Measurement Issues and Behavior of Productivity Variables

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Everyone expects a temporary drop in efficiency as equipment is installed and workers learn to use it.

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But managers often underestimate the costly ripple effects of new equipment on inventory, quality, equipment utilization, reject rates, downtime, and material waste. Indeed, these indirect costs often exceed the direct cost of the new equipment and can persist for more than a year after the equipment is installed. Here, then, is the paradox of capital investment. It is essential to long-term productivity growth, yet in the short run, if poorly managed, it can play havoc with TFP.

Putting in new equipment is just as likely to create confusion and make things worse for a number of months. Unless the investment is made with a commitment to continual learning—and unless performance measures are chosen carefully—the benefits that finally emerge will be small and slow in coming.

Still, many companies today are trying to meet their competitive problems by throwing money at them—new equipment and new plants. Our findings suggest that there are other things they ought to do first, things that take less time to show results and are much less expensive. We were not surprised to find a negative correlation between waste rates or the percentage of rejects and TFP, but we were amazed by its magnitude. In the Process plants, changes in the waste rate measured by the ratio of waste material to total cost, expressed as a percentage led to dramatic operating improvements.

The strength of this relationship is more surprising when we remember that a decision to boost the production throughput rate which ought to raise TFP because of the large fixed components in labor and capital costs also causes waste ratios to increase. In theory, therefore, TFP and waste percent should increase together. The fact that they do not indicates the truly powerful impact that waste reduction has on productivity.

The positive effect on TFP of cutting work-in-process WIP inventories for a given level of output was much greater than we could explain by reductions in working capital. These data support the growing body of empirical evidence about the benefits of reducing WIP. From studies of both Japanese and American companies, we know that cutting WIP leads to faster, more reliable delivery times, lowers reject rates faster production cycle times reduce inventory obsolescence and make possible rapid feedback when a process starts to misfunction , and cuts overhead costs.

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We now know it also drives up TFP. The trouble is, simply pulling work-in-process inventory out of a factory will not, by itself, lead to such improvements. More likely, it will lead to disaster. WIP is there for a reason, usually for many reasons; it is a symptom, not the disease itself. A long-term program for reducing WIP must attack the reasons for its being there in the first place: erratic process yields, unreliable equipment, long production changeover and set-up times, ever-changing production schedules, and suppliers who do not deliver on time.

Defective products, mismanaged equipment, and excess work-in-process inventory are not only problems in themselves. They are also sources of confusion. Our answer to this question is an emphatic No!

Responding to new demands and new opportunities requires change, but it does not require the confusion it usually creates. Much of our evidence on confusion comes from factories that belong to the same company and face the same external pressures.


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Some plant managers are better than others at keeping these pressures at bay. The good ones limit the number of changes introduced at any one time and carefully handle their implementation. Less able managers always seem caught by surprise, operate haphazardly, and leapfrog from one crisis to the next. Much of the confusion in their plants is internally generated. But we could see that what managers did to mitigate or fuel confusion within factories at a given level of complexity had a profound impact on TFP.

Of the sources of confusion we examined, none better illustrated this relationship with TFP than engineering change orders.

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ECOs require a change in the materials used to make a product, the manufacturing process employed, or the specifications of the product itself. Moreover, the debilitating effects of ECOs persisted for up to a year. Our data suggest that the average level of ECOs implemented in a given month, as well as the variation in this level, is detrimental to TFP. Many companies would therefore be wise to reduce the number of ECOs to which their plants must respond. This notion suggests, in turn, that more pressure should be placed on engineering and marketing departments to focus attention on only the most important changes—as well as to design things right the first time.

Essential ECOs should be released in a controlled, steady fashion rather than in bunches. More expensive ECOs actually had a positive effect. The reason: plant managers usually had warning of major changes and, recognizing that they were potentially disruptive, carefully prepared the ground by warning supervisors, training workers, and bringing in engineers.


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  • By contrast, minor ECOs were simply dumped on the factory out of the blue. If setting up adequate measures of performance is the first step toward getting full competitive leverage out of manufacturing, identifying factory-level goals like waste or WIP reduction is the second. But without making a commitment to ongoing learning, a factory will gain no more from these first two steps than a one-time boost in performance.

    To sustain the leverage of plant-level operations, managers must pay close attention to—and actively plan for—learning. Confusion, as we have seen, is especially harmful to TFP.

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    Thus the two essential tasks of factory management are to create clarity and order that is, to prevent confusion and to facilitate learning. Not at all. Confusion, like noise or static in an audio system, makes it hard to pick up the underlying message or figure out the source of the problem. It impedes learning, which requires controlled experimentation, good data, and careful analysis. Worse, engineers, supervisors, operators, and managers easily become discouraged by the futility of piecemeal efforts.

    In such environments, TFP lags or falls. Reducing confusion and enhancing learning do not conflict. They make for a powerful combination—and a powerful lever on competitiveness. A factory that manages change poorly, that does not have its processes under control, and that is distracted by the noise in its systems learns too slowly, if at all, or learns the wrong things.

    In such a factory, new equipment will only create more confusion, not more productivity. Many companies have tried to solve their data-processing problems by bringing in computers. They soon learned that computerizing a poorly organized and error-ridden information system simply creates more problems: garbage in, garbage out. Of course, advanced technology is important, often essential. But there are many things that managers must do first to prepare their organizations for these new technologies. When plant managers are stuck with poor measures of how they are doing and when a rigid, by-the-book emphasis on standards, budgets, and exception reports discourages the kind of experimentation that leads to learning, the real levers on factory performance remain hidden.

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    Measuring labor productivity gives an estimate of the combined effects of these underlying trends. Labor productivity can also indicate short-term and cyclical changes in an economy. If the output is increasing while labor hours remains static, it signals that the labor force has become more productive. In addition to the three traditional factors outlined above, this is also seen during economic recessions , as workers increase their labor effort when unemployment rises and the threat of lay-offs looms to avoid losing their jobs.

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