Network DEA and Its Applications (2017–2022): A Systematic Literature Review

Abstract

Data Envelopment Analysis (DEA) is one of the fastest growing approaches to solving management problems for the multi-criteria evaluation of the efficiency of homogeneous production systems. The general trend in recent years has been the development of network DEA (NDEA) models, which can consider the complicated structure of Decision Making Units (DMUs) and, therefore, can be more informative from the point of view of management science than traditional DEA models. The aim of this study is the systematization and clarification of general trends in the development of NDEA applications over the past 6 years (2017–2022). This study uses the methodology of a systematic literature review, which includes the analysis of the dynamics of the development of the topic, the selection of the main clusters of publications according to formal (citation, branches of knowledge, individual researchers) and informal (topics) criteria, and the analysis of their content. This review reveals that, most frequently, network structures are used for bank models, supply chain models, models of eco-efficiency of complex production systems, models of innovation processes, and models of universities or their departments and healthcare systems. Two-stage models, where the outputs of the first stage are the inputs of the second (intermediate outputs), are the most commonly used. However, in recent years, there has been a noticeable tendency to complicate DEA models and introduce hierarchical structures into them.

Malmquist productivity index for two-stage network systems under data uncertainty: A real-world case study

“…Overall, this research contributes to the advancement of productivity measurement methods and offers a comprehensive and effective framework for analyzing the performance of two-stage network decision-making units under data ambiguity. Future research can build upon this study by expanding the application of the Malmquist productivity index to other sectors or industries with similar characteristics of two-stage network systems [ 69 – 71 ]. Furthermore, the Malmquist productivity index can be extended to address special data scenarios, including those involving negative data [ 72 – 74 ].…”

Network activity analysis for arbitrary production structures and technologies: Generic approach with application to network DEA

“…Comprehensive compilations of the then-state of the art of network DEA were already presented by a collection of 22 articles in a handbook edited by Cook and Zhu (2014) and by a monography of Kao (2017). Chen et al (2013), Kao (2014), and Ratner et al (2023) present reviews of network DEA literature in general. 2 A review of Alves and Meza (2023) focuses specifically on models with slacksbased efficiency measures, already resulting in 97 publications (until 2021), starting with Tone and Tsutsui (2009) and progressively increasing in last years, including mostly applications in banking, energy, environment, and transportation.…”