximated by the DPO of the company.

Concerning the second and third items, the company DSO and DPO are very serviceable starting places. If a product were significantly different from the norm, it would be relatively easy for a product manager to estimate an improved figure.

The first assumption, however, is a known source of error. The higher the gross margin (GM) percent, the greater the error. Figure 1 shows the error ratio for GM percent up to 40 percent. You can see that there is much of a problem when GM is less than about 20 percent and the information is usable if GM is less than 30 percent.

As noted earlier, HI is a simple index; GM times turnover. An acceptable level is typically 100. For purpose of comparison, figure 2 shows both GROWC and HI at 100, and figure 3 shows GROWC and HI at 50 and 200.

Note that HI is strongly driven by turnover. Increasing turnover brings word good rewards in terms of reduced GM levels required to achieve a given goal. GROWC also rewards turnover, but only to a point. As inventories are driven down, the investment in working capital becomes essentially static at the level of receivables. This, of course, assumes credit sales. With no receivable, HI underestimates returns because it ignores payables.

Figure 4 shows the family of curves of GROWC from 30 percent to 80 percent. There are two strong lessons for product management here. First, the margin requirements (to achieve a given turns) increase very fast at low turnover levels. Second, there is a point of diminishing returns in pushing turnover too hard. At some point, resources would be better spent in seeking ways to increase GM rather than be applied to inventory management. Figure 4 also shows that there are a number of ways to achieve a given level of return. Products with two turn and 23 percent GM yield approximately the same financial results as products with 15 turns and six percent GM. As a product manager, you would be indifferent to these two very dissimilar items.

In a business with many unique products, this method can provide a way of reviewing items for replacement or for emphasis by the sales function. It provides away of evaluating apples and oranges.

Implementation of this technique is best done with graphic presentations. The formula is not intuitive, nor is it easy to quickly evaluate by hand. It is, however, very easy to integrate into computer-based information systems. Graphs are easy to understand and can be posted about the workplace for ready reference. We have found that graphs are usable by relatively inexperienced product managers and that they are not as threatening as other techniques.

Figure 5, 6, and 7 are expansions of figure 4. The areas of interest are blown up to make the analysis and extrapolation easier. These are the real meat of the implementation. They provide a framework for making product trade-off and resource allocation decisions.

GROWC is a powerful product management tool. It relates better to run on assets criteria than do other measures and is a much better tool than GIMROY or HEALTHINDEX, and it relates to two readily available pieces of information: gross margin percent and inventory turnover. This facilitates the use of the measure and make possible product-by-product comparisons.

Every business needs to understand the contributions various products make to the company. Measurement varies, but can generally be cataloged into estimates of margin generation (gross margin, contribution margin, etc.), measures of asset usage (turnover, days sales outstanding, etc.), and measures of return (ROI, RONA, ROE, etc.). These return measures are especially difficult to implement on a product-by-product basis. It is relatively easy to find the margin a product generates by looking at its gross margin, but the issues get very thorny when one needs to look at fixed cost and asset allocation. In practice, it is virtually impossible to come to schemes that are readily accepted across organizations. There are simply too many valid points of view on the most appropriate method, and each different method tends to produce a different answer. However, there is considerable motivation to continue the search. Gross margin and turnover simply do not tell the whole story. They do not relate well enough to an ultimate return-on-asset type of success criteria.

Many businesses are composed primarily of working capital: inventory, receivables, payables. It is not uncommon to find more than 80 percent of the company's assets in working capital. In these instances, a measure of return on working capital would provide a pretty good approximation of return on assets.

From a product management perspective, GROWC is still not a very handy measure with which to work. It requires data on a product-by-product basis which is not readily available. More commonly, product managers utilize devices such as "Health Index" (HI; often also known as GIMROY) which is simply gross margin percent multiplied by turnover. This data is easily accessible and the computation is simple.

The problem with HI is the lack of a clear relationship with the company's financial success criteria and little knowledge of how the measure behaves. Even given these criticisms, it is widely used. There is a very strong motivation to have data that relates the performance of products to each other and to the company's goals. GROWC has the potential of meeting these needs using simple, readily available data and a minimum of simplifying assumptions.

It is possible to relate GROWC to gross margin percent and inventory turnover for each specific product. Three significant assumptions are:

* Selling price can be approximated by grossing up average inventory cost using gross margin percent.

* An individual product's days sales outstanding (DSO) is known or is approximated by the DSO of the company.

* An individual product's days payables outstanding (DPO) is known or is approximated by the DPO of the company.

Concerning the second and third items, the company DSO and DPO are very serviceable starting places. If a product were significantly different from the norm, it would be relatively easy for a product manager to estimate an improved figure.

The first assumption, however, is a known source of error. The higher the gross margin (GM) percent, the greater the error. Figure 1 shows the error ratio for GM percent up to 40 percent. You can see that there is much of a problem when GM is less than about 20 percent and the information is usable if GM is less than 30 percent.

As noted earlier, HI is a simple index; GM times turnover. An acceptable level is typically 100. For purpose of comparison, figure 2 shows both GROWC and HI at 100, and figure 3 shows GROWC and HI at 50 and 200.

Note that HI is strongly driven by turnover. Increasing turnover brings word good rewards in terms of reduced GM levels required to achieve a given goal. GROWC also rewards turnover, but only to a point. As inventories are driven down, the investment in working capital becomes essentially static at the level of receivables. This, of course, assumes credit sales. With no receivable, HI underestimates returns because it ignores payables.

Figure 4 shows the family of curves of GROWC from 30 percent to 80 percent. There are two strong lessons for product management here. First, the margin requirements (to achieve a given turns) increase very fast at low turnover levels. Second, there is a point of diminishing returns in pushing turnover too hard. At some point, resources would be better spent in seeking ways to increase GM rather than be applied to inventory management. Figure 4 also shows that there are a number of ways to achieve a given level of return. Products with two turn and 23 percent GM yield approximately the same financial results as products with 15 turns and six percent GM. As a product manager, you would be indifferent to these two very dissimilar items.

In a business with many unique products, this method can provide a way of reviewing items for replacement or for emphasis by the sales function. It provides away of evaluating apples and oranges.

Implementation of this technique is best done with graphic presentations. The formula is not intuitive, nor is it easy to quickly evaluate by hand. It is, however, very easy to integrate into computer-based information systems. Graphs are easy to understand and can be posted about the workplace for ready reference. We have found that graphs are usable by relatively inexperienced product managers and that they are not as threatening as other techniques.

Figure 5, 6, and 7 are expansions of figure 4. The areas of interest are blown up to make the analysis and extrapolation easier. These are the real meat of the implementation. They provide a framework for making product trade-off and resource allocation decisions.

GROWC is a powerful product management tool. It relates better to run on assets criteria than do other measures and is a much better tool than GIMROY or HEALTHINDEX, and it relates to two readily available pieces of information: gross margin percent and inventory turnover. This facilitates the use of the measure and make possible product-by-product comparisons.

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Author: | Metcalf, Jerry |
---|---|

Publication: | Industrial Management |

Date: | Jul 1, 1990 |

Words: | 984 |

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