A Taxonomy and Review of the Network Data Envelopment Analysis Literature

Abstract

Performance measurement deals with ongoing monitoring and evaluation of the operations of the organizations so as to be able to improve their productivity and performance. Thus, the adoption of performance evaluation methods is necessary, which are capable of taking into account all the environmental factors of the organization, identifying the inefficient production processes and suggesting adequate ways to improve them. Such a method is Data Envelopment Analysis (DEA), which is the most popular non-parametric and data driven technique for assessing the efficiency of homogeneous decision making units (DMUs) that use multiple inputs to produce multiple outputs. The DMUs may consist of several sub-processes that interact and perform various operations. DEA has a wide application domain, such as public sector, banks, education, energy systems, transportation, supply chains, countries and so forth. However, the classical DEA models treat the DMU as a “black box”, i.e. a single stage production process that transforms some external inputs to final outputs. In such a setting, the internal structure of the DMU is not taken into consideration. Thus, the conventional DEA models fail to mathematically represent the internal characteristics of the DMUs, as well as they fall short to provide precise results and useful information regarding the sources that cause inefficiency. To consider for the internal structure of the DMUs, recent methodological advancements are developed, which extend the standard DEA and constitute a new field, namely the network DEA. The network DEA methods are capable of reflecting accurately the DMUs’ internal operations as well as to incorporate their relationships and interdependences. In network DEA, the DMU is considered as a network of interconnected sub-units, with the connections indicating the flow of intermediate products. In this chapter, we describe the underlying notions of network DEA methods and their advantages over the classical DEA ones. We also conduct a critical review of the state-of-the art methods in the field and we provide a thorough categorization of a great volume of network DEA literature in a unified manner. We unveil the relations and the differences of the existing network DEA methods. In addition, we report their limitations concerning the returns to scale, the inconsistency between the multiplier and the envelopment models as well as the inadequate information that provide for the calculation of efficient projections. The most important network DEA methods do not secure the uniqueness of the efficiency scores, i.e. the same level of overall efficiency is obtained from different combinations of the efficiencies of the sub-processes. Also, the additive efficiency decomposition method provides biased efficiency assessments. Finally, we discuss about the inability of the existing approaches to be universally applied on every type of network structure.