Forecasting obsolescence risk and product life cycle with machine learning

Connor Jennings, Dazhong Wu, Janis Terpenny

Research output: Contribution to journalArticlepeer-review

21 Scopus citations

Abstract

Rapid changes in technology have led to an increasingly fast pace of product introductions. For long-life systems (e.g., planes, ships, and nuclear power plants), rapid changes help sustain useful life, but at the same time, present significant challenges associated with obsolescence management. Over the years, many approaches for forecasting obsolescence risk and product life cycle have been developed. However, gathering inputs required for forecasting is often subjective and laborious, causing inconsistencies in predictions. To address these issues, the objective of this research is to develop a machine learning-based methodology capable of forecasting obsolescence risk and product life cycle accurately while minimizing maintenance and upkeep of the forecasting system. Specifically, this new methodology enables prediction of both the obsolescence risk level and the date when a part becomes obsolete. A case study of the cell phone market is presented to demonstrate the effectiveness and efficiency of the new approach. Results have shown that machine learning algorithms (i.e., random forest, artificial neural networks, and support vector machines) can classify parts as active or obsolete with over 98% accuracy and predict obsolescence dates within a few months.

Original languageEnglish (US)
Article number7543522
Pages (from-to)1428-1439
Number of pages12
JournalIEEE Transactions on Components, Packaging and Manufacturing Technology
Volume6
Issue number9
DOIs
StatePublished - Sep 2016

All Science Journal Classification (ASJC) codes

  • Electronic, Optical and Magnetic Materials
  • Industrial and Manufacturing Engineering
  • Electrical and Electronic Engineering

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