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Strategies to the Prediction, Mitigation and Management of Product Obsolescence by Michael G. Pecht, Peter Sandborn, Ulrich Ermel, Bjoern Bartels

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4.1 OBSOLESCENCE FORECASTING—PARTS WITH EVOLUTIONARY PARAMETRIC DRIVERS

This section presents forecasting models that are applicable to electronic parts that have clearly identifiable evolutionary parametric drivers. An evolutionary parametric driver is a parameter (or a combination of parameters) describing a part that evolves over time. For example, for flash memory chips, an evolutionary parametric driver is memory size, whereas for microprocessors it has traditionally been clock frequency (although recently this has begun to give way to power consumption). Section 4.1.1 describes the basic life cycle curve forecasting method, and Section 4.1.2 describes the extension of the life cycle curve forecasting method that uses data mining to determine the window of obsolescence of the parts.

4.1.1 Basic Life Cycle Curve Forecasting Method (Solomon et al., 2000)

This method is based upon the life cycle curve description of an electronic part discussed in Chapter 3. In Figure 4-1, the light efficiency of various types of lamps is plotted versus time. The light efficiency is a clear evolutionary parametric driver for lamps. The maturity of each lamp technology is clearly correlated to the appearance of a more efficient lamp technology. Notice that technologies “overlap,”: the introduction of new technology does not generally lead to the predecessor’s immediate obsolescence. The obsolescence forecasting methodology described in this section is based on predicting the life cycle curves ...

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