Analysis of Rear Differential Component Clustering in Transmission Systems Using Hierarchical Cluster Analysis with and without Procurement Strategy Matrix Variables

Abstract

The automotive industry has faced significant challenges due to the large number of Tier 2 suppliers for Rear Differential components, with 17 suppliers providing 32 different parts. This situation has resulted in increased production costs and more complex supply chain management. This study aimed to analyze the clustering of Rear Differential components in transmission systems using Hierarchical Cluster Analysis, with the goal of supporting cost reduction in the automotive industry. Two clustering models were compared: Model 1, which excluded procurement strategy matrix variables (Special Requirements, Raw Material Grade, Raw Material Type, Manufacturing Process, Tier 2 Supplier Information, and Company Location), and Model 2, which incorporated an additional variable related to the Procurement Strategy Matrix. The decision criteria for determining the optimal number of clusters were based on four key factors: 1) Product design, 2) Characteristics, 3) Materials, and 4) Manufacturing. The clustering results for both models revealed the same optimal number of 13 clusters; however, the similarity matrix between the clusters differed. Furthermore, the number of members within each cluster varied. Based on the criteria for determining the optimal number of clusters, Model 2, which included the Procurement Strategy Matrix variable, demonstrated superior clustering efficiency compared to Model 1. Ultimately, this research identified 13 optimal clusters, reducing the number of Tier 2 suppliers from 17 to 13, representing a 23.53% reduction.