Supplier Selection by the Pair of Nondiscretionary Factors-Imprecise Data Envelopment Analysis Models

Discretionary models for evaluating the efficiency of suppliers assume that all criteria are discretionary, that is, controlled by the management of each supplier and varied at its discretion. These models do not assume supplier selection in the conditions that some factors are nondiscretionary. The objective of this paper is to propose a new pair of nondiscretionary factors-imprecise data envelopment analysis (NF-IDEA) models for selecting the best suppliers in the presence of nondiscretionary factors and imprecise data. A numerical example demonstrates the application of the proposed method.Full Tex

A new Russell model for selecting suppliers

Recently, supply chain management (SCM) has been considered by many researchers. Supplier evaluation and selection plays a significant role in establishing an effective SCM. One of the techniques that can be used for selecting suppliers is data envelopment analysis (DEA). In some situations, to select suitable suppliers, purchasing managers deal with undesirable outputs, dual-role factors and imprecise data. The objective of this paper is to propose a new Russell model for selecting the best suppliers in the presence of undesirable outputs, dual-role factors and imprecise data. A case study demonstrates the application of the proposed method. Copyright © 2014 Inderscience Enterprises Ltd

A novel network data envelopment analysis model for evaluating green supply chain management

Green supply chain management (GSCM) has become a method to improve environmental performance. Under stakeholder pressures, forces and regulations, companies need to improve the GSCM practice, which are effected by practices such as green purchasing, green design, product recovery, and collaboration with patrons and suppliers. As companies promote the GSCM, their economic performance and environmental performance will be enhanced. Hence, GSCM evaluation is very important for any company. One of the techniques that can be used for evaluating GSCM is data envelopment analysis (DEA). Traditional models of data envelopment analysis (DEA) are based upon thinking about production as a "black box". One of the drawbacks of these models is to omit linking activities. The objective of this paper is to propose a novel network DEA model for evaluating the GSCM in the presence of dual-role factors, undesirable outputs, and fuzzy data. A case study demonstrates the application of the proposed model. A case study demonstrates the applicability of the proposed model. © 2013 Elsevier B.V

A new hybrid decision making system for supplier selection

The objective of this paper is to develop a hybrid decision making system using Data Envelopment Analysis (DEA) and linguistic fuzzy models for selecting the best supplier in the presence of multiple decision makers. In this hybrid system, first the weights of selected criteria are obtained from each of the decision makers as linguistic fuzzy numbers within the framework of group decision making. Then, to select the best supplier, absolute weight restriction (AWR) model is incorporated into the DEA model. A real case study demonstrates the application of the model

Assessing green performance of power plants by multiple hybrid returns to scale technologies

Efficiency measurement is a key and strategic factor in improving an organization’s performance and increasing their competitive advantage. Nevertheless, measuring efficiency in settings with multicomponent production technologies is a major issue with the existing approaches in the literature. The main contribution of the current paper is to develop a novel nonparametric approach to evaluate efficiency and obviate some of the theoretical barriers in multi-output settings. To this end, for the first time, new technologies assuming multiple hybrid returns-to-scale (MHRTS) with output-specific inputs, joint inputs, and outputs are developed. The new technologies are based on some of the axiomatic principles in data envelopment analysis (DEA) for forming a new production possibility set (PPS) to measure the efficiency of decision-making units (DMUs). By implementing the MHRTS technologies with output-specific inputs, joint inputs, and outputs, the proposed models can deal with undesirable outputs. Compared with the existing technