Strategy, Productivity, and History
2.3 Measurement of Productivity
Productivity is a critical business variable that directly impacts the “bottom line”;
improved productivity raises net profits. P/OM is responsible for the productivity of the process. This is such a critical factor in a company’s overall strategy that excel- lence in productivity achievement is a major P/OM issue.
Productivity is a system property that interacts with other system properties such as reliability and consistency, as well as customers’ perceptions of quality. It interacts with the variable costs of goods and services, as well as with the fixed costs of facilities, training, and technology. It also interacts with the presence and the availability of management as a resource. It is often found that output rates and qualities (and therefore productivity) are better on regular work shifts as compared to “graveyard” shifts because managers are scarce resources for the latter.
Productivity measures the performance of the organization’s processes for doing work. It is an important way of grading how well P/OM and the rest of the system are doing. It is a score like RBIs (runs batted in), your credit rating, or ROI (return on investment).
Productivity, according to The Association for Operations Management, which used to be called the American Production and Inventory Control Society (APICS), is defined as “an overall measure of the ability to produce a good or a service.” It is the actual output of production compared to the actual input of resources. Productivity is a relative measure across time or against common entities (Blackstone and Cox 2004).
Operations management views the measurement of productivity as an essential tool for assessing the performance of an organization’s productive capacity over a spe- cific time period and in comparison to the competition. When outputs are high and inputs are low, the system is said to be efficient and productive. The APICS Dictionary definition of productivity can be converted into the terms of this text by recognizing that productivity is the ratio measure of the output divided by the input as given by
Productivity Outputs
Inputs Measure of production efficiency.
= = (2.1)
This productivity measure compares the quantity of goods or services produced in a period of time (t) and the quantity of resources employed in turning out these goods or services in the same period of time (t) (Fabricant 1969).
Equation 2.1 is relatively easy to measure for physical goods. It is more difficult to find appropriate measures for some services outputs such as units of education or health care. Creative knowledge workers provide other instances of intangible outputs that are highly valued, but elusive to calculate. The effort has to be made to appraise the value of these outputs in a standardized way to provide a benchmark (or standard) for measurement. Several authors have proposed ways to measure service productivity. Some of these are described below.
Levitt (1972), in his article, Production Line Approach to Service, argues that,
“if customer service is consciously treated as ‘manufacturing in the field,’ it will get the same kind of detailed attention that manufacturing gets. It will be carefully planned, controlled, automated where possible, audited for quality control, and reg- ularly reviewed for performance improvement and customer reaction.” In the arti- cle, Managing Our Way to Higher Service-Sector Productivity, Biema and Greenwald (1997) also argue that productivity improvement techniques used in manufacturing sector are applicable to service sector as well in spite of the complexity of the service sector.
According to Drucker (1999), improving knowledge–worker productivity is one of the biggest challenges in the twenty-first century. He lists six major determinants of knowledge–worker productivity. These include: offer a clear task definition, put responsibility for productivity on workers themselves and provide autonomy, use
continuous innovation, continuous learning and teaching, focus on quality and not only quantity of output, and treat knowledge worker as an “asset” rather than a “cost.”
Improving the productivity of specific service businesses has been the focus of attention of several other authors also—for example, hospitals and banks by Sherman (1984), Bell Labs by Kelley and Caplan (1993), and sales force by Ledingham et al. (2006).
Productivity measures are benchmarks for comparing how well the system is doing compared to other systems, or over time. The comparisons include: how A is doing over time; how A compares to B; how departments within A compare to each other; how A compares to an industry average; how A compares to the best of the industry, etc.
Sales perceives productivity as high customer sales volume (called an effective marketing system) accompanied by low producer costs (called an efficient producer system). Thus
Productivity Effectiveness
Efficiency High customer sales v
= = oolume
Low producer expenses . (2.2) From a systems point of view, the inclusion of sales provides a correct measure of output for productivity measurement. It is never productive to make a lot of product that is not sold, even if the cost of making it is low.
At the same time, P/OM employs productivity measures to assess how well the production system is functioning. The kinds of questions that are being addressed are: how many units of resources are consumed to produce the output, and how many units of output can be made with a fixed amount of capacity? These are blue- prints for productivity improvement.
Both ways of viewing productivity have benefits. They stem from different interests that need to be shared. The best interests of the company are served by merging what is learned about P/OM’s efficiency and sales/marketing effectiveness.
2.3.1 Labor Productivity
Labor efficiency is an often-used measure of productivity where productivity is a ratio of output units produced to input labor resources expended per unit of time (t). The following equation gives the dimensions for output and input:
Output =units per hour and Input = dollars per hour =hourly wages.. (2.3) Equation 2.4 measures labor productivity over a specific period of time, t, and gives the relationship between the dollars of labor resources required in period (t) to achieve the output rate (t). This is a measure of how much output is being obtained for each dollar spent.
