Using the optimization algorithm to evaluate and predict the business performance of logistics companies–a case study in Vietnam

Tien-Muoi Le, Chia-Nan Wang and Han-Khanh Nguyen

Applied Economics, 2020, vol. 52, issue 38, 4196-4212

Abstract: In the current market economy, it is important to evaluate and forecast the situation and business performance of enterprises to provide the necessary information for managers to plan for future use of resources. This study aims to evaluate and predict the business performance of logistics companies in Vietnam. The authors use the optimal algorithm in the data envelopment analysis (DEA) to evaluate the business efficiency of the companies in the years 2014–2017. In addition, the authors use Grey system theory to forecast business results and their future use during the period of 2018–2021. The research shows that Gemadept Corporation and Sea & Air Freight International use the their business resources effectively and as expected, and that these companies will continue to thrive in the future. This study provides a method to measure, evaluate, and forecast the business performance of the logistics companies. Managers and the government can rely on this approach for implementation and overall planning of logistics enterprises in the future.

Date: 2020
References: Add references at CitEc
Citations: View citations in EconPapers (2)

Downloads: (external link)
http://hdl.handle.net/10.1080/00036846.2020.1733474 (text/html)
Access to full text is restricted to subscribers.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:taf:applec:v:52:y:2020:i:38:p:4196-4212

Ordering information: This journal article can be ordered from
http://www.tandfonline.com/pricing/journal/RAEC20

DOI: 10.1080/00036846.2020.1733474

Access Statistics for this article

Applied Economics is currently edited by Anita Phillips

More articles in Applied Economics from Taylor & Francis Journals
Bibliographic data for series maintained by Chris Longhurst ().