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AKILLI BAKIMDA YENİ BİR YAKLAŞIM: GERÇEK ZAMANLI YEDEK PARÇA TAHMİNLEME

Yıl 2021, Cilt: 7 Sayı: 2, 41 - 55, 29.12.2021

Öz

Satış sonrası müşteri hizmetleri, kaliteli yedek parça hizmetleri ve uygun fiyatlar ile hedef müşteri kitlesini memnun etmeye çalışmaktadır. Sıkça tekrarlayan müşteri ziyaretleri operasyonel satış maliyetlerinde önemli artışlara neden olurken, bu yanıltıcı politika uzun vadede kurumsal itibarı da zedeleyebilir. Akıllı bakım bağlamında gerçek zamanlı yedek parça tahminleme çözümü, C4.5, Apriori algoritmaları ve ağırlıklandırılmış k-en yakın komşuluk (kNN) uyarlamalarını birleştiren hibrit bir sınıflandırma modeli ile sorumlu teknik servise ilgili arıza için en olası yedek parçayı proaktif olarak önerir. Deneysel sonuçlara göre, önerilen yaklaşımın insan düzeyindeki yedek parça tahminleme performansını yaklaşık iki katına çıkardığı görülmektedir. En iyi model konfigürasyonuna göre televizyon ürün grubu için gerçek zamanlı yedek parça tahminleme çözümü SAP sisteminde canlı kullanıma alınmıştır.

Destekleyen Kurum

KOSGEB ArGe ve İnovasyon Destek Programı

Proje Numarası

3N2L8

Teşekkür

Gerçek Zamanlı Yedek Parça çözümü KOSGEB ArGe ve İnovasyon Destek Programı çerçevesinde 3N2L8 proje koduyla desteklenmektedir. Bu makale Ekim 2021 döneminde Marmara Üniversitesi'nde düzenlenen IMISC'21 (International Management Information Systems) konferansında sunulmuştur.

