University of Maryland
College Park, MD 20742
Frank Mauro and Ron Knox
PartMiner Information Systems, Inc.
Part obsolescence dates (the date on which the part is no longer procurable from its original source) are important inputs during design planning. Most electronic part obsolescence forecasting algorithms are based, at least in part, on the development of models for the partís lifecycle. Traditional methods of life cycle forecasting utilized in commercially available tools and services are ordinal scale based approaches, in which the life cycle stage of the part is determined from a combination of technological and market attributes (e.g., TACTrac, Q-Star, Total Parts Plus). Analytical models based on technology and/or market trends have also appeared including a methodology based on forecasting part sales curves, and leading-indicator approaches.
Existing commercial forecasting tools are good at articulating the current state of a partís availability and identifying alternatives, but limited in their capability to forecast future obsolescence dates and do not generally provide quantitative confidence limits when predicting future obsolescence dates or risks. More accurate forecasts, or at least forecasts with a quantifiable accuracy would open the door to the use of lifecycle planning tools that could lead to more significant sustainment cost avoidance.
This paper demonstrates the use of data mining based algorithms to augment commercial obsolescence risk databases by increasing their predictive capabilities. The method is a combination of life cycle curve forecasting and the determination of electronic part vendor-specific windows of obsolescence using data mining of historical last-order or last-ship dates. The extended methodology not only enables more accurate obsolescence forecasts but can also generate forecasts for user-specified confidence levels. The methodology has been demonstrated on both individual parts and modules.
While successful electronic part obsolescence forecasting involves more than just predicting part-specific last order dates, being able to predict original vendor last order dates more accurately using a combination of market trending and data mining is an important component of an overall obsolescence risk forecasting strategy.
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