Appendix Productivity Measurement

Productivity measures the relationship between actual inputs used (both quantities and costs) and actual outputs produced. The lower the inputs for a given quantity of outputs or the higher the outputs for a given quantity of inputs, the higher the productivity. Measuring productivity improvements over time highlights the specific input–output relationships that contribute to cost leadership. The productivity measures discussed in this appendix relate closely to the productivity component introduced in this chapter.

Partial Productivity Measures

Partial productivity, the most frequently used productivity measure, compares the quantity of output produced with the quantity of an individual input used. In its most common form, partial productivity is expressed as a ratio:

The higher the ratio, the greater the productivity.

Consider direct materials productivity at Chipset in 2020.

Note that direct materials partial productivity ignores Chipset’s other input, manufacturing conversion capacity. Partial-productivity measures become more meaningful when comparisons are made that examine productivity changes over time, either across different facilities or relative to a benchmark. Exhibit 13-7 presents partial-productivity measures for Chipset’s inputs for 2020 and the comparable 2019 inputs that would have been used to produce 2020 output, using information from the productivity-component calculations in section Productivity Component of Change in Operating Income. These measures compare actual inputs used in 2020 to produce 1,150,000 units of CX1 with inputs that would have been used in 2020 had the input–output relationship from 2019 carried over to 2020.

Evaluating Changes in Partial Productivities

Note how the partial-productivity measures differ for variable-cost and fixed-cost components. For variable-cost elements, such as direct materials, productivity improvements measure the reduction in input resources used to produce output (3,450,000 square centimeters of silicon wafers to 2,900,000 square centimeters). For fixed-cost elements such as manufacturing conversion capacity, partial productivity measures the reduction in overall capacity from 2019 to 2020 (3,750,000 square centimeters of silicon wafers to 3,500,000 square centimeters) regardless of the amount of capacity actually used in each period.

An advantage of partial-productivity measures is that they focus on a single input. As a result, they are simple to calculate and easy for operations personnel to understand. Managers and operators examine these numbers and try to understand the reasons for the productivity changes—such as better training of workers, lower labor turnover, better incentives, improved methods, or substitution of materials for labor. Isolating the relevant factors helps Chipset implement and sustain these practices in the future.

For all their advantages, partial-productivity measures also have serious drawbacks. Because partial productivity focuses on only one input at a time rather than on all inputs simultaneously, managers cannot evaluate the effect on overall productivity, if (say) manufacturing-conversion-capacity partial productivity increases while direct materials partial productivity decreases. Total factor productivity (TFP), or total productivity, is a measure of productivity that considers all inputs simultaneously.

Total Factor Productivity

Total factor productivity (TFP) is the ratio of the quantity of output produced to the costs of all inputs used based on current-period prices.

TFP considers all inputs simultaneously and the tradeoffs across inputs based on current input prices. Do not think of all productivity measures as physical measures lacking financial content—how many units of output are produced per unit of input. TFP is intricately tied to minimizing total cost—a financial objective.

Calculating and Comparing Total Factor Productivity

We first calculate Chipset’s TFP in 2020, using 2020 prices and 1,150,000 units of output produced (based on information from the first part of the productivity-component calculations in section Productivity Component of Change in Operating Income).

By itself, the 2020 TFP of 0.058748 units of CX1 per dollar of input costs is not particularly helpful. We need something to compare the 2020 TFP against. One alternative is to compare TFPs of other similar companies in 2020. However, finding similar companies and obtaining accurate comparable data are often difficult. Companies, therefore, usually compare their own TFPs over time. In the Chipset example, we use as a benchmark TFP calculated using the inputs that Chipset would have used in 2019 to produce 1,150,000 units of CX1 at 2020 prices (that is, we use the costs calculated from the second part of the productivity-component calculations in section Productivity Component of Change in Operating Income). Why do we use 2020 prices? Because using the current year’s prices in both calculations controls for input-price differences and focuses the analysis on adjustments the manager made in quantities of inputs in response to changes in prices.

Using 2020 prices, TFP increased 9.8% $[(0.058748-0.053519)÷0.053519=0.098,$ or 9.8%] from 2019 to 2020. Note that the 9.8% increase in TFP also equals the $1,912,500 gain (Exhibit 13-6, column 4) divided by the $19,575,000 of actual costs incurred in 2020 (Exhibit 13-6, column 5). Total factor productivity increased because Chipset produced more output per dollar of input cost in 2020 relative to 2019, measured in both years using 2020 prices. The gain in TFP occurs because Chipset increases the partial productivities of individual inputs and, consistent with its strategy, combines inputs to lower costs. Note that increases in TFP cannot be due to differences in input prices because we used 2020 prices to evaluate both the inputs that Chipset would have used in 2019 to produce 1,150,000 units of CX1 and the inputs actually used in 2020.

Using Partial and Total Factor Productivity Measures

A major advantage of TFP is that it measures the combined productivity of all inputs used to produce output and explicitly considers gains from using fewer physical inputs as well as substitution among inputs. Managers can analyze these numbers to understand the reasons for changes in TFP—for example, better human resource management practices, higher quality of materials, or improved manufacturing methods.

Although TFP measures are comprehensive, operations personnel find financial TFP measures more difficult to understand and less useful than physical partial-productivity measures. For example, companies that are more labor intensive than Chipset use manufacturing-labor partial-productivity measures. However, if productivity-based bonuses depend on gains in manufacturing-labor partial productivity alone, workers have incentives to substitute materials (and capital) for labor. This substitution improves their own productivity measure, while possibly decreasing the overall productivity of the company as measured by TFP. To overcome these incentive problems, companies—for example, Eaton and Whirlpool—explicitly adjust bonuses based on manufacturing-labor partial productivity for the effects of other factors such as investments in new equipment and higher levels of scrap. That is, they combine partial productivity with TFP-like measures.

Many companies such as Behlen Manufacturing, a steel fabricator, and Dell Computers use both partial productivity and total factor productivity to evaluate performance. Partial productivity and TFP measures work best together because the strengths of one offset the weaknesses of the other.