Identifying an Efficiency Productivity Model for Faculties with DEA Benchmarking Technique


  • Sasarose Jaijit Kasetsart University
  • Punpiti Piamsa-nga Kasetsart University
  • Nalina Phisanbut Kasetsart University
  • Juta Pichitlamken Kasetsart University



University management, target setting, benchmarking, decision analysis, performance evaluation, cluster analysis, data envelopment analysis


Purpose: Many universities in Thailand aim to raise their ranking; thus, university administrators set the target outputs for their institutions. However, the university-level targets may differ from those at the faculty levels because the inherent nature of academic fields and faculty capacity to produce outputs may not be considered. Therefore, we propose a model to determine possible target values for faculty outputs with weak efficiency by benchmarking the university under study with one of the leading universities in Thailand. As a result, the evidence-based target values allow inefficient faculties to know what outputs they need to improve under the assumption that "if each faculty improves their productivity to reach a target value, the university can rank higher." This can lead to a more realistic and achievable target instead of a single target across all faculties.

Study design/methodology/approach: Due to inherent differences among faculties, they are clustered by subject areas with the hierarchical cluster analysis to reduce bias. Then an efficiency score of each faculty is computed via the Data Envelopment Analysis.

Findings: The faculties of the university under study are clustered into three subject areas: 1) agricultural science and technology management, 2) engineering and ecology, and 3) social sciences and humanities. The DEA technique provides the slack values to be used in target settings that mitigate the bias from different capabilities on producing outputs across subject areas. For the faculties in agricultural science and technology management, social sciences, and humanities, the inadequacy of performed research and teaching operations are essential indicators, i.e., the percentage of the sum of slack values in both aspects is more than 80%. In engineering and ecology, the essential indicators (i.e., the percentage of the sum of slack values in both aspects is 91.10%) are related to teaching and international outlook operations. However, the teaching operation is the most critical aspect (i.e., the maximum value of the percentage sum of each subject area's slack values is 42.23%) that all subject areas should be focused on for improvement.

Originality/value: Our approach can provide a quantitative decision support tool that allows university administrators to set realistic operational policies according to evidence-based target values tailored for each subject area.

Author Biographies

Punpiti Piamsa-nga , Kasetsart University

PUNPITI PIAMSA-NGA received the B.Eng. and M.Eng. in electrical engineering from Kasetsart University, Bangkok, Thailand, in 1989 and 1992, respectively, and D.Sc. degree in computer engineering from the George Washington University, Washington DC, USA in 1999.

Since 1993, he has been a member of the Department of computer engineering, Kasetsart University, where he is currently an associate professor and the head of the department. He is also a deputy director for research information at Kasetsart University Research and Development Institute. His research interests include data analytics, multimedia, and pattern recognition for agricultural, biological, and environmental data; and design and implementation of computer science curricula.  He is also the head of the Multimedia Analysis and Discovery Research Laboratory at Kasetsart University.

Dr.Piamsa-nga was a recipient of the 2006 Thailand Excellent Research award from the National Research Council of Thailand.

Nalina Phisanbut, Kasetsart University

NALINA PHISANBUT received the B.Sc. with First Class Honours in mathematics and Ph.D. in computer science from the University of Bath, Bath, UK, in 2002 and 2011, respectively.

She was a researcher at the National Institute of Informatics (NII), Tokyo, Japan from 2007 to 2008, and the University of Kent, Kent, UK from 2013 to 2014, and is currently a computer scientist at the Multimedia Analysis and Discovery Research Laboratory at Kasetsart University, Bangkok, Thailand. She has research experiences in computer algebra for linear boundary problems and data mining for bibliographic data. Her current research interest includes data analytics and machine learning for agricultural, biological, and environmental data; and performance analysis of academic and research activities.

Juta Pichitlamken, Kasetsart University

JUTA PICHITLAMKEN received the B.S. in chemical engineering from Cornell University, New York, U.S.A., M.S. in chemical engineering from University of Washington, Washington, U.S.A., and M.S. and Ph.D. in industrial engineering from Northwestern University, Illinois, U.S.A. She is an Associate Professor at Department of Industrial Engineering, Kasetsart University, Bangkok, Thailand. Her research interests are in simulation, stochastic modeling and decision analysis. She can be reached at