Industrial clusters are geographic concentrations of interconnected firms, specialized suppliers, service providers, and associated institutions in a particular field that are present in a nation or region. Industrial clusters arise because they increase the productivity with which firms can compete. In the recent years, industrial clustering have received considerable attention from economists and industrial analysts, since industrial clustering is seen as the main reason for economic growth, national competitiveness, and success of certain economic region. However, few literatures discussed industrial clustering from the perspectives on logistics industry. The purpose of this study is to propose a quantitative analysis method for determining geographic patterns, characteristics, and industrial correlations of logistics industrial clustering. This study attempts to develop fuzzy clustering models to analyze logistics industrial clustering.
This study develops a three-phase model to analyze logistics industrial clustering. In the first phase of this study, sub-industries of logistics industry were classified by using fuzzy clustering model. Five sub-industries were classified by considering logistics service attributes, namely, the second-party carrier, warehousing, and the third-party carrier, warehousing, and intermediary. In the second phase of this study, the industrial correlations between logistics sub-industries and other industries were discussed by using the fuzzy clustering approach in input-output analysis. In the third phase, the geographic patterns and competitiveness of logistics clusters were determined and evaluated by using fuzzy clustering. Three major logistics clusters in Taiwan are determined, namely, northern compound logistics industrial cluster, central carrier and 3PL logistics industrial cluster, and southern compound logistics industrial cluster. The competitiveness and logistics performance of northern logistics industrial cluster were evaluated better than those of central and southern logistics industrial clusters.
The results of this study provide an overall picture of logistics industrial clustering in Taiwan. The proposed modeling provides ways by which government and firms can evaluate logistics industrial clustering development and positioning. In addition, it is envisaged that the results of this study may shed light on quantitative methods of industrial clustering and logistics industrial clustering issues.