淡江大學機構典藏:Item 987654321/74344
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    Title: 應用模糊集群法於臺灣物流產業群聚分析
    Other Titles: Applying fuzzy clustering approach to analyze logistics industrial clustering in Taiwan
    Authors: 蔡明穎;Tsai, Ming-Ying
    Contributors: 淡江大學運輸管理學系碩士班
    溫裕弘;Wen, Yuh-Horng
    Keywords: 產業群聚;物流產業;模糊集群法;Industrial Clustering;Logistics Industrial Clustering;Fuzzy Clustering
    Date: 2011
    Issue Date: 2011-12-28 18:23:47 (UTC+8)
    Abstract: 產業群聚已成為國家推動產業發展與提高競爭力之重要策略,產業群聚研究亦為近幾年受矚目之研究議題。產業群聚多藉地理位置的鄰近,分享產生的規模經濟與範疇經濟效果、及共享資訊價值,節省許多有形、無形之交易成本等優勢。然過去產業群聚研究較著重於探討已發展之產業群聚案例,探討群聚發展與關鍵因素、分析群聚競爭力等,對於產業形成群聚之類型、群聚分群之量化模式研究較少。本研究嘗試應用模糊集群法建構物流產業群聚分析模式,旨在於提出一套分析與衡量物流產業群聚分布及物流產業關聯分析之量化分析方法,並針對物流產業聚落進行競爭力評估,探討物流產業聚落之競爭力型態。
    本研究第一階段建構物流產業之子產業分類模糊集群模式,依據物流業服務屬性進行子產業定義分群,得其國內物流產業類別現分為五個類別:運輸2PL業、倉儲2PL業、運輸3PL業、倉儲3PL業、中間商3PL業。第二階段則針對各個物流子產業對應其他各級產業之產業關聯分析建構模糊集群模式,以了解物流產業之產業關聯特性,獲得兩大類群,「以二級產業為服務對象之物流類群」與「以三級產業為服務對象之物流類群」,並得其以二級產業為服務對象之物流類群為其他各級產業之基礎服務產業,然以三級產業為服務對象之物流類群偏向附加價值產業。第三階段針對各個物流子產業,依據地理區位建構模糊集群模式,得出運輸2PL業聚落、運輸3PL業聚落、以及中間商3PL業聚落分為三個聚落-北部聚落、中部聚落、以及南部聚落,而倉儲2PL業聚落、倉儲3PL業聚落分為兩個聚落-北部聚落與南部聚落。進一步,彙整國內物流聚落得其北部複合物流聚落、中部運輸與第三方物流聚落、南部複合物流聚落,再針對聚落進行競爭力評比,藉由AHP法成對評比矩陣,計算得出加權評估值,獲得北部聚落為高物流效能、高競爭優勢聚落,其次為南部聚落,次之為中部聚落。
    本研究建構一系列物流產業群聚分析模式,建議政府機關與物流廠商亦可以本研究模式為基礎,了解國內物流產業之發展、產業之定位、競爭屬性與型態,以作為廠商考慮區位與聚落決策之參考,並提供國內物流產業規劃、布局作為策略之參考基礎。
    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.
    Appears in Collections:[Graduate Institute & Department of Transportation Management] Thesis

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