Forecasting obsolescence risk using machine learning

Connor Jennings, Dazhong Wu, Janis Terpenny

Research output: Chapter in Book/Report/Conference proceedingConference contribution

1 Scopus citations

Abstract

With rapid innovation in the electronics industry, product obsolescence forecasting has become increasingly important. More accurate obsolescence forecasting would have cost reduction effects in product design and part procurement over a product's lifetime. Currently many obsolescence forecasting methods require manual input or perform market analysis on a part by part basis; practices that are not feasible for large bill of materials. In response, this paper introduces an obsolescence forecasting framework that is capable of being scaled to meet industry needs while remaining highly accurate. The framework utilizes machine learning to classify parts as active, in production, or obsolete and discontinued. This classification and labeling of parts can be useful in the design stage in part selection and during inventory management with evaluating the chance that suppliers might stop production. A case study utilizing the proposed framework is presented to demonstrate and validate the improved accuracy of obsolescence risk forecasting. As shown, the framework correctly identified active and obsolete products with an accuracy as high as 98.3%.

Original languageEnglish (US)
Title of host publicationMaterials; Biomanufacturing; Properties, Applications and Systems; Sustainable Manufacturing
PublisherAmerican Society of Mechanical Engineers
ISBN (Electronic)9780791849903
DOIs
StatePublished - 2016
EventASME 2016 11th International Manufacturing Science and Engineering Conference, MSEC 2016 - Blacksburg, United States
Duration: Jun 27 2016Jul 1 2016

Publication series

NameASME 2016 11th International Manufacturing Science and Engineering Conference, MSEC 2016
Volume2

Other

OtherASME 2016 11th International Manufacturing Science and Engineering Conference, MSEC 2016
CountryUnited States
CityBlacksburg
Period6/27/167/1/16

All Science Journal Classification (ASJC) codes

  • Industrial and Manufacturing Engineering

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