Akademik Makine Öğrenmesi humanist_oglak profil fotoğrafı Sedanur Durğun 02.11.2026 3 görüntülenme, PDF 2 kez açıldı, 0 kez indirildi, 0 yorum

Lojistik Sektöründe Müşteri Davranışlarının Makine Öğrenmesi ile Segmentasyonu

makine öğrenmesi matematiksel modelleme rfm analizi tedarik zinciri yönetimi veri madenciliği

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Özet. Bu çalışmanın amacı, tedarik zinciri yönetiminde denetimsiz makine öğrenmesi tekniklerini kullanarak lojistik performans odaklı, dinamik bir müşteri segmentasyonu modeli geliştirmektir. Projede öncelikle, pazarlama analitiğinin temelini oluşturan geleneksel RFM (Recency, Frequency, Monetary) modelinin teorik altyapısı incelenmiş; bu modele lojistik operasyon süreçlerinin bir göstergesi olan "Shipping Delay" (Sevkiyat Gecikmesi) değişkeni eklenerek model genişletilmiştir. Veri madenciliği süreçleri kapsamında, büyük veri setleri üzerinde aykırı değer analizi (IQR), veri normalizasyonu ve boyut indirgeme (PCA) teknikleri sistematik bir matematiksel yaklaşımla uygulanmıştır. Kümeleme aşamasında K-Means algoritması kullanılarak müşteriler karakteristik özelliklerine göre ayrıştırılmış; optimum küme sayısı "Dirsek Yöntemi" ile belirlenmiştir. Geliştirilen modelin başarısı; Silhouette Skoru, Davies-Bouldin Endeksi ve Calinski-Harabasz Endeksi gibi validasyon metrikleriyle matematiksel olarak doğrulanmıştır. Teorik çerçeve, DataCo Supply Chain veri seti üzerinden yapılan uygulama ile desteklenmiş; algoritmanın çok boyutlu verileri anlamlandırmadaki etkinliği görselleştirme teknikleriyle sunulmuştur. Elde edilen sonuçlar, kümeleme analizinin ve istatistiksel modellemenin, karmaşık tedarik zinciri problemlerini çözmede, müşteri sadakatini ölçmede ve operasyonel verimliliği artırmada ne kadar güçlü bir araç olduğunu ortaya koymaktadır.

Abstract. The objective of the study is to develop a dynamic customer segmentation model focused on logistics performance by utilizing unsupervised machine learning techniques in supply chain management. In the initial phase of the study, the theoretical framework of the traditional RFM (Recency, Frequency, Monetary) model, which forms the basis of marketing analytics, was examined. This model was subsequently extended by integrating the "Shipping Delay" variable as a key indicator of operational logistics processes. Within the scope of data mining procedures, outlier analysis (IQR), data normalization, and dimensionality reduction (PCA) techniques were applied through a systematic mathematical approach. During the clustering stage, customers were segmented according to their characteristic behaviors using the K-Means algorithm, where the optimal number of clusters was determined via the "Elbow Method." The performance and reliability of the developed model were mathematically validated using metrics such as the Silhouette Score, Davies-Bouldin Index, and Calinski-Harabasz Index. The theoretical framework was supported by an empirical application using the DataCo Supply Chain dataset, and the effectiveness of the algorithm in interpreting high-dimensional data was demonstrated through advanced visualization techniques. The results reveal that clustering analysis and statistical modeling are powerful instruments for addressing complex supply chain challenges, measuring customer loyalty, and enhancing operational efficiency.

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