Since neither lends itself well to direct measurement, productivity technicians often prefer to look the other way. Managers do so at their peril. The first oversight, time, is not purchased, so it is usually ignored. If two businesses use identical machines, the same number of people, and equivalent materials to produce identical products, most productivity indexes would produce identical scores. But suppose one business ships orders within three days of receiving them and the other takes three weeks.
Is their productivity the same? Obviously not. This is not an exaggerated example. Increasingly, companies are discovering the competitive power of shortening their production cycles—or the dangers of not doing so.
But unless a productivity index assigns some value to the amount of time consumed, it is unrealistic to expect managers to focus on shortening turnaround times. Assigning inventory carrying costs is a step in the right direction, although most companies record carrying charges far below their true competitive costs. Carrying costs should not only be realistic but they should also reflect where the inventory sits in terms of value added as well as how long it sits.
An additional time-based charge that captures how long it takes to complete an order can focus attention even more directly on possible gains in turnaround time. The new head of a sheet metal plant owned by a major electronics company learned this lesson soon after he took over. But that represented a misunderstanding of its mission. So the new manager introduced a productivity index that focused on turnaround time, and he posted the results prominently.
Eventually, the plant cut prototype production time from 20 weeks to three days. The second crucial but often overlooked aspect of many productivity measurement systems regards whose performance is being measured.
Most systems target inputs on the shop floor, but manufacturing efficiency is not only a function of who and what are located there. Engineers, supervisors, and other white-collar employees make significant contributions to manufacturing productivity, but few systems measure their roles.
The Northern Telecom system cited earlier is a notable exception. To a large extent, the absence of such measures reflects two principal difficulties of quantifying productivity in any service setting: measuring output and connecting employee actions to outputs.
For the line worker in an auto plant, output is basically the number of cars or components produced. The connection between worker activity and output is also straightforward—the person tightens three bolts on every car, and this action helps complete the car. Measuring the productivity of product designers is a much more subtle problem. Designing an item to make production smoother will improve the efficiency of the entire plant, for example.
If such a design takes twice as long to complete as a simpler approach, it certainly does not mean that the engineer is less productive. It does mean, however, that managers must be creative and open to new ways of thinking about an operation. A plant manager of an important supplier to the auto industry met with resistance at headquarters to his request to augment his engineering staff.
He knew that the additional money would be well spent, but he had no measurement system for making his case. Indeed, over time, as the size and expertise of the engineering group increased, the ratio of total output to materials input showed dramatic increases. Are they true gauges of productivity? For the economist or measurement specialist, no. Can they focus managers and employees on critical aspects of the production process and, therefore, lead to improved performance?
Management carries the burden of usage almost entirely. Productivity indexes today are being used to compare the performance of companies in an industry, plants in a company, and departments in a plant.
The results influence investment choices, judgments about factory closings, and decisions on management compensation, so managers must be careful to make fair comparisons. What is fair is not always obvious. Consider some of the ambiguities on the output side of the productivity ratio. Exhibit II describes the output in and of a hypothetical plant making two related products.
In , the price of product A rose, so many customers switched to product B. From the set of facts presented, what conclusions can be drawn about the change in output—and, therefore, the change in productivity? Depending on your point of view, output went up, down, or stayed the same. If you look at nominal revenues, output rose dramatically. If you adjust for the price change by comparing revenues using prices, output went down. If you focus on physical units, output stayed the same.
You might look to standard costs for guidance, but they may also present a confusing picture as well as concerns about accuracy. What really happened at the company depends on what really happened at the plant and in the marketplace, not in the numbers.
Was product A radically redesigned? Was the old price relationship between the two products somehow incorrect? Was there a dramatic change in input costs for product A? A manager must consider questions like these before evaluating productivity trends in such a case.
Price changes, of course, are not the only important factor affecting output. Quality has an impact on productivity measures. The most productive plant or company does not necessarily have the lowest cost per unit of output.
It does have the lowest cost per comparable unit of output, though. If the two businesses produce the same number of tires, it is not immediately clear which is more productive. Or suppose one enterprise, by virtue of a product development breakthrough, uses the same number of workers and machines to manufacture one million 15,mile tires in and one million 30,mile tires in Ignoring price for the moment, is output and thus productivity constant?
Comparing the performance of plants making different products requires a method to determine equivalencies. The three most common alternatives are standard costs, price, and technical parameters like miles of tire life that quantify product performance.
If a company is and wants to remain a low-cost producer, it might focus on prices. If it wants to promote innovation, it might use technical parameters. Standard costs will focus attention on internal improvements independent of developments in the marketplace. Managers also need to interpret trends, which can create further ambiguities. There is a fundamental distinction, for example, between levels of productivity and rates of productivity change.
But whose performance should management worry about? The result generally compares units of work per units of time. These units change, based on factors such as industry or department, and individual businesses may analyze their productivity differently than others in the same sector. Productivity may even be calculated on sections within processes, such as the operations a machine can perform in a given time. In this example, productivity becomes a measure of throughput.
Productivity is a measure of quantity, so it serves only as a starting point for analysis. Considering the examples above, the sales department may have a high call volume but low purchase response; the housekeeper could increase her room count while complaints from arriving guests climb; the manufacturer may see both record output and record waste, reducing the number of pieces it can sell.
Effective measures of efficiency require adjustment to the gross output amount or an output unit that factors out poor quality or wasted effort. Because key productivity measures vary widely across enterprises, outputs and inputs change as needed to pinpoint the key analytics. Outputs may include sales amounts, units produced or activities performed, while typical inputs are often time-based, usually labor hours.
If poor lighting and noise cause fatigue, examine improvements you can make in the workplace. Choose your productivity improvements and implement them. Measure productivity after improvements. Again, divide the number of units completed by the number of hours required to make them.
After making improvements, you might find that four units were completed in two hours. Your employees are now making two units per hour. Subtract the old production rate from the new. In the example, employees produce two units per hour, an improvement of. This is your production improvement figure.
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