AKILLI BAKIMDA YENİ BİR YAKLAŞIM: GERÇEK ZAMANLI YEDEK PARÇA TAHMİNLEME

Year 2021, Volume: 7 Issue: 2, 41 - 55, 29.12.2021

Eren Esgin Volkan Özay Görkem Özkan

Abstract

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.

Keywords

Sınıflandırma, Akıllı Bakım, SAP, Gerçek Zamanlı Yedek Parça Tahmini, Ağırlıklandırılmış k-En Yakın Komşuluk

Supporting Institution

KOSGEB ArGe ve İnovasyon Destek Programı

Project Number

3N2L8

Thanks

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.

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

Abstract

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.

Keywords

Classification, Intelligent Maintenance, SAP, Spare Part Prediction on Real Time Basis, Weighted k-Nearest Neighbor

Project Number

3N2L8

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