The following is a revision of a posting on NWLEAN in January, 2011 in response to Mike Thelen’s call for “Laws of nature” in manufacturing.

Learning curves are often mentioned informally, as in “there is a learning curve on this tool,” just to say that it takes learning and practice to get proficient at it. There is, however, a formal version expressing costs as a function of cumulative production volume during the life of a manufactured product. T. P. Wright first introduced the *learning curve* concept in the US aircraft industry in 1936, about labor costs; Bruce Henderson generalized in the *experience curve*, to include all costs , particularly those of purchased components.

The key idea is to look at *cumulative* volume. After all, how many units of a product you have made since you started *is* your experience, and it stands to reason that, the more you have already made of a product, the easier and cheaper it becomes for you to build one more. The x-axis of the experience curve is defined clearly and easily. The y-axis, on the other hand, is the cost per unit of the product, one of the characteristics that are commonly discussed as if they were well-defined, intrinsic properties like weight and color. They really are a function of current production volume, and contain allocations that can be calculated in different ways for shared resources and resources used over time. The classic reference on the subject, Bruce Henderson’s Perpectives on Experience (1972), glosses over these difficulties and presents empirical evidence about *prices* rather than costs.

Assuming an unambiguous and meaningful definition of unit costs, it is reasonable to assume that they would decline as experience in making the product accumulates. But what might be the shape of the cost decline curve? Engineers like to plot quantities and look for straight lines on various kinds of graph paper. Even before looking at empirical data, we can reflect on the logic of the most common types of models:

- In a plot of unit cost versus cumulative volume in regular, Cartesian coordinates, a straight line means a linear cost decline, which makes no sense because you would end up with negative costs for a sufficiently large volume.
- In a semi-logarithmic plot, a straight line would mean an exponential cost decline, which makes no sense either, because you could make an infinite volume at a finite cost.
- If you try a log-log plot, a straight line means an inverse-power cost decline, meaning, for example, the unit cost drops by 20% every time the cumulative volume doubles. This approach has none of the above problems. It represents a smooth decline as long as production continues, slow enough that the cumulative costs keeps growing to infinity with the volume.

I don’t know of any deeper theoretical justification for using inverse-power laws in learning or experience curves. Henderson, investigated the prices of various industrial products. I remember in particular his analysis of the Ford Model T, which showed prices from 1908 to 1927 that were consistent with a fixed percentage drop in unit costs for each doubling of the cumulative volume. The prices followed an obvious straight line on a log-log plot, suggesting that the costs did the same below.

Today, you don’t hear much about experience curves in the car industry, but you do in Electronics, where products have much shorter lives and this curve is a key factor in planning. When working in semiconductors, I remember a proposal from a Japanese electronics manufacturer that was designing one of our chips into a product. Out of curiosity, I plotted the declining prices they were offering to pay for increasing quantities on log-log scales, and found that they were perfectly aligned. There was no doubt that this was how they had come up with the numbers.

The slope of your own curve is a function of your improvement abilities. Your market share then determines where you are on the x-axis. The higher your market share the faster your cumulative production volume grows. Being first lets you to grab market share early; being farther along the curve than your competitors allows you to retain it.

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Jan 21 2011

## Learning or experience curves

The following is a revision of a posting on NWLEAN in January, 2011 in response to Mike Thelen’s call for “Laws of nature” in manufacturing.

Learning curves are often mentioned informally, as in “there is a learning curve on this tool,” just to say that it takes learning and practice to get proficient at it. There is, however, a formal version expressing costs as a function of cumulative production volume during the life of a manufactured product. T. P. Wright first introduced the

learning curveconcept in the US aircraft industry in 1936, about labor costs; Bruce Henderson generalized in theexperience curve, to include all costs , particularly those of purchased components.The key idea is to look at

cumulativevolume. After all, how many units of a product you have made since you startedisyour experience, and it stands to reason that, the more you have already made of a product, the easier and cheaper it becomes for you to build one more. The x-axis of the experience curve is defined clearly and easily. The y-axis, on the other hand, is the cost per unit of the product, one of the characteristics that are commonly discussed as if they were well-defined, intrinsic properties like weight and color. They really are a function of current production volume, and contain allocations that can be calculated in different ways for shared resources and resources used over time. The classic reference on the subject, Bruce Henderson’s Perpectives on Experience (1972), glosses over these difficulties and presents empirical evidence aboutpricesrather than costs.Assuming an unambiguous and meaningful definition of unit costs, it is reasonable to assume that they would decline as experience in making the product accumulates. But what might be the shape of the cost decline curve? Engineers like to plot quantities and look for straight lines on various kinds of graph paper. Even before looking at empirical data, we can reflect on the logic of the most common types of models:

I don’t know of any deeper theoretical justification for using inverse-power laws in learning or experience curves. Henderson, investigated the prices of various industrial products. I remember in particular his analysis of the Ford Model T, which showed prices from 1908 to 1927 that were consistent with a fixed percentage drop in unit costs for each doubling of the cumulative volume. The prices followed an obvious straight line on a log-log plot, suggesting that the costs did the same below.

Today, you don’t hear much about experience curves in the car industry, but you do in Electronics, where products have much shorter lives and this curve is a key factor in planning. When working in semiconductors, I remember a proposal from a Japanese electronics manufacturer that was designing one of our chips into a product. Out of curiosity, I plotted the declining prices they were offering to pay for increasing quantities on log-log scales, and found that they were perfectly aligned. There was no doubt that this was how they had come up with the numbers.

The slope of your own curve is a function of your improvement abilities. Your market share then determines where you are on the x-axis. The higher your market share the faster your cumulative production volume grows. Being first lets you to grab market share early; being farther along the curve than your competitors allows you to retain it.

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By Michel Baudin • Laws of nature •