Efficiency Analysis of Large Global Manufacturing Companies by Data Envelopment Analysis Approach(Pages 176-183)
Thi Kim Lien Nguyen1,2* and Hong Huyen Le2
1Scientific Research-International Cooperation, Thanh Dong University, Hai Duong, Hai Duong Province 171960.
2Department of Economics-Business Administration, Thanh Dong University, Hai Duong, Hai Duong Province 171960, Vietnam.
The development process of the manufacturing industry is a foundation for establishing many large enterprises around the world. The purpose of this study measures the performance of eight large manufacturing companies from past to future by a data envelopment analysis (DEA) approach. First, the super-SBM model was used to calculate the efficiency score in the previous term. Second, the resampling model with Lucas and weights applies to compute the forecasting values based on the historical data from 2016 to 2020; notably, this model can calculate the efficiency score in the future period of 2021–2025, based on integrating super-efficiency. The empirical results of the past, current, and estimated scores reveal that Toyota, Apple, Samsung, Honda, and Cardinal always obtain the performance above one number. Whereas Cardinal is the best manufacturing company with a consistently high score based on the efficiency qualification in the whole term, Ford is the worst manufacturing company as its efficiency score under one number. Finding results figure out an overall picture of the operational process of large manufacturing companies. The analysis result suggests a direction for improving the inefficient cases in future terms.
Manufacturing company; data envelopment analysis; super-SBM model; resampling model; efficiency.
C81- Methodology for Collecting, Estimating, and Organizing Microeconomic Data; G17 - Financial Forecasting and Simulation; L60- General.
How to Cite:
Thi Kim Lien Nguyen and Hong Huyen Le. Efficiency Analysis of Large Global Manufacturing Companies by Data Envelopment Analysis Approach. [ref]: vol.19.2021. available at: https://refpress.org/ref-vol19-a17/
Licensee REF Press This is an open access article licensed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses/by-nc/3.0/) which permits unrestricted, non-commercial use, distribution and reproduction in any medium, provided the work is properly cited.