Productivity Output
Input Units of output Dollars o
( ) ( )
( ) ( )
t t
t t
= = ff labor( ) .t (2.4)
Another way of stating the measure of productivity is to put a dollar value on the output volume per unit of time (t) as given in the following equation:
Labor productivity Output
Input Sales in dollars
( ) ( )
( ) ( )
t t
t t
= = DDollarsof labor( ).t (2.5)
The advantage of this method is that it can deal with the productivity of the system across different kinds of units. For example, the productivity of a paint company that puts paints of many colors in cans of many sizes could be measured.
This approach is used for national accounting of productivity where there are many different kinds of units to be included (i.e., furniture, clothing, energy, and food).
Dollars can standardize the output measure across diverse categories.
Labor productivity, often measured as the ratio of sales dollars to labor cost dol- lars, is called a partial measure of productivity. It is partial because it only looks at labor and does not include capital.
2.3.2 Capital Productivity
There are also other factors that can be used to measure productivity. For example, capital may be used as an input resource in place of labor, or both capital and labor may be used as discussed in Section 2.3.3. Capital productivity is the productivity of the invested capital in technology. Equation 2.6 measures capital productivity as the number of units of output per dollar of invested capital, whereas Equation 2.7 measures capital productivity as a pure ratio based on dollars of output per dollar of invested capital:
Capital productivity Output
Input Units of output
( ) ( )
( ) (
t t
t t
= = ))
( ),
Dollarsof capitalt (2.6)
Capital productivity Dollar value of output units( )
( )t = Dolllars of capital resources( )tt . (2.7) Such measures of capital productivity might help organizations in the service sectors to address the value of returns on investments in computers and telecom- munications. By extending this reasoning, it is possible to develop other partial measures of productivity with respect to areas such as energy expended, space uti- lized, and materials consumed.
2.3.3 Multifactor Productivity
Multifactor productivity (MFP) can be measured in different ways. As shown in Equation 2.8, sales and finished goods are considered as outputs, but work-in-pro- cess is not. Also, labor costs and capital expenditures (amortized) are treated as inputs. MFP (see Equation 2.8) is measured as the ratio of dollars earned to dollars spent. Total factor productivity is another name associated with inclusion of more factors than labor. Multifactor (and total factor) productivity is measured on a regular basis to determine how well the US industrial base is doing and whether it is improving its competitive position in the world.
Multifactor productivity Alloutputsof goodsand services
( ) ( ;
t = t $$)
( ,$) . Total input resources expendedt (2.8) A change in multifactor productivity reflects the difference between the change in output (production of goods and services) and the change in labor and capital inputs engaged in the production of the output.
Multifactor productivity does not measure the specific contributions of labor, capital, or any other factor of production. Instead, it reflects the joint effects of many factors, including new technology, economies of scale, managerial skill, and change in the organization of production.
(USDL, July 7, 1994)
If it seems useful to include work-in-process in the numerator because WIP is certain to be sold, then that should be done. Similarly, it may be important to include all costs and expenses in the denominator (i.e., energy, material, and miscellaneous costs). This is called total productivity as distinguished from total factor productivity.
Beinhocker et al. (2009) state, “We believe that, in the years to come, ‘resource productivity’ (the output achieved from every unit of oil, power, water and other resource input) will become central to company competitiveness.” With this in mind, planning for resource price increases, volatility, and even shortages is impor- tant for strategists for future planning.
2.3.3.1 Trends in Multifactor (MFP) Productivity
Table 2.1 shows the average annual percent changes in MFP for the United States over different periods of time (USDL, February 1995). US productivity averaged just slightly above 2% per year from 1948 to 1973. That was considered to be healthy, though not robust, growth. Then came “the shrinking era,” which lasted for 22 years from 1973 to 1995. These were tough years for US manufacturing firms. The US auto industry observed that new Japanese plants in America (trans- plants in Tennessee, OH) in the 1970’s were twice as productive as traditional US auto plants in Detroit.
There began a long learning process on the part of US auto manufacturers to increase their productivity. It took a dozen years (1995–2007), but it worked. US auto making reflected more productive processes. This improvement applied to more than automobiles. After many years of declining growth rates, and even some years of negative growth, US productivity had accelerated to levels equivalent to the post-World War II years.
