Performance measurement of sustainable third party reverse logistics provider by data envelopment analysis: a case study of an Indian apparel manufacturing group

2015 ◽  
Vol 1 (3) ◽  
pp. 273 ◽  
Author(s):  
V.G. Venkatesh ◽  
Sonali Bhattacharya ◽  
Madhvi Sethi ◽  
Sahil Dua
Keyword(s):  
Data Envelopment Analysis ◽  
Performance Measurement ◽  
Reverse Logistics ◽  
Third Party ◽  
Data Envelopment ◽  
Apparel Manufacturing

A New Look at Selecting Third-Party Reverse Logistics Providers

2012 ◽  
pp. 246-254
Author(s):  
Reza Farzipoor Saen
Keyword(s):  
Decision Making ◽  
Data Envelopment Analysis ◽  
Reverse Logistics ◽  
Third Party ◽  
Data Envelopment ◽  
Weight Restrictions ◽  
Use Of Data ◽  
Available Information ◽  
Nondiscretionary Factors ◽  
Decision Making Criteria

The use of Data Envelopment Analysis (DEA) in many fields is based on total flexibility of the weights. However, the problem of allowing total flexibility of the weights is that the values of the weights obtained by solving the unrestricted DEA program are often in contradiction to prior views or additional available information. Also, many applications of DEA assume complete discretionary of decision making criteria. However, they do not assume the conditions that some factors are nondiscretionary. To select the most efficient third-party reverse logistics (3PL) provider in the conditions that both weight restrictions and nondiscretionary factors are present, a methodology is introduced. A numerical example demonstrates the application of the proposed method.

A New Look at Selecting Third-Party Reverse Logistics Providers

2010 ◽  
Vol 3 (1) ◽  
pp. 58-67 ◽  
Author(s):  
Reza Farzipoor Saen
Keyword(s):  
Decision Making ◽  
Data Envelopment Analysis ◽  
Reverse Logistics ◽  
Third Party ◽  
Data Envelopment ◽  
Weight Restrictions ◽  
Numerical Example ◽  
Use Of Data ◽  
Available Information ◽  
Nondiscretionary Factors

The use of Data Envelopment Analysis (DEA) in many fields is based on total flexibility of the weights. However, the problem of allowing total flexibility of the weights is that the values of the weights obtained by solving the unrestricted DEA program are often in contradiction to prior views or additional available information. Also, many applications of DEA assume complete discretionary of decision making criteria. However, they do not assume the conditions that some factors are nondiscretionary. To select the most efficient third-party reverse logistics (3PL) provider in the conditions that both weight restrictions and nondiscretionary factors are present, a methodology is introduced. A numerical example demonstrates the application of the proposed method.

A DECISION MODEL FOR SELECTING THIRD-PARTY REVERSE LOGISTICS PROVIDERS IN THE PRESENCE OF BOTH DUAL-ROLE FACTORS AND IMPRECISE DATA

2011 ◽  
Vol 28 (02) ◽  
pp. 239-254 ◽  
Author(s):  
REZA FARZIPOOR SAEN
Keyword(s):  
Data Envelopment Analysis ◽  
Reverse Logistics ◽  
Decision Model ◽  
Dual Role ◽  
Third Party ◽  
Data Envelopment ◽  
Imprecise Data ◽  
Numerical Example ◽  
Proposed Model

This paper introduces a model for dealing with selecting third-party reverse logistics (3PL) providers in the presence of both dual-role factors and imprecise data. The proposed model is based on data envelopment analysis (DEA). A numerical example demonstrates the application of the proposed method.

