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

Moon, Kyoung-Sook; Lee, Hee Won; Kim, Hee Jean; Kim, Hongjoong; Kang, Jeehoon; Paik, Won Chul

doi:10.3390/s22093244

Cited by 3 publications

(2 citation statements)

References 19 publications

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“…The idea is that being proactive in this area allows the business to cut costs by improving the organizational efficiency, and at the same time provide a better customer experience. AI can be employed to predict the obsolescence date of products based on individual wear and tear [30]. Little is known about the magnitude of the impact by forecasting obsolescence to prioritize preventative actions and avoid costs.…”

Section: Retentionmentioning

confidence: 99%

Preliminary Results on the use of Artificial Intelligence for Managing Customer Life Cycles

Ahlstrand¹,

Boldt²,

Borg³

et al. 2023

Linköping Electronic Conference Proceedings

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During the last decade we have witnessed how artificial intelligence (AI) have changed businesses all over the world. The customer life cycle framework is widely used in businesses and AI plays a role in each stage. However, implementing and generating value from AI in the customer life cycle is not always simple. When evaluating the AI against business impact and value it is critical to consider both the model performance and the policy outcome. Proper analysis of AI-derived policies must not be overlooked in order to ensure ethical and trustworthy AI. This paper presents a comprehensive analysis of the literature on AI in customer life cycles (CLV) from an industry perspective. The study included 31 of 224 analyzed peer-reviewed articles from Scopus search result. The results show a significant research gap regarding outcome evaluations of AI implementations in practice. This paper proposes that policy evaluation is an important tool in the AI pipeline and empathizes the significance of validating both policy outputs and outcomes to ensure reliable and trustworthy AI.

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Section: Retentionmentioning

confidence: 99%

Preliminary Results on the use of Artificial Intelligence for Managing Customer Life Cycles

Ahlstrand¹,

Boldt²,

Borg³

et al. 2023

Linköping Electronic Conference Proceedings

View full text Add to dashboard Cite

show abstract

“…The limited success of ML or DL methods for obsolescence prediction can be attributed to the scarcity of available data. To address this problem, Moon et al [ 14 ] proposed using the k -means clustering method before applying a ML algorithm and employed this approach to improve the forecasting accuracy of the obsolescence date of electronic diodes.…”

Section: Introductionmentioning

confidence: 99%

Adaptive Data Selection-Based Machine Learning Algorithm for Prediction of Component Obsolescence

Moon

Lee

Kim

2022

Sensors

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Product obsolescence occurs in the manufacturing industry as new products with better performance or improved cost-effectiveness are developed. A proactive strategy for predicting component obsolescence can reduce manufacturing losses and lead to customer satisfaction. In this study, we propose a machine learning algorithm for a proactive strategy based on an adaptive data selection method to forecast the obsolescence of electronic diodes. Typical machine learning algorithms construct a single model for a dataset. By contrast, the proposed algorithm first determines a mathematical cover of the dataset via unsupervised clustering and subsequently constructs multiple models, each of which is trained with the data in one cover. For each data point in the test dataset, an optimal model is selected for regression. Results of empirical experiments show that the proposed method improves the obsolescence prediction accuracy and accelerates the training procedure. A novelty of this study is that it demonstrates the effectiveness of unsupervised clustering methods for improving supervised regression algorithms.

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Sales prediction hybrid models for retails using promotional pricing strategy as a key demand driver

Phumchusri,

Phupaichitkun

2024

J Revenue Pricing Manag

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

Cited by 3 publications

References 19 publications

Preliminary Results on the use of Artificial Intelligence for Managing Customer Life Cycles

Preliminary Results on the use of Artificial Intelligence for Managing Customer Life Cycles

Adaptive Data Selection-Based Machine Learning Algorithm for Prediction of Component Obsolescence

Sales prediction hybrid models for retails using promotional pricing strategy as a key demand driver

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