Machine Learning Based Prediction of Obsolescence Risk

Authors

  • Ajay K. Dass Department of Electronics & Telecommunication Engineering, Sinhgad College of Engineering, Pune, India.
  • S. D. Lokhande Department of Electronics & Telecommunication Engineering, Sinhgad College of Engineering, Pune, India.

Keywords:

Stages of the life cycle, machine learning, obsolescence, maintenance for computer equipment

Abstract

Rapid technological ups and downs have led to an increasingly quick jump in product overviews. Fast ups and downs enable useful life for long-life schemes, but they also provide significant issues for managing obsolescence when combined. Numerous techniques for anticipating obsolescence risk and product life cycle have been developed over time. However, gathering the data necessary for prediction is frequently difficult and independent, leading to disparities in forecasts. The goal of this paper is to develop a ML based system capable of accurately forecasting obsolescence risk and product life cycle while minimizing maintenance and upkeep of the predicting scheme in order to report these issues. Specifically, this innovative approach enables prediction of the obsolescence risk level as well as the timeframe during which a part becomes obsolete. A case study of the computer sale is presented to demonstrate the value and potency of the unique approach.

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Graphical presentation of computer products sale

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Published

13.02.2023

How to Cite

K. Dass , A. ., & Lokhande , S. D. . (2023). Machine Learning Based Prediction of Obsolescence Risk . International Journal of Intelligent Systems and Applications in Engineering, 11(4s), 293–301. Retrieved from https://ijisae.org/index.php/IJISAE/article/view/2667

Issue

Vol. 11 No. 4s (2023)

Section

Research Article

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Copyright (c) 2023 Ajay K. Dass , S. D. Lokhande

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