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Forecasting Obsolescence of Components by Using a Clustering-Based Hybrid Machine-Learning Algorithm.

Kyoung-Sook Moon¹, Hee Won Lee¹, Hee Jean Kim²

¹Department of Mathematical Finance, Gachon University, 1342 Seongnamdaero, Sujeong-gu, Seongnam-si 13120, Gyeonggi-do, Korea.

Sensors (Basel, Switzerland)

|May 20, 2022

Summary

This summary is machine-generated.

This study introduces a machine learning algorithm to predict electronic diode obsolescence dates, improving accuracy for components with limited data. The method uses clustering and a hybrid approach for better manufacturing planning and customer satisfaction.

Keywords:

components obsolescence diminishing manufacturing sources and material shortages forecasting machine learning unsupervised clustering

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Area of Science:

Engineering
Computer Science
Materials Science

Background:

Product obsolescence is a significant challenge in manufacturing, impacting costs and customer satisfaction.
Proactive obsolescence management strategies are crucial for mitigating manufacturing losses.
Forecasting obsolescence dates for electronic components, like diodes, is complex due to data limitations.

Purpose of the Study:

To develop a machine learning algorithm for accurate prediction of electronic diode obsolescence dates.
To address the challenge of limited data availability in obsolescence forecasting.
To enhance manufacturing planning and improve customer satisfaction through proactive obsolescence management.

Main Methods:

An unsupervised clustering algorithm was employed to group data based on similarity.
Specialized machine learning models were developed for each identified data cluster.
A hybrid prediction method combining multiple reliable techniques was constructed to enhance accuracy.

Main Results:

The proposed algorithm successfully forecasts obsolescence dates for electronic diodes.
Clustering-based grouping and specialized models improved prediction accuracy.
The hybrid method effectively overcame limitations posed by scarce data.

Conclusions:

The developed clustering-based hybrid machine learning method significantly improves obsolescence date prediction accuracy for electrical components.
This proactive approach aids in mitigating manufacturing losses and enhancing customer satisfaction.
The findings offer a valuable tool for managing product lifecycles in the electronics industry.