A new fuzzy network data envelopment analysis model for measuring the performance of agility in supply chains

2013 ◽  
Vol 69 (1-4) ◽  
pp. 291-318 ◽  
Author(s):  
Kaveh Khalili-Damghani ◽  
Madjid Tavana
Author(s):  
Hossein Hajaji ◽  
Sara Yousefi ◽  
Reza Farzipoor Saen ◽  
Amir Hassanzadeh

Nowadays, forward-thinking companies move beyond conventional structures of organizations and consider all parties of the supply chain. The objective of this paper is to present an adaptive network data envelopment analysis (DEA) model to evaluate overall and divisional efficiency of sustainable supply chains in the presence of desirable and undesirable outputs. Our adaptive network DEA model can assess overall and divisional efficiency of supply chains given managerial and natural disposability. Also, it suggests new investment opportunity given congestion type. A case study is presented.


2021 ◽  
Vol 2021 ◽  
pp. 1-17
Author(s):  
Alireza Khosravi ◽  
Mohammad Fallah ◽  
Seyyed Esmaeil Najafi

One of the new concepts that have found a considerable position in many countries of the world is organizing EFQM organizational excellence models. Different organizations and institutions have been evaluated and compared on its basis, and the move towards improvement and promotion is strengthened in them due to creation of competitive space. The EFQM organizational excellence model cannot remove the managers’ and users’ need for the levels of quantitative goals’ operation solely. Thus, requirement for a tool which considers quantitative goals and present environment was felt, and in this manner, various assessment processes were created to be used in different organizations; one of the most important ones is the technique for Data Envelopment Analysis. Evaluating organization efficiency based on the EFQM model is one of the strategic managerial tools in many organizations. The classic DEA models were designed to work with deterministic data and cannot deal with uncertainties in their inputs. The techniques developed so far for fuzzy performance evaluation are also very limited. Given that the inputs and outputs of a real system are not always definite and accurate and that some data can only be expressed in vague verbal and subjective terms, the use of fuzzy sets in modeling is inevitable (Ali et al., 2019). In this paper, a Network Data Envelopment Analysis Model is proposed in fuzzy conditions for assessing units of an organization based on an organizational excellence model. The suggestive model utilizes the privileges of both Fuzzy Network Data Envelopment Analysis and EFQM organizational excellence models simultaneously in order to assess organization’s efficiency. The Fuzzy Network Data Envelopment Analysis model is able to calculate the whole organization’s efficiency as well as organization’s efficiency separately for various phases of the organizational excellence model. Another privilege of the suggested model is that it utilizes fuzzy theory and concepts for modeling and observance of existing noncertainties in the experts’ views while assessing organization’s excellence criteria. The EFQM-fuzzy network DEA model is applied for assessing a holding’s organizational units within the discipline of “project management.”


DYNA ◽  
2018 ◽  
Vol 85 (204) ◽  
pp. 83-90 ◽  
Author(s):  
Lidia Angulo Meza ◽  
João Carlos Soares de Mello ◽  
Silvio Figueiredo Gomes Junior ◽  
Plácido Moreno

A pesar de que los modelos estándar del Análisis Envolvente de Datos (DEA) han sido ampliamente utilizados en la evaluación de la eficiencia en educación, existen pocos estudios que utilizan modelos DEA en red (Network DEA – NDEA) en la evaluación educativa. En el presente trabajo, se ha propuesto una alternativa a la evaluación oficial realizada a cada tres años por la CAPES (agencia brasileña para la regulación de los programas de post-graduación) mediante un modelo DEA en red. El uso de NDEA se justifica ya que dependiendo del punto de vista algunas variables pueden ser consideradas como entradas o como salidas. El uso de NDEA evita la necesidad de decidir si una variable es una entrada o una salida de todo el proceso. Esto ocurre porque una variable puede ser tanto una salida para una etapa y una entrada para otro. Nuestro modelo relacional NDEA evalúa tanto la productividad como la calidad junto con la eficiencia global, a partir de datos bibliométricos.


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