The renewal of US productivity growth was not surprising. Great efforts had been made in the manufacturing sectors to improve efficiency. Subjects such as total quality management (TQM), reengineering, and turnarounds, discussed in the general press, were not fads. They heralded solid accomplishments. During the period from December 2007 to June 2009, there was a subprime mortgage crisis that led to the collapse of the housing bubble in the USA. This triggered a global financial crisis. In addition, productivity suffered as a result of sending abroad (off- shoring) manufacturing jobs which have always been more productive than service jobs. As seen in Table 2.1, productivity recovery began after 2011.
Adoption of new technology promises more developments yet to come.
However, it is important to reiterate the comparison between manufacturing pro- ductivity and service-based productivity. In addition to measures of service produc- tivity being much lower than those of manufacturing, they have not shown notable improvements over many years in all parts of the world.
There have been substantial investments in computers and telecommunications within the service sectors, but only recently are there real signs of improvement. At last, broad-based changes in productivity can be reported. Technological advances in voice recognition and intelligent service dialog between computers and people have begun to change the landscape. This will accelerate as P/OM managers learn how to apply the new technologies to service systems. This productivity differential
Table 2.1 Average Annual Percent Changes in USA MFP Year Multifactor Productivity % Change
1948–1973 2.2 —
1973–1990 0.5 −77.2%
1990–1995 0.5 0.00%
1995–2000 1.3 160%
2000–2007 1.4 7.6%
2007–2012 0.6 −57%
2011–2012 1.4 133%
Source: This table is based in part on Table A in a Bureau of Labor Statistics News Release dated June 28, 2013, enti- tled Preliminary Multifactor Productivity Trends—2012.
constitutes a significant difference that currently exists between services and man- ufacturing. It is an area of great potential for P/OM.
Students and practitioners of business know that productivity improve- ments translate into more profits and greater profitability for organizations and improve the state of the economy. In the same vein, economists believe that productivity improvements translate into higher standards of living and greater prosperity. Productivity measures get factored into inflation calculations as well as other economic scenarios. Increases in productivity are generally regarded as a means of checking inflationary trends. Table 2.1 is well correlated with national prosperity.
This is because by working smarter—not harder—profit margins increase.
There is more money available to invest in other business opportunities. The short- age of capital drives interest rates up. However, if there are more jobs available than people to fill them, labor becomes scarce, and rising salaries begin to fuel the expansion of inflation. High productivity is a counterforce to inflation. Fewer peo- ple are needed to produce more work. The cost of goods and services moves down with productivity improvements. The contribution of P/OM to the well-being of the national economy is widely recognized. The complaint that high productivity causes unemployment leads to the recognition that good jobs are not those which can be better done by machines. Good jobs are thinking jobs and creative activities.
We have to redefine the beneficial occupations for a future in which machines do laborious work better than people.
Relative productivity compares the performance of competitive processes for which P/OM is accountable. This means that if two processes are under consider- ation for a new product, the productivity measures associated with each product should be derived. When quality problems exist, adjustments downward must be made to productivity. For example, the value of sales plus finished goods plus WIP must be reduced by the cost of defectives. Final decisions about the two processes will not be made on productivity advantages alone, but relative productivity will play a major part in planning.
2.3.4 Operational Measures of the Organization’s Productivity
Productivity measures can be common sense ratios such as the value of pieces made in a factory divided by the cost of making them, or the number of documents produced by the typing pool divided by the number of people doing word process- ing. Such operational measures of productivity are valuable benchmarks to com- panies that are focused on continuous improvement. Relative productivity is such a benchmark.
A productivity measure used by restaurants is the daily dollars generated per table. Many fast-food restaurants use dollars generated per square foot. Airlines measure plane occupancy per flight and average occupancy per route, as well as for all routes flown. Department stores use sales dollars per square foot of space.
Mail-order companies measure sales dollars for categories (i.e., fashion, toys, and luggage) by type of illustration (i.e., color and size) on a percent of page basis.
Trends in same-store sales can be followed as a benchmarking guide to retail pro- ductivity. Many companies measure the productivity of their complaint depart- ments by the ratio of the number of complaints dealt with per day divided by the number of complaint handlers.
Productivity measures should be chosen to reflect strategic goals. For example, some schools can maximize instructor productivity by having large classrooms. The relevant input–output model is apparent. The total of student-in-class hours per instructor increases with large classrooms but personal contact with the instructor per student is reduced. With large class sizes, instructors must be entertaining to get good ratings. Student ratings of instructors may not be a satisfactory measure of educational productivity. Teachers’ grading of students is especially difficult in large-size classes. Appropriate benchmarks for educational productivity exemplify the difficulties of measuring what counts.
Formulating appropriate productivity measures to capture the effectiveness of operations is always a P/OM benchmarking challenge. It takes insights and creativ- ity to measure what matters in performance and what truly can be controlled and corrected.