Sensitivity Analysis for Identifying the Critical Productivity Factors of Container Terminals

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BO, Lu ;KYU, Park Nam.
Sensitivity Analysis for Identifying the Critical Productivity Factors of Container Terminals. 
Strojniški vestnik - Journal of Mechanical Engineering, [S.l.], v. 59, n.9, p. 536-546, june 2018. 
ISSN 0039-2480.
Available at: <https://www.sv-jme.eu/article/sensitivity-analysis-for-identifying-the-critical-productivity-factors-of-container-terminals/>. Date accessed: 29 nov. 2021. 
doi:http://dx.doi.org/10.5545/sv-jme.2012.931.
Bo, L., & Kyu, P.
(2013).
Sensitivity Analysis for Identifying the Critical Productivity Factors of Container Terminals.
Strojniški vestnik - Journal of Mechanical Engineering, 59(9), 536-546.
doi:http://dx.doi.org/10.5545/sv-jme.2012.931
@article{sv-jmesv-jme.2012.931,
	author = {Lu  Bo and Park Nam Kyu},
	title = {Sensitivity Analysis for Identifying the Critical Productivity Factors of Container Terminals},
	journal = {Strojniški vestnik - Journal of Mechanical Engineering},
	volume = {59},
	number = {9},
	year = {2013},
	keywords = {Data envelopment analysis, Regression analysis, Sensitivity analysis, Container terminal productivity},
	abstract = {With the rapid expansion of international trade, a distinctive feature of the contemporary container terminal industry is that competition has become more intense. If container terminal managers can gain a proper appreciation of their various productivity factors, they may be able to identify which factors have a more positive influence on productivity. The core of sensitivity analysis for evaluating terminal productivity is to remove input variables one by one, then re-estimate the correlation between productivity and investment. From this perspective, sensitivity analysis provides a more appropriate benchmark for identifying which factors are more critical for productivity improvement. This analysis has been variously studied utilizing either Data Envelopment Analysis or Regression Analysis. Given the strengths associated with these two analyses, this paper applies both approaches to the same set of data for 28 major East Asian container terminals and compares the results of the efficiency. The results of this study can provide a useful reference to port managers for developing improvement strategies.},
	issn = {0039-2480},	pages = {536-546},	doi = {10.5545/sv-jme.2012.931},
	url = {https://www.sv-jme.eu/article/sensitivity-analysis-for-identifying-the-critical-productivity-factors-of-container-terminals/}
}
Bo, L.,Kyu, P.
2013 June 59. Sensitivity Analysis for Identifying the Critical Productivity Factors of Container Terminals. Strojniški vestnik - Journal of Mechanical Engineering. [Online] 59:9
%A Bo, Lu 
%A Kyu, Park Nam
%D 2013
%T Sensitivity Analysis for Identifying the Critical Productivity Factors of Container Terminals
%B 2013
%9 Data envelopment analysis, Regression analysis, Sensitivity analysis, Container terminal productivity
%! Sensitivity Analysis for Identifying the Critical Productivity Factors of Container Terminals
%K Data envelopment analysis, Regression analysis, Sensitivity analysis, Container terminal productivity
%X With the rapid expansion of international trade, a distinctive feature of the contemporary container terminal industry is that competition has become more intense. If container terminal managers can gain a proper appreciation of their various productivity factors, they may be able to identify which factors have a more positive influence on productivity. The core of sensitivity analysis for evaluating terminal productivity is to remove input variables one by one, then re-estimate the correlation between productivity and investment. From this perspective, sensitivity analysis provides a more appropriate benchmark for identifying which factors are more critical for productivity improvement. This analysis has been variously studied utilizing either Data Envelopment Analysis or Regression Analysis. Given the strengths associated with these two analyses, this paper applies both approaches to the same set of data for 28 major East Asian container terminals and compares the results of the efficiency. The results of this study can provide a useful reference to port managers for developing improvement strategies.
%U https://www.sv-jme.eu/article/sensitivity-analysis-for-identifying-the-critical-productivity-factors-of-container-terminals/
%0 Journal Article
%R 10.5545/sv-jme.2012.931
%& 536
%P 11
%J Strojniški vestnik - Journal of Mechanical Engineering
%V 59
%N 9
%@ 0039-2480
%8 2018-06-28
%7 2018-06-28
Bo, Lu, & Park Nam Kyu.
"Sensitivity Analysis for Identifying the Critical Productivity Factors of Container Terminals." Strojniški vestnik - Journal of Mechanical Engineering [Online], 59.9 (2013): 536-546. Web.  29 Nov. 2021
TY  - JOUR
AU  - Bo, Lu 
AU  - Kyu, Park Nam
PY  - 2013
TI  - Sensitivity Analysis for Identifying the Critical Productivity Factors of Container Terminals
JF  - Strojniški vestnik - Journal of Mechanical Engineering
DO  - 10.5545/sv-jme.2012.931
KW  - Data envelopment analysis, Regression analysis, Sensitivity analysis, Container terminal productivity
