A Data Mining Based Approach to Electronic Part Obsolescence Forecasting

TLDR: The use of data mining based algorithms to augment commercial obsolescence risk databases thus improving their predictive capabilities and enabling more accurate Obsolescence forecasts can be demonstrated on both individual parts and modules.

Abstract: Many technologies have life cycles that are shorter than the life cycle of the product they are in. Life cycle mismatches caused by the obsolescence of technology (and particularly the obsolescence of electronic parts) results in high sustainment costs for long field life systems, e.g., avionics and military systems. This paper demonstrates the use of data mining based algorithms to augment commercial obsolescence risk databases thus improving 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.