A survey on potentials, pathways and challenges of large language models in new-generation intelligent manufacturing

Zhang, Chao; Xu, Qingfeng; Yu, Yongrui; Zhou, Guanghui; Zeng, Keyan; Chang, Fengtian; Ding, Kai

doi:10.1016/j.rcim.2024.102883

Robotics and Computer-Integrated Manufacturing

2025

DOI: 10.1016/j.rcim.2024.102883

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A survey on potentials, pathways and challenges of large language models in new-generation intelligent manufacturing

Chao Zhang,

Qingfeng Xu,

Yongrui Yu

et al.

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A Novel Autoencoder-Integrated Clustering Methodology for Inventory Classification: A Real Case Study for White Goods Industry

Keskin,

Taskin

2024

Sustainability

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This article presents an inventory classification method that provides more accurate results in the white goods factory, which will contribute to sustainability, sustainability economics, and supply chain management targets. A novel inventory classification application is presented with real-world data. Two different datasets are used, and these datasets are compared to each other. These larger dataset is Stock Keeping Unit (SKU)-based (6.032 SKUs), and the smaller one is product-group-based (270 product groups). In the first phase, Artificial Intelligence (AI) clustering methods that have not been used in the field of inventory classification, to our knowledge, are applied to these datasets; the results are obtained and compared using K-Means, Gaussian mixture, agglomerative clustering, and spectral clustering methods. In the second stage, an autoencoder is separately hybridized with the AI clustering methods to develop a novel approach to inventory classification. Fuzzy C-Means (FCM) is used in the third step to classify inventories. At the end of the study, these nine different methodologies (“K-Means, Gaussian mixture, agglomerative clustering, spectral clustering” with and without the autoencoder and Fuzzy C-Means) are compared using two different datasets. It is shown that the proposed new hybrid method gives much better results than classical AI methods.

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A Novel Autoencoder-Integrated Clustering Methodology for Inventory Classification: A Real Case Study for White Goods Industry

Keskin,

Taskin

2024

Sustainability

View full text Add to dashboard Cite

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A survey on potentials, pathways and challenges of large language models in new-generation intelligent manufacturing

Cited by 1 publication

References 95 publications

A Novel Autoencoder-Integrated Clustering Methodology for Inventory Classification: A Real Case Study for White Goods Industry

A Novel Autoencoder-Integrated Clustering Methodology for Inventory Classification: A Real Case Study for White Goods Industry

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