N2  - With the rapid expansion of international trade, a distinctive feature of the contemporary container terminal industry is that competition has become more intense. If container terminal managers can gain a proper appreciation of their various productivity factors, they may be able to identify which factors have a more positive influence on productivity. The core of sensitivity analysis for evaluating terminal productivity is to remove input variables one by one, then re-estimate the correlation between productivity and investment. From this perspective, sensitivity analysis provides a more appropriate benchmark for identifying which factors are more critical for productivity improvement. This analysis has been variously studied utilizing either Data Envelopment Analysis or Regression Analysis. Given the strengths associated with these two analyses, this paper applies both approaches to the same set of data for 28 major East Asian container terminals and compares the results of the efficiency. The results of this study can provide a useful reference to port managers for developing improvement strategies.
UR  - https://www.sv-jme.eu/article/sensitivity-analysis-for-identifying-the-critical-productivity-factors-of-container-terminals/
@article{{sv-jme}{sv-jme.2012.931},
	author = {Bo, L., Kyu, P.},
	title = {Sensitivity Analysis for Identifying the Critical Productivity Factors of Container Terminals},
	journal = {Strojniški vestnik - Journal of Mechanical Engineering},
	volume = {59},
	number = {9},
	year = {2013},
	doi = {10.5545/sv-jme.2012.931},
	url = {https://www.sv-jme.eu/article/sensitivity-analysis-for-identifying-the-critical-productivity-factors-of-container-terminals/}
}
TY  - JOUR
AU  - Bo, Lu 
AU  - Kyu, Park Nam
PY  - 2018/06/28
TI  - Sensitivity Analysis for Identifying the Critical Productivity Factors of Container Terminals
JF  - Strojniški vestnik - Journal of Mechanical Engineering; Vol 59, No 9 (2013): Strojniški vestnik - Journal of Mechanical Engineering
DO  - 10.5545/sv-jme.2012.931
KW  - Data envelopment analysis, Regression analysis, Sensitivity analysis, Container terminal productivity
N2  - With the rapid expansion of international trade, a distinctive feature of the contemporary container terminal industry is that competition has become more intense. If container terminal managers can gain a proper appreciation of their various productivity factors, they may be able to identify which factors have a more positive influence on productivity. The core of sensitivity analysis for evaluating terminal productivity is to remove input variables one by one, then re-estimate the correlation between productivity and investment. From this perspective, sensitivity analysis provides a more appropriate benchmark for identifying which factors are more critical for productivity improvement. This analysis has been variously studied utilizing either Data Envelopment Analysis or Regression Analysis. Given the strengths associated with these two analyses, this paper applies both approaches to the same set of data for 28 major East Asian container terminals and compares the results of the efficiency. The results of this study can provide a useful reference to port managers for developing improvement strategies.
UR  - https://www.sv-jme.eu/article/sensitivity-analysis-for-identifying-the-critical-productivity-factors-of-container-terminals/
Bo, Lu, AND Kyu, Park.
"Sensitivity Analysis for Identifying the Critical Productivity Factors of Container Terminals" Strojniški vestnik - Journal of Mechanical Engineering [Online], Volume 59 Number 9 (28 June 2018)

Authors

Affiliations

  • Dalian University, Institute of Electronic Commerce and Modern Logistics, China 1
  • Tongmyong University, School of Port and Logistics, South Korea 2

Paper's information

Strojniški vestnik - Journal of Mechanical Engineering 59(2013)9, 536-546

https://doi.org/10.5545/sv-jme.2012.931

With the rapid expansion of international trade, a distinctive feature of the contemporary container terminal industry is that competition has become more intense. If container terminal managers can gain a proper appreciation of their various productivity factors, they may be able to identify which factors have a more positive influence on productivity. The core of sensitivity analysis for evaluating terminal productivity is to remove input variables one by one, then re-estimate the correlation between productivity and investment. From this perspective, sensitivity analysis provides a more appropriate benchmark for identifying which factors are more critical for productivity improvement. This analysis has been variously studied utilizing either Data Envelopment Analysis or Regression Analysis. Given the strengths associated with these two analyses, this paper applies both approaches to the same set of data for 28 major East Asian container terminals and compares the results of the efficiency. The results of this study can provide a useful reference to port managers for developing improvement strategies.

Data envelopment analysis, Regression analysis, Sensitivity analysis, Container terminal productivity