What does efficiency measure

Some productivity indexes boast technical elegance and statistical precision—but have little to do with daily management decision making, or even, for that matter, the bottom line. What you need is enough good information to enable you to determine how well your company is taking a pile of raw materials, a bunch of machines, stacks of paperwork, and groups of employees, and turning out useful goods or services.

As long as you stay mindful of how the perfect can get in the way of the good, a few basic guidelines can help you design a system that meets your needs. What is productivity anyway? Rather, productivity is output divided by input. So the job of productivity measurement is to highlight how to get more units of output goods produced or services rendered for each unit of input materials, labor hours, machine time than your competitors are able to deliver. One U. Effective productivity measurement, therefore, takes a multifactor perspective: it identifies the contribution of each factor in production, and then combines the factors to create an understanding of productivity trends.

Keep your measurement system simple—not because people are stupid, but because they need an intuitive grasp of a measurement in order for it to affect their decisions and priorities. Then, too, the effort required to get a slightly more precise measurement may not be worth it.

He devoted many months to the assignment and also tapped the knowledge of several academic experts. The result was a truly sophisticated model that combined historical performance data with economic forecasts to set target productivity levels for each business unit. So headquarters asked the obvious question—Why?

Why was an organization that was generating handsome profits and cash flows showing such disappointing productivity? The expert could not answer the question, nor was his model designed to do so. Not surprisingly, executives saw little value in the new system and scrapped it.

Staff specialists or outside consultants—experts in cost accounting, statistics, and economics—usually play an important role in designing these systems. But specialists are often trained to focus on the technical elegance and statistical accuracy of productivity indexes.

All too often, they introduce methods that are very precise but ignore the real challenges managers face. While collecting information on productivity measurement systems and interviewing managers at plants across the United States during the last several years, I have seen many examples of effective productivity measurement—systems that have led to big strides in operating efficiency. But more frequently I have encountered frustration and confusion.

Productivity measurement is simply too important to be delegated to productivity specialists. A set of practical guidelines can help them understand, evaluate, and apply productivity measurement techniques effectively. What is productivity? Productivity is not about wages. High wages can present a problem, not because workers are paid too much but because they produce too little.

In deciding how best to measure productivity, managers should focus not on dollars per hour but on labor dollars per product. That is, on labor content, not labor cost. Workers who are very productive can be paid thousands of dollars more than employees elsewhere and the business can still prosper, as manufacturers like Lincoln Electric have demonstrated.

Productivity measurement should focus on overall capabilities, not on one set of costs. How good is your company at taking a pile of raw materials, a bunch of machines, stacks of paperwork, and groups of employees, and turning out useful goods or services?

It is, as much as possible, a relationship between physical inputs and outputs. The formula is disarmingly simple. The company producing more with a given set of inputs capital, labor, and materials or using fewer inputs to produce the same output has an advantage over the company producing less.

Lower input costs create an added advantage—but not the principal advantage that productivity measures must identify. The central mission of a productivity index is to illuminate how a business can get more units of output per labor hour, per machine, or per pound of materials than its competitors. Still, much of U. At the national level, productivity figures do mean labor productivity. The Bureau of Labor Statistics, the primary source of productivity information, logically enough focuses on labor productivity.

Cost accounting also reinforces this bias. The allocation of overhead, for example, is often based exclusively on labor hours. This approach may have been reasonable when labor hours represented a large percentage of total costs, but today, for many businesses, labor is a minor cost element. But there is much more to productivity, and many companies miss opportunities to bolster efficiency in nonlabor areas.

Consider one U. His intuition proved correct, as Exhibit I illustrates. His subordinates are now looking for ways to reduce overhead and make better use of technology. Single-minded attention to direct labor can produce unexpected consequences. Several years ago, a big New York bank concerned about labor costs in its back office implemented a department-by-department system to measure productivity, defined as transactions per employee.

Senior management gave high visibility to the new system and even used it to calculate a large portion of the bonuses it paid to line managers.

So the line managers computerized everything in sight. The result was increased productivity in every department but one—data processing.

