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Undesirable inputs and outputs in DEA models - ScienceDirect
Source: sciencedirect.com
Article summary:
1. The paper presents an approach to treat both undesirable inputs and outputs simultaneously in non-radial DEA models.
2. The technology sets are estimated by two linear programming problems which are called Additive models with constant and variable returns to scale, respectively.
3. The proposed model is feasible and bounded and tested on a hypothetical data.
Article analysis:
The article “Undesirable Inputs and Outputs in DEA Models” provides an overview of the use of Data Envelopment Analysis (DEA) models for assessing the efficiency of Decision Making Units (DMUs). The article is written in a clear and concise manner, making it easy to understand the concepts presented. However, there are some potential biases that should be noted when evaluating the trustworthiness and reliability of this article.
First, the article does not provide any evidence or sources to support its claims about the effectiveness of DEA models for assessing DMU efficiency. Additionally, there is no discussion of possible risks associated with using these models or any counterarguments that could be made against them. Furthermore, the article does not present both sides equally; instead, it focuses solely on the benefits of using DEA models without exploring any potential drawbacks or limitations.
In addition, there are some missing points of consideration that should have been addressed in this article. For example, there is no mention of how different types of inputs or outputs may affect the accuracy or reliability of these models. Additionally, there is no discussion about how different types of data can influence the results obtained from these models or how they can be used in different contexts. Finally, there is no exploration into how these models can be used to improve decision making processes within organizations or how they can be applied to real-world scenarios.
In conclusion, while this article provides a good overview of DEA models and their potential uses for assessing DMU efficiency, it lacks sufficient evidence to support its claims and fails to explore important points such as potential risks associated with using these models or how they can be applied in different contexts. As such, readers should take caution when evaluating this article’s trustworthiness and reliability before drawing any conclusions from its content.