The Idea in Brief

“Whoever came up with this has no idea what my business is like.” This is the common lament of managers confronted with overly complicated models for measuring productivity. 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.

Fortunately, you don’t need to get a Ph.D. in order to learn how to obtain solid, meaningful productivity measurements. 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.

The Idea in Practice

What is productivity anyway? It’s not about wages—in fact, it’s not about any specific set of costs. 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.

What’s important to realize here is that labor is only one element in productivity measurement. One U.S. plant manager discovered that nearly 40% of his company’s productivity-improvement budget was allocated to upgrading direct labor efficiency—even though direct labor accounted for only 10% of manufacturing costs. 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.

But don’t sacrifice function for form. 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.

A few additional guidelines:

  • Include time in what you measure. Because time isn’t purchased, it’s often ignored in productivity analysis. But the competitive power of shorter production cycles is clear.
  • Find ways to measure white-collar productivity. The guy who tightens bolts on the assembly line makes an obvious contribution to the product. But how do you measure what you get from, say, the product designers? One plant manager found that the bigger and smarter his engineering team, the better the company’s materials productivity.
  • Compare apples to apples. For the insights to be relevant, comparisons between departments or companies must be made appropriately. And productivity measures that influence such things as people’s compensation must be accurate and fair.
  • Interpret trends. Plant A may seem to have better productivity than Plant B, but if Plant B is improving and Plant A’s trend lines are headed south, the situation deserves a second look.

A few years ago, a major manufacturing-based conglomerate asked a gifted mathematician to join its corporate staff. One of his first assignments was to design a system that senior managers could use to evaluate the operating efficiency of the company’s various divisions. 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.

A version of this article appeared in the January 1988 issue of Harvard Business Review.