While staff was shrinking in the rest of the bank, data processing came under incredible pressure. It boosted its staff as well as its spending on hardware and software. If that expansion in overhead was best for the bank, executives could never say for sure; their measurement system focused only on the productivity of direct labor.

The trouble with single-factor productivity measures whether output per labor hour, output per machine, or output per ton of material is that it is easy to increase the productivity of one factor by replacing it with another.

Labor, capital, and materials are all potential substitutes for each other. Effective productivity measurement requires the development of an index that identifies the contribution of each factor of production and then tracks and combines them.

Take a hypothetical plant that machines purchased castings as one step in its production of motors. Now the company decides to purchase this component premachined. What happens to productivity?

Output has remained constant, but the number of workers has fallen, so labor productivity is up. So too is capital productivity, by virtue of the lower asset base. But with top management pushing hard for identifiable productivity increases, there is a real risk that defining productivity too narrowly will lead to unsound decisions by subordinates.

A multifactor view of productivity is important, therefore, but it is difficult for one index to encompass all inputs. Using several different single-factor measures can also yield a multifactor perspective.

Indeed, even if a plant uses one aggregate measure, it still makes sense to use single-factor measures because they help identify the sources of aggregate productivity trends.

A big change in a multifactor productivity measurement raises obvious questions: Is the change due to simultaneous shifts in the productivity of labor, capital, and materials, or has only one dimension changed?

And because employees are not pre-programmed machines that perform tasks as fast as physics allow, they have a maximum level of productivity and efficiency that can be attained.

For our intents and purposes, we only need to care about the two most relevant types of efficiency: static and dynamic.

The first of two that we have is static efficiency. Static efficiency is a type of efficiency that relates to an existing environment in which the work gets done. This usually means improving upon an existing process or product and taking advantage of existing opportunities. We can say that it improves static efficiency by making the existing process of clocking in more efficient for employees and management. This is one way to reduce time waste and obtain an increase in this type of efficiency.

The second of two that we have is dynamic efficiency. The word dynamic here refers to improvements and developments of new processes and products in relation to time. This can also apply to creation of new opportunities for oneself to improve long-term profitability. Using DeskTime again as an example, we can say that investing into it for the first time to make the process of invoicing and project tracking easier is a time-dependent process that may have a high upfront cost, but with it comes the increase of profitability in the long run because manual accounting is no longer necessary.

Here we access new resources and make new improvements, all resulting in an observable increase in dynamic efficiency. Consider the data that DeskTime gathers: DeskTime time , productive time , productivity , and effectiveness.

The two metrics that belong to efficiency analysis are productivity and effectiveness , displayed as a percentage. When it comes to applying these percentages, they can be used to inform you about the efficiency of any one employee. If an employee has a high percentage of DeskTime productivity and effectiveness , then the employee can be said to be efficient because he or she is using all his and her time and resources to work towards a goal.

Regardless, this measurement is useful for comparing teams and individuals in the workplace to find if their time is being used productively as is defined by DeskTime. Having all this information at your fingertips means that you can compare individual employee metrics to determine if there is an issue, and if there is one, figure out what strategies can be used for optimizing their performance.

Thus, staff and work processes affect the productivity of an operation. Efficiency is a relative concept. It is measured by comparing achieved productivity with a desired norm, target, or standard. Output quantity and quality achieved and the level of service provided are also compared to targets or standards to determine to what extent they may have caused changes in efficiency.

Efficiency is improved when more outputs of a given quality are produced with the same or fewer resource inputs, or when the same amount of output is produced with fewer resources.

These relationships are illustrated in Figure 2. Efficiency is only one dimension of the performance of a government program or operation. Auditors should be equally aware of other dimensions of performance, including economy and effectiveness.

Due regard to economy requires that resources of appropriate quantity and quality be obtained at least cost. Because efficiency derives from the relationship between resource inputs and outputs, the concepts of efficiency and economy are inextricably linked.

Economic acquisition of resources contributes to efficiency by minimizing the cost of inputs used. Effectiveness questions overlap with and extend beyond efficiency into program effects and impacts outcomes.

Efficiency is closely linked to effectiveness because it is an important factor in determining the least-cost method of achieving desired outcomes. How economy, efficiency, and effectiveness are interrelated is displayed in Figure 3.