Kaynakça

  • Aguilera, J., Gonzalez, L. C., Montes-y-Gomez, M., ve Rosso, P. (2018). A new weighted k-nearest neighbor algorithm based on newton's gravitational force. In Iberoamerican Congress on Pattern Recognition, 305-313. doi:10.1007/978-3-030-13469-3_36.
  • Boriah, S., Chandola, V., ve Kumar, V. (2008). Similarity measures for categorical data: A comparative evaluation, In Proceedings of the 2008 SIAM international conference on data mining, 243-254. doi:10.1137/1.9781611972788.22.
  • Chapman, P., Clinton, J., Kerber, R., Khabaza, T., Reinartz, T., Shearer, C., Wirth, R. (1999). CRISP-DM 1.0 Step-by-Step Data Mining Guide.
  • Domingos, P. (2015). The master algorithm: How the quest for the ultimate learning machine will remake our world. Basic Books.
  • Dudani, S.A. (1978). The distance-weighted k-nearest-neighbor rule. IEEE trans. on systems, man and cybernetics, 8(4), 311-313.
  • Kantardzic, M. (2011). Data mining: concepts, models, methods, and algorithms. John Wiley & So Liu, C., Cao, L. ve Yu, P. S. (2014). A hybrid coupled k-nearest neighbor algorithm on imbalance data. International Joint Conference on Neural Networks (IJCNN), 2011-2018. doi:10.1109/IJCNN.2014.6889798.
  • Liu, W. ve Chawla, S. (2011). Class confidence weighted knn algorithms for imbalanced data sets, In Pacific-Asia conference on knowledge discovery and data mining, 345-356. doi:10.1007/978-3-642-20847-8_29.
  • Mateos-Garcia, D., Garcia-Gutierrez, J., ve Riquelme-Santos, J.C. (2016). An evolutionary voting for k-nearest neighbors. Expert Systems with Applications, 43, 9-14. doi:10.1016/j.eswa.2015.08.017.
  • Pang-Ning, T., Steinbach, M., ve Kumar, V. (2006). Introduction to data mining, Pearson Education India.
  • Parvinnia, E., Sabeti, M., Zolghadri Jahromi, M. ve Boostani, R. (2014). Classification of EEG Signals using adaptive weighted distance nearest neighbor algorithm. Journal of King Saud University-Computer and Information Sciences, 26(1), 1-6. doi:10.1016/j.jksuci.2013.01.001.
  • Song, Y., Huang, J., Zhou, D., Zha, H. ve Giles, C. L. (2007). IkNN: Informative k-nearest neighbor pattern classification, European Conference on Principles of Data Mining and Knowledge Discovery, 248-264. doi:10.1007/978-3-540-74976-9_25.
  • Tan, S. (2005). Neighbor-weighted k-nearest neighbor for unbalanced text corpus. Expert Systems with Applications, 28(4), 667-671. doi:10.1016/j.eswa.2004.12.023.
  • Wang, C., Cao, L., Wang, M., Li, J., Wei, W., ve Ou, Y. (2011). Coupled nominal similarity in unsupervised learning, Proceedings of the 20th ACM international conference on Information and knowledge management, 973-978. doi:10.1145/2063576.2063715.
  • Yuxuan, L. & Zhang, X. (2011). Improving k nearest neighbor with exemplar generalization for imbalanced classification, Pacific-Asia Conference on Knowledge Discovery and Data Mining, 321-332. doi:10.1007/978-3-642-20847-8_27.
  • Zhang, S., Cheng, D., Deng, Z., Zong, M., ve Deng, X. (2018). A novel kNN algorithm with data-driven k parameter computation. Pattern Recognition Letters, 109, 44-54. doi:10.1016/j.patrec.2017.09.036.
  • Zhu, Q., Feng, J., ve Huang, J. (2016). Natural neighbor: A selfadaptive neighborhood method without parameter K. Pattern Recognition Letters, 80, 30-36. doi:10.1016/j.patrec.2016.05.007.

A NEW APPROACH IN INTELLIGENT MAINTENANCE: SPARE PART PREDICTION ON REAL TIME BASIS

Yıl 2021, Cilt: 7 Sayı: 2, 41 - 55, 29.12.2021

Öz

After sales customer services attempt to satisfy target customers by qualified spare part services and affordable prices. While undesirable repetitive customer visits result in significant increase at sales operational costs, this misleading policy may deteriorate organizational goodwill at long run. In the context of intelligent maintenance, spare part prediction solution proactively proposes the most probable spare part to the responsible technical service by a hybrid classification model that combines C4.5, Apriori algorithms and weighted k-Nearest Neighbor (kNN) adaptations. The experimental results demonstrate that proposed approach approximately doubles the human-level performance at spare part prediction scenario. According to best runtime configuration analysis, a real time spare part prediction model has been deployed for television product group at the client’s SAP system.

