In a traditional single-stage Data Envelopment Analysis (DEA) structure, it is not known how the inputs are converted into outputs within the core of a DMU. The so-called “black box” analysis is assigned to such cases, to depict that a decision-making unit (DMU) is being treated as a whole unit that measures its relative performance by only considering its exogenous inputs and exogenous outputs. The internal operations of a DMU might consist of several interrelated and/or independent functions and tasks. Ignoring the internal operations of a DMU could lead to misleading outcomes. To enable the study of internal operations and identify any causes of inefficiency, research has extended DEA models to consider network structures.
While network structures have the potential to investigate the sources of inefficiency, two major challenges similar to those in a single-stage production system emerge. The first one concerns the lack of discrimination power. This problem often coincides with the existence of a high number of DEA-efficient DMUs. The second challenge is pertinent to what is referred to in the literature as an `unrealistic' weighting scheme. Indeed, it is allowed for a high relative-importance weight to be assigned to `less important' inputs or outputs, and/or a low weight to significant factors. This choice of weights could turn a DMU into a DEA-efficient unit.
The aforementioned challenges can impede the attainment of a fair and unique ranking. Fairness can be generally considered as a universal mechanism that corresponds to using a system of evaluation and ranking that is acceptable by the units being assessed. In the DEA context, fairer evaluation outcomes could be achieved by establishing effective rules and guidelines (e.g., higher degree of discriminatory power, more realistic weight scheme, level of cooperation between DMUs and divisions of a particular DMU) and/or implementing DEA methodologies (e.g., cross-efficiency, Nash bargaining game efficiency, super efficiency, multi-criteria DEA) that everyone can accept and account to fairness in a way everyone can find agreeable.
For this Special Issue, we welcome state-of-the-art contributions that span all areas of analytical and empirical research related to new developments and improvements in network DEA and how these reflect a fairer evaluation and ranking system. The goal is to ensure a wide representation of approaches and applications in areas such as sustainable supply chain management, manufacturing job shop, crop production, environmental efficiency, and climate change.
List of Topics:
- Data Envelopment Analysis (DEA)
- Network DEA
- Discrimination Power
- Unrealistic Weight Scheme