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

Author

Listed:
  • Sena Keskin

    (Department of Industrial Engineering, Yildiz Technical University, Besiktas, Istanbul 34349, Turkey)

  • Alev Taskin

    (Department of Industrial Engineering, Yildiz Technical University, Besiktas, Istanbul 34349, Turkey
    Industrial Data Analytics and Decision Support Systems Center, Azerbaijan State University of Economics (UNEC), Istiqlaliyyat Str. 6, Baku 1001, Azerbaijan)

Abstract

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.

Suggested Citation

  • Sena Keskin & Alev Taskin, 2024. "A Novel Autoencoder-Integrated Clustering Methodology for Inventory Classification: A Real Case Study for White Goods Industry," Sustainability, MDPI, vol. 16(21), pages 1-36, October.
  • Handle: RePEc:gam:jsusta:v:16:y:2024:i:21:p:9244-:d:1505880
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    References listed on IDEAS

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    1. Baumgärtner, Stefan & Quaas, Martin, 2010. "What is sustainability economics?," Ecological Economics, Elsevier, vol. 69(3), pages 445-450, January.
    2. Ester Guijarro & Eugenia Babiloni & Manuel Cardós, 2022. "On the estimation of the fill rate for the continuous (s, S) inventory system for the lost sales context," PLOS ONE, Public Library of Science, vol. 17(2), pages 1-13, February.
    3. Tsai, Chi-Yang & Yeh, Szu-Wei, 2008. "A multiple objective particle swarm optimization approach for inventory classification," International Journal of Production Economics, Elsevier, vol. 114(2), pages 656-666, August.
    4. Krongthong Heebkhoksung, 2024. "A New Paradigm for Sustainable Supply Chain Management for Business Operation," Sustainability, MDPI, vol. 16(14), pages 1-16, July.
    5. Millstein, Mitchell A. & Yang, Liu & Li, Haitao, 2014. "Optimizing ABC inventory grouping decisions," International Journal of Production Economics, Elsevier, vol. 148(C), pages 71-80.
    6. Wang, Kejun & Qi, Xiaoxia & Liu, Hongda & Song, Jiakang, 2018. "Deep belief network based k-means cluster approach for short-term wind power forecasting," Energy, Elsevier, vol. 165(PA), pages 840-852.
    7. Lolli, F. & Ishizaka, A. & Gamberini, R., 2014. "New AHP-based approaches for multi-criteria inventory classification," International Journal of Production Economics, Elsevier, vol. 156(C), pages 62-74.
    8. Li, Kehua & Ma, Zhenjun & Robinson, Duane & Ma, Jun, 2018. "Identification of typical building daily electricity usage profiles using Gaussian mixture model-based clustering and hierarchical clustering," Applied Energy, Elsevier, vol. 231(C), pages 331-342.
    9. Fatih Yiğit & Şakir Esnaf, 2021. "A new Fuzzy C-Means and AHP-based three-phased approach for multiple criteria ABC inventory classification," Journal of Intelligent Manufacturing, Springer, vol. 32(6), pages 1517-1528, August.
    10. Zamani Dadaneh, Dariush & Moradi, Sajad & Alizadeh, Behrooz, 2023. "Simultaneous planning of purchase orders, production, and inventory management under demand uncertainty," International Journal of Production Economics, Elsevier, vol. 265(C).
    11. Wang, Xincheng & Gong, Tianyu, 2024. "Digital-enabled supply chain innovation and CO2 emissions: The contingent role of first-tier supplier's structural holes," Technological Forecasting and Social Change, Elsevier, vol. 201(C).
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