Measuring productivity for all inputs at once is called total productivity measurement. In practice, it may not be necessary to measure the effect of all inputs. Many firms measure the productivity of only those factors that are thought to be relevant indicators of organizational performance and success. Thus, in practical terms, total productivity measurement can be defined as focusing on a limited number of inputs, which, in total, indicates organizational success. In either case, total productivity measurement requires the development of a multifactor measurement approach. A common multifactor approach suggested in the productivity literature (but rarely found in practice) is the use of aggregate productivity indices. Aggregate indices are complex and difficult to interpret and have not been generally accepted. Two approaches that have gained some acceptance are profile measurement and profit-linked productivity measurement.
Profile Productivity Measurement
Producing a product involves numerous critical inputs such as labor, materials, capital, and energy. Profile measurement provides a series or vector of separate and distinct partial operational measures. Profiles can be compared over time to provide information about productivity changes. To illustrate the profile approach, we will use only two inputs: labor and materials. Let’s return to the Nevada Company example. As before, Nevada implements a new production and assembly process in 2007. Only now, let’s assume that the new process affects both labor and materials. Initially, let’s look at the case for which the productivity of both inputs moves in the same direction. The following data for 2006 and 2007 are available:
Profile Productivity Measurement
Producing a product involves numerous critical inputs such as labor, materials, capital, and energy. Profile measurement provides a series or vector of separate and distinct partial operational measures. Profiles can be compared over time to provide information about productivity changes. To illustrate the profile approach, we will use only two inputs: labor and materials. Let’s return to the Nevada Company example. As before, Nevada implements a new production and assembly process in 2007. Only now, let’s assume that the new process affects both labor and materials. Initially, let’s look at the case for which the productivity of both inputs moves in the same direction. The following data for 2006 and 2007 are available:
Exhibit 15-3 provides productivity ratio profiles for each year. The 2006 profile is (4, 0.200), and the 2007 profile is (5, 0.217). Comparing profiles for the two years, we can see that productivity increased for both labor and materials (from 4 to 5 for labor and from 0.200 to 0.217 for materials). The profile comparison provides enough information for a manager to conclude that the new assembly process has definitely improved overall productivity. The value of this improvement, however, is not revealed by the ratios.
As just shown, profile analysis can provide managers with useful insights about changes in productivity. However, comparing productivity profiles will not always reveal the nature of the overall change in productive efficiency. In some cases, profile analysis will not provide any clear indication of whether a productivity change is good or bad. To illustrate, let’s revise the Nevada Company data to allow for trade-offs among the two inputs. Assume that all the data are the same except for materials used in 2007. Let the materials used in 2007 be 1,300,000 pounds. Using this revised number, the productivity profiles for 2006 and 2007 are presented in Exhibit 15-4. The productivity profile for 2006 is still (4, 0.200), but the profile for 2007 has changed to (5, 0.192). Comparing productivity profiles now provides a mixed signal. Productivity for labor has increased from 4 to 5, but productivity for materials has decreased from 0.200 to 0.192. The new process has caused a trade-off in the productivity for the two measures. Furthermore, while a profile analysis reveals that the trade-off exists, it does not reveal whether the trade-off is good or bad. If the economic effect of the productivity changes is positive, then the trade-off is good; otherwise, it must be viewed as bad.
Valuing the trade-offs would allow us to assess the economic effect of the decision to change the assembly process. Furthermore, by valuing the productivity change, we obtain a total measure of productivity.
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