| Autor: |
K. Dass , Ajay, Lokhande , S. D. |
| Jazyk: |
angličtina |
| Rok vydání: |
2023 |
| Předmět: |
|
| Zdroj: |
International Journal of Intelligent Systems and Applications in Engineering; Vol. 11 No. 4s (2023); 293-301 |
| ISSN: |
2147-6799 |
| Popis: |
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. |
| Databáze: |
OpenAIRE |
| Externí odkaz: |
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