Measuring Performance of a Three-Stage Network Structure Using Data Envelopment Analysis and Nash Bargaining Game: A Supply Chain Application

2018 ◽  
Vol 17 (05) ◽  
pp. 1429-1467 ◽  
Author(s):  
Mohammad Amirkhan ◽  
Hosein Didehkhani ◽  
Kaveh Khalili-Damghani ◽  
Ashkan Hafezalkotob
Keyword(s):  
Supply Chain ◽  
Data Envelopment Analysis ◽  
Nash Bargaining ◽  
Data Envelopment ◽  
Second Stage ◽  
Proposed Model ◽  
Nash Bargaining Game ◽  
Dea Models ◽  
Overall Efficiency

The issue of efficiency analysis of network and multi-stage systems, as one of the most interesting fields in data envelopment analysis (DEA), has attracted much attention in recent years. A pure serial three-stage (PSTS) process is a specific kind of network in which all the outputs of the first stage are used as the only inputs in the second stage and in addition, all the outputs of the second stage are applied as the only inputs in the third stage. In this paper, a new three-stage DEA model is developed using the concept of three-player Nash bargaining game for PSTS processes. In this model, all of the stages cooperate together to improve the overall efficiency of main decision-making unit (DMU). In contrast to the centralized DEA models, the proposed model of this study provides a unique and fair decomposition of the overall efficiency among all three stages and eliminates probable confusion of centralized models for decomposing the overall efficiency score. Some theoretical aspects of proposed model, including convexity and compactness of feasible region, are discussed. Since the proposed bargaining model is a nonlinear mathematical programming, a heuristic linearization approach is also provided. A numerical example and a real-life case study in supply chain are provided to check the efficacy and applicability of the proposed model. The results of proposed model on both numerical example and real case study are compared with those of existing centralized DEA models in the literature. The comparison reveals the efficacy and suitability of proposed model while the pitfalls of centralized DEA model are also resolved. A comprehensive sensitivity analysis is also conducted on the breakdown point associated with each stage.

Exploring the water–energy–food nexus from a perspective of agricultural production efficiency using a three-stage data envelopment analysis modelling evaluation method: a case study of the middle and lower reaches of the Yangtze River, China

Water Policy ◽  
2018 ◽  
Vol 21 (1) ◽  
pp. 49-72 ◽  
Author(s):  
Jiazhong Zheng ◽  
Weiguang Wang ◽  
Dan Chen ◽  
Xinchun Cao ◽  
Wanqiu Xing ◽  
...  
Keyword(s):  
Data Envelopment Analysis ◽  
Agricultural Production ◽  
Yangtze River ◽  
Production Efficiency ◽  
Evaluation Method ◽  
Data Envelopment ◽  
The Yangtze River ◽  
Agricultural Resources ◽  
Important Challenge

Abstract A coordinated nexus of agricultural resources is vital to achieve food security and sustainable development in China. Comprehensively considering the water–energy–food nexus as well as the external environment, this study adopts a three-stage data envelopment analysis (DEA) modelling evaluation method to assess the agricultural production efficiency (APE) of seven provinces in the middle and lower reaches of the Yangtze River (MLYR) during 1996–2015. The results show that the three-stage DEA modelling evaluation method reveals real APE and is considered to be a better quantitative method than conventional approaches. A gradually widening range of APE is an important challenge for this region. Significantly, this region generates huge demands for agricultural resources. Moreover, regional emissions of greenhouse gases (GHG) decreased from 34.20 million tons standard coal in 1996 to 32.11 million tons standard coal in 2015, though APE has continued to decrease by 2.56% in the past two decades. In general, the management and technology levels should be improved simultaneously, even though specific opportunities for APE improvement vary across provinces in MLYR. However, understanding the temporal and spatial variation of APE along with the WEF nexus from a production-based insight is a vital step toward appropriately targeted policy making for nationwide resources savings and emissions reduction.

scholarly journals Measuring Efficiency of Knowledge Production in Health Research Centers Using Data Envelopment Analysis (DEA): A Case Study in Iran

10.19082/3266 ◽  
2016 ◽  
Vol 8 (11) ◽  
pp. 3266-3271
Author(s):  
Mohammad Meskarpour Amiri ◽  
Taha Nasiri ◽  
Seyed Hassan Saadat ◽  
Hosein Amini Anabad ◽  
Payman Mahboobi Ardakan
Keyword(s):  
Data Envelopment Analysis ◽  
Knowledge Production ◽  
Health Research ◽  
Data Envelopment ◽  
Research Centers ◽  
Measuring Efficiency ◽  
Using Data
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