Assessment of port efficiency using stepwise selection and window analysis in data envelopment analysis

Seaports play an important role in the national economy, with $900+ billion worth of goods moving in and out of the USA in containers. Growing shipping capacity and severe congestion in US ports raise concerns on their state of efficiency. Assessment of relative efficiency of container ports can provide insights into the productivity and performance of different ports. This study applies the appropriate data envelopment analysis (DEA) models to assess the changes in efficiency for 15 US container ports. The input and output variables in the DEA models are chosen using stepwise selection, a first-time application in the port industry. Their relationships are then examined using the available records since the year 2000. Comparisons among different ports over time are carried out using 4-year window analysis. The results are instrumental to decision-makers, providing a way to link performance measurements to operational planning activities and identifying investment areas within ports that can affect income and trade.

• Record URL:
  https://doi.org/10.1057/s41278-020-00155-6
• Availability:
  • Find a library where document is available. Order URL: http://worldcat.org/issn/14792931
• Supplemental Notes:
  • Copyright © 2020 Macmillan Publishers Ltd.
• Authors:
  • Seth, Sonal
  • Feng, Qianmei
• Publication Date: 2019-9

Language

• English

Media Info

• Media Type: Web
• Features: Appendices; References;
• Pagination: pp 536-561
• Serial:
  • Maritime Economics & Logistics
  • Volume: 22
  • Issue Number: 4
  • Publisher: Palgrave Macmillan
  • ISSN: 1479-2931
  • EISSN: 1479-294X
  • Serial URL: https://link.springer.com/journal/41278

Subject/Index Terms

• TRT Terms: Container terminals; Data analysis; Economic efficiency; Port operations; Time windows
• Geographic Terms: United States
• Subject Areas: Marine Transportation; Operations and Traffic Management; Terminals and Facilities;

Filing Info

• Accession Number: 01761103
• Record Type: Publication
• Files: TRIS
• Created Date: Nov 25 2020 3:16PM