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

Keskin S., Taşkın A.

SUSTAINABILITY, cilt.16, sa.21, ss.1-36, 2024 (SCI-Expanded)

  • Yayın Türü: Makale / Tam Makale
  • Cilt numarası: 16 Sayı: 21
  • Basım Tarihi: 2024
  • Doi Numarası: 10.3390/su16219244
  • Dergi Adı: SUSTAINABILITY
  • Derginin Tarandığı İndeksler: Science Citation Index Expanded (SCI-EXPANDED), Social Sciences Citation Index (SSCI), Scopus, Aerospace Database, Agricultural & Environmental Science Database, CAB Abstracts, Communication Abstracts, Food Science & Technology Abstracts, Geobase, INSPEC, Metadex, Veterinary Science Database, Directory of Open Access Journals, Civil Engineering Abstracts
  • Sayfa Sayıları: ss.1-36
  • Yıldız Teknik Üniversitesi Adresli: Evet

Özet

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.