Bütünleşik AHP Ve K-Ortalama Kümeleme Tabanlı Yedek Parça Talep Tahmini: Sağlık Ekipmanları Alanında Bir Uygulama

Abstract

Bu çalışmada, uluslararası pazarda manyetik rezonans cihazlarının yedek parça taleplerini daha doğru tahmin edebilmek için bütünleşik bir yöntem geliştirilmiştir. Önerilen yöntem üç temel bileşenden oluşmaktadır: En önemli kriterin belirlenmesi için çok kriterli karar verme, benzer talep özelliklerine göre ülkelerin gruplandırılması için kümeleme ve her bir küme için talep tahmini yapmak amacıyla regresyon teknikleri kullanılmıştır. İlk olarak, Analitik Hiyerarşi Süreci ile yedek parça taleplerini etkileyen kriterlerin göreli ağırlıkları hesaplanmış ve en önemli kriter seçilmiştir. Daha sonra, K-ortalama kümeleme algoritması kullanılarak ülkeler bu kritere göre benzer taleplere sahip gruplara ayrılmıştır. Her bir küme için özelleştirilmiş regresyon modelleri oluşturularak talep tahminleri gerçekleştirilmiştir. Önerilen yaklaşım, özellikle yerel üretim tesisi bulunmayan ülkelerde yedek parça tedarik süreçlerinin iyileştirilmesini ve talep tahmin doğruluğunun artırılmasını sağlamaktadır. Çalışmanın bulguları, küme bazlı talep tahmini yaklaşımının stok yönetimi ve tedarik zinciri süreçlerinde verimliliği artırabileceğini göstermektedir. Sonuç olarak, geliştirilen model, sağlık sektöründe yüksek maliyetli manyetik rezonans cihazlarının bakım ve onarım süreçlerini iyileştirmekte ve yedek parça tedarik zincirinin daha etkin yönetilmesini sağlamaktadır.

Keywords

• Yedek parça talep tahmini
• Analitik hiyerarşi süreci
• K-ortalama kümeleme

Integrated Use of AHP and K-means Clustering Methods for Spare Part Demand Forecasting: An Application in Healthcare Equipment

Abstract

In this study, an integrated approach has been developed to improve the accuracy of spare parts demand forecasting for magnetic resonance imaging devices in the international market. The proposed method consists of three main components: multi-criteria decision-making to determine the most critical criterion, clustering to group countries based on similar demand characteristics, and regression techniques to forecast demand for each cluster. First, the Analytic Hierarchy Process is employed to calculate the relative weights of the criteria influencing spare parts demand, and the most significant criterion is selected. Then, the K-means clustering algorithm is applied to categorize countries into groups based on this criterion. Customized regression models are developed for each cluster to enhance demand forecasting accuracy. The proposed approach improves spare parts supply processes, particularly in countries without local manufacturing facilities, and enhances forecast accuracy. The findings indicate that a cluster-based demand forecasting approach can increase efficiency in inventory management and supply chain operations. In conclusion, the developed model contributes significantly to optimizing the maintenance and repair processes of high-cost magnetic resonance imaging devices in the healthcare sector and improving the management of the spare parts supply chain.

Keywords

• Spare part demand forecasting
• Analytic hierarchy process
• K-means clustering

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