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This was last updated in November Related Terms application program interface API An application program interface API is code that allows two software programs to communicate with each other. Login Forgot your password? Forgot your password? No problem! Submit your e-mail address below. Here are some possible reasons:. The last point is particularly important. Yet most managements have failed to recognize that technological progress is a kind of learning.
Assigning specialists to seek technical improvements and to incorporate them in operations obviously helps bring about improvement. The industrial learning curve thus embraces more than the increasing skill of an individual by repetition of a simple operation. Instead, it describes a more complex organism—the collective efforts of many people, some in line and others in staff positions, but all aiming to accomplish a common task progressively more efficiently.
It seems preferable to retain the general name, but to remember that it can be broken down into meaningful terms which are more specific. If learning curve performance is a natural characteristic, then such performance should be found not only for more types of activities already recognized as responsive, but also for unlikely operations such as those not previously reported or believed susceptible. Petroleum refining offers a good example of the type of industry to which the learning curve might be thought to be inapplicable.
It is characterized by large investments in heavy equipment, and is so highly automated that learning is thought to be either nonexistent or too small to be of value. Let us see how true this belief is. Petroleum refining comprises such process operations as distillation, cracking, and reforming.
From time to time, scattered observations have appeared in the literature to the effect that the capacity of some of the units performing these operations is larger than their design capacity.
In , the worldwide, installed design capacity of fluid catalytic cracking units was 1,, barrels per stream day; however, the actual throughput was about one-third greater—as much as 1,, barrels per stream day! This aggregate is composed of the throughputs of the individual cracking units at a point in time.
It does not show how they changed over time. But by plotting the performance of individual units at a point in time against their age at that time, we obtain a clue to this pattern; see Exhibit III. The points are the ratios of the achieved capacity to the design capacity as determined from published tabulations.
The selected points of Exhibit III were calculated from these data.
On the learning curve: transforming education outcomes in India
In general, as the older units show C, D, E, F, G, and H , performance rapidly improved in the first few years, and continued at a slower rate in later years. Exhibit III. Exhibit IV shows that successive annual points for an individual cracking unit indicate that growth occurs in a step-wise fashion, so that the points are scattered in a band instead of lying on a smooth curve. The pattern of improvement indicated by the colored line, which is the same curve as the one in Exhibit III, resembles the inverse of a learning curve on an arithmetic chart.
If the parameters are changed so that the number of days to process , barrels is plotted against cumulated throughput on a logarithmic chart, a declining straight line can be drawn through the points see Exhibit V. Exhibit IV. Exhibit V. What accounts for this improvement in performance? Safety margins for critical equipment are included during design stages of a project to ensure getting required design performance. Thus, actual performance can and should be higher than the design target. Operators will soon learn to take advantage of built-in safety margins.
However, equipment not considered critical in design and without extra safety margins may limit initial performance to the design target. Obviously, removal of such a bottleneck can result in marked improvement. But as time passes, fewer and fewer bottlenecks remain to be uncovered, so progress slows. These circumstances explain a relatively rapid early growth and a subsequent gradual slowing down as more and more capital and ingenuity are required to eliminate further bottlenecks.
In this industry with its heavy dependence on machinery, the improvement curve appears to reflect technological resourcefulness. So it seems reasonable to believe that this technical skill will continue to result in such enhancement patterns so long as increased demand or other incentives occur to prod the search for improvement, and so long as it is backed up by the present level of research and engineering efforts.
But if learning is greatest where the most people are involved, the learning curve should be revealed most clearly in refinery operations which have a high labor content. One such circumstance would be the start-up of units after shutdown periods for repairs. Specialists are on hand to assure proper functioning of instruments. Extra craft people are assigned to handle emergencies. More supervisors are present to give guidance.
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The regular crew is particularly busy in routing flows, opening and closing valves, lining out system components, and operating manual controls before the automatic ones are cut in. This relatively high human activity content of start-ups suggests that the people involved should be susceptible to a significant degree of learning. And actual experience confirms this. Over a period of ten years the time necessary to put a Whiting refinery fluid cracking unit of the American Oil Company on stream dropped to less than half the time initially required.
In view of the repetitive nature of much maintenance work and the continuing efforts made to organize for better efficiency, the performance of a maintenance department as a whole might be expected to show progressive learning. And, as Exhibit VI shows, this expectation is sound. The points in the exhibit show a declining trend for productive labor man-hours on maintenance and shutdowns during the years — At the end of that period the plot seems to be leveling off. If the management had speculated then about this curve, it might have felt that it had reached a plateau, that maintenance had learned how best to do the jobs required so that a further decline would not occur, at least for a while.
But actually, as the point plotted as a cross shows, the trend continued, ending up about where it should have been expected. Exhibit VI. This is not difficult to explain. Department performance is a composite of many individual tasks.
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And if it is a reflection of learning, then individual maintenance operations should follow established learning curves. This expectation is confirmed not only in refining but also in other large manufacturing plants. Exhibit VII. Building new items of heavy equipment also appears to be characterized by learning. The per-barrel investment costs of units for some individual processes thermal cracking, polymerization, catalytic cracking, and catalytic reforming also decline progressively. In the case of thermal cracking, the decline continued for 33 years.
Learning curve definition and meaning | Collins English Dictionary
A learning curve measure of the rate of decline for fluid catalytic cracking units is suggested by the observation that the steel required and the investment cost in were estimated to be one-third of those required to duplicate the capacity of the original downflow fluid plant, which was built in During the intervening years, about 3 million barrels of fluid cracking capacity were built. Exhibit VIII. Note that price rises can distort the picture.
However, since such construction is largely assembly work, this finding is consistent with the general learning curve experience: that is, operations with similar ratios of assembly to machine work have similar learning curve slopes. This decline reflects learning by construction people in how to build so as to reduce unit costs, and by research, engineering, and operating people in what to build. It provides a clue to the relative contributions of capital and of technology to learning curve performance.
The fact that a plant built to duplicate the performance of an original one could be built with one-third the steel indicates that the what-to-build contributions of the progressively improved technology embodied in successive plants greatly outweighs the how-to-build contributions of capital for the better tools and equipment employed in building them. The great extent to which technology can be dominant over investment has been shown by three recent studies. Technology also contributes to building larger plants with attendant economies, because construction costs do not increase proportionately with size.
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Doubling the capacity of a plant does not necessarily double the cost. Rather, it increases it by some lesser amount, which can be represented by an exponent of the size.
A plant twice as large may cost about 2 0. If this 0. Since plants have been built two or more times larger than originally planned, the economies of size from progress in research and engineering also contributed to offsetting the effects of inflation.
Exhibit IX. In this instance, technological progress did decrease costs more than inflation increased them. This circumstance might suggest that depreciation allowances have been more than adequate to provide the capital necessary to replace units when they are retired.
Actually, rapid obsolescence is concomitant with rapid technological progress. A competing firm with a new, low-cost unit has the advantage of a smaller capital charge for its products and can thereby profitably sell them at lower prices. To survive in the face of such competition, companies may be forced to replace existing units before they are fully amortized and before depreciation reserves have become large enough to pay for the replacements. And this is particularly true for units depreciated by the straight-line and other methods required for equipment installed before An industry is an aggregate of components.
We can reason that if learning in components is widespread, it should be reflected in aggregate performance. A logarithmic plot in Exhibit X of man-hours per barrel versus cumulated barrels of crude oil refined in the United States since results in the fairly regular type of decline such reasoning would lead us to expect.
Exhibit X. Man-hours per Barrel Refined in the Petroleum Industry. Other industries show similar declines, as illustrated by Exhibit XI for the U. Exhibit XI.