Proje Numarası

3N2L8

Kaynakça

  • Aguilera, J., Gonzalez, L. C., Montes-y-Gomez, M., ve Rosso, P. (2018). A new weighted k-nearest neighbor algorithm based on newton's gravitational force. In Iberoamerican Congress on Pattern Recognition, 305-313. doi:10.1007/978-3-030-13469-3_36.
  • Boriah, S., Chandola, V., ve Kumar, V. (2008). Similarity measures for categorical data: A comparative evaluation, In Proceedings of the 2008 SIAM international conference on data mining, 243-254. doi:10.1137/1.9781611972788.22.
  • Chapman, P., Clinton, J., Kerber, R., Khabaza, T., Reinartz, T., Shearer, C., Wirth, R. (1999). CRISP-DM 1.0 Step-by-Step Data Mining Guide.
  • Domingos, P. (2015). The master algorithm: How the quest for the ultimate learning machine will remake our world. Basic Books.
  • Dudani, S.A. (1978). The distance-weighted k-nearest-neighbor rule. IEEE trans. on systems, man and cybernetics, 8(4), 311-313.
  • Kantardzic, M. (2011). Data mining: concepts, models, methods, and algorithms. John Wiley & So Liu, C., Cao, L. ve Yu, P. S. (2014). A hybrid coupled k-nearest neighbor algorithm on imbalance data. International Joint Conference on Neural Networks (IJCNN), 2011-2018. doi:10.1109/IJCNN.2014.6889798.
  • Liu, W. ve Chawla, S. (2011). Class confidence weighted knn algorithms for imbalanced data sets, In Pacific-Asia conference on knowledge discovery and data mining, 345-356. doi:10.1007/978-3-642-20847-8_29.
  • Mateos-Garcia, D., Garcia-Gutierrez, J., ve Riquelme-Santos, J.C. (2016). An evolutionary voting for k-nearest neighbors. Expert Systems with Applications, 43, 9-14. doi:10.1016/j.eswa.2015.08.017.
  • Pang-Ning, T., Steinbach, M., ve Kumar, V. (2006). Introduction to data mining, Pearson Education India.
  • Parvinnia, E., Sabeti, M., Zolghadri Jahromi, M. ve Boostani, R. (2014). Classification of EEG Signals using adaptive weighted distance nearest neighbor algorithm. Journal of King Saud University-Computer and Information Sciences, 26(1), 1-6. doi:10.1016/j.jksuci.2013.01.001.
  • Song, Y., Huang, J., Zhou, D., Zha, H. ve Giles, C. L. (2007). IkNN: Informative k-nearest neighbor pattern classification, European Conference on Principles of Data Mining and Knowledge Discovery, 248-264. doi:10.1007/978-3-540-74976-9_25.
  • Tan, S. (2005). Neighbor-weighted k-nearest neighbor for unbalanced text corpus. Expert Systems with Applications, 28(4), 667-671. doi:10.1016/j.eswa.2004.12.023.
  • Wang, C., Cao, L., Wang, M., Li, J., Wei, W., ve Ou, Y. (2011). Coupled nominal similarity in unsupervised learning, Proceedings of the 20th ACM international conference on Information and knowledge management, 973-978. doi:10.1145/2063576.2063715.
  • Yuxuan, L. & Zhang, X. (2011). Improving k nearest neighbor with exemplar generalization for imbalanced classification, Pacific-Asia Conference on Knowledge Discovery and Data Mining, 321-332. doi:10.1007/978-3-642-20847-8_27.
  • Zhang, S., Cheng, D., Deng, Z., Zong, M., ve Deng, X. (2018). A novel kNN algorithm with data-driven k parameter computation. Pattern Recognition Letters, 109, 44-54. doi:10.1016/j.patrec.2017.09.036.
  • Zhu, Q., Feng, J., ve Huang, J. (2016). Natural neighbor: A selfadaptive neighborhood method without parameter K. Pattern Recognition Letters, 80, 30-36. doi:10.1016/j.patrec.2016.05.007.
Toplam 16 adet kaynakça vardır.

Ayrıntılar

Birincil Dil Türkçe
Bölüm Makaleler
Yazarlar

Eren Esgin

Volkan Özay Bu kişi benim

Görkem Özkan Bu kişi benim

Proje Numarası 3N2L8
Erken Görünüm Tarihi 29 Aralık 2021
Yayımlanma Tarihi 29 Aralık 2021
Yayımlandığı Sayı Yıl 2021 Cilt: 7 Sayı: 2

Kaynak Göster

APA Esgin, E., Özay, V., & Özkan, G. (2021). AKILLI BAKIMDA YENİ BİR YAKLAŞIM: GERÇEK ZAMANLI YEDEK PARÇA TAHMİNLEME. Yönetim Bilişim Sistemleri Dergisi, 7(2), 41-55.