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Please use this identifier to cite or link to this item: http://ntour.ntou.edu.tw:8080/ir/handle/987654321/47903

Title: 網絡資料包絡分析法應用於散裝航運公司之績效評估
The Performance Evaluation of Bulk Shipping Corporations Applying by Network Data Envelopment Analysis
Authors: Hsu, Ying-Chen
Contributors: NTOU:Department of Shipping and Transportation Management
Keywords: 網絡資料包絡分析法;績效評估;集中式法;交叉效率;平衡計分卡;散裝航運公司
Network Data Envelopment Analysis;Performance Evaluation;Centralized Approach;Cross-efficiency;Balanced Scorecard;Bulk Shipping Corporation
Date: 2015
Issue Date: 2018-08-08T03:52:46Z
Abstract: 航運產業係國家經濟發展之關鍵,對臺灣而言尤為進出口貿易之命脈。然全球航運產業於2008年底遭逢金融海嘯,面臨嚴重之存亡威脅,於此嚴峻之經營環境下,航運公司必須以更有效率的方式創造營運商機。本文主要目的為填補過去研究之不足,應用網絡資料包絡分析法(Network Data Envelopment Analysis, NDEA)構建績效評估模式,並輔以平衡計分卡(Balanced Scorecard, BSC)四大構面(即學習與成長、內部商業流程、顧客以及財務)以強化其實用性,藉以釐清應改善之方向以提升受評單位之績效。本文之模式乃根據集中式概念,將平衡計分卡四大構面視為相互連接的四個階段,藉此可同時衡量各受評單位之整體效率與各構面之個別效率,不僅有助於對受評單位進行更深入之分析,更提供一個結合網絡資料包絡分析法與平衡計分卡優點之研究方法,受評單位經由此評估過程得檢視其營運問題並確認改善之方向。 本文採用兩岸三地散裝航運公司之數據為例進行模式之演示,提出並比較各受評公司於自我評估與同儕評估下之排序與績效差異,並據以提出可行之績效改善策略。研究結果發現,於兩岸三地航運公司中,臺灣散裝航運公司普遍具有較佳表現,其中整體效率最佳者為T-TL3。以各構面而言,T-TL2、T-TL6、C-TL8與C-TL11於「學習與成長構面」有效率,T-TL5於「內部商業流程構面」有效率,C-TL8與H-TL14於「顧客構面」具有效率,T-TL5與T-TL7於「財務構面」有效率。本文將網絡資料包絡分析模式,輔以平衡計分卡構面,應用於完整之航運公司營運數據,期望研究結果可協助航運公司加強資源配置與改善營運策略,針對可提升績效之方向持續努力與增加投資。
The shipping industry is essential for the economic development of nations like Taiwan as a means delivering and receiving cargo. Shipping has been depressed since 2008 as a result of the financial crisis increasing pressure for the shipping corporations to operate more efficiently. This paper aims to contribute to the existing literature by proposing a Network Data Envelopment Analysis (NDEA) model consolidated with Balanced Scorecard (BSC) to identify and understand paths to improve DMU’s performance. The proposed model treats the four perspectives of BSC (learning and growth, internal business processes, customer, and financial) as four interconnected stages on the basis of the centralized concept and calculates overall efficiency of each DMU as well as the individual efficiency of BSC each stage. This paper advances the analysis of the DMU, suggests an approach that benefits from NDEA and BSC, and further provides examples of potential insights into specific operations where modification can improve DMU’s performance. For demonstrating the proposed model, this paper applies it to a limited sample of bulk shipping corporations among Taiwan, China and Hong Kong, presents and compares the ranking and the differences in performances between peer-evaluation and self-evaluation of each company, and suggests available strategies for performance improvement. The results show that the bulk shipping corporations in Taiwan perform well among all DMUs, especially for T-TL3 with the highest overall efficiency. Moreover, T-TL2, T-TL6, C-TL8 and C-TL11 are efficient in learning and growth perspective; T-TL5 is efficient in internal business processes perspective; C-TL8 and H-TL14 are efficient in customer perspective; T-TL5 and T-TL7 are efficient in financial perspective. This paper suggests the way the proposed combined use of NDEA and BSC applied to a complete set of operating data has the potential to assist shipping corporations improve resource allocation and operational strategies, focus efforts and investments on areas that have potential to generate improved performance.
URI: http://ethesys.lib.ntou.edu.tw/cgi-bin/gs32/gsweb.cgi?o=dstdcdr&s=G0020073005.id
Appears in Collections:[航運管理學系] 博碩士論文

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