Planned obsolescence (in parts)

In response to my post on planned obsolescence, some have pointed out that a good is composed of many different inputs. If there are differences in the quality of the different parts of a good, then it might be rational to reduce its lifespan.

That is a important possibility which I should have considered. Imagine that good 1 is composed of parts A, B and C whose lifespan are 1, 2 and 3 years. The manufacturer of good 1 will converge one the lifespan which, in relative terms, will maximize his profits. If it costs more to bring part A to the lifespan of part C than it is to bring part C to the lifespan of part A, then a lower total lifespan would be appreciable.  That decision reduces the lifespan and the marginal cost which means that a greater quantity of goods can be consumed than if we “over-engineer” by bringing A to the level of C. This point was brought to my attention by Michael Makovi, a graduate student at Texas Tech University’s department of agricultural economics. In his words:

The corporate executive types told the engineers: If one part of the product can last X years but the other part of the machine can last Y years, then under-engineer the longer-lasting parts so that the whole product uniformly lasts the lowest-common-denominator of time, so that when the product fails, the customer didn’t waste money on over-engineering other parts of the product to last longer (…) engineers balked because it seemed immoral, but the executives assured them that it was in the customer’s own best interest. For example (…) if one part of your refrigerator lasts 10 years but another part lasts 20 years, and if the 10 year part cannot be replaced, so you have to replace the whole refrigerator at once, then over-engineering the other part to last 20 years is a waste of money.


2 thoughts on “Planned obsolescence (in parts)

  1. Any engineer can tell you this is wrong.
    If one part lasts on average 10 years, and another part 10years, than on average you have a product that lasts 5 years on average (depends on the failure distribution).

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