Clustering analysis prioritization of automobile logistics services

Abstract

Purpose - The purpose of this paper is to provide a clustering approach to segment supply chain partners in the automobile industry and prioritize services offered by third party logistics service (3PL) providers. Design/methodology/approach - In total, 98 automobile and auto-parts manufacturers are surveyed to identify service needs, preferences, and outsourcing commitments. By applying a two-stage clustering approach combined with Ward's minimum-variance method and the K-means algorithm, the logistics companies prioritize their services to better satisfy groups of customers with specific preferences. Findings - Four distinctive groups of manufacturers are identified using the two-stage clustering approach. The clusters separate logistic preferences and outsourcing patterns of after market parts suppliers, original equipment service parts suppliers, original equipment manufacturer parts suppliers, and tier one car makers. The paper finds that distribution and delivery services hold the highest percentage of services outsourced among the manufacturers. Originality/value - This paper models logistic services as customizable services and develops a data system methodology to define the profiles of automobile manufacturers and their preferred logistic services. Through the analysis of service preferences and clustering, the paper identifies the key logistic services that can be customized for members of the automobile supply chain. A case is provided which demonstrates how a logistics company can provide customized service designs for specific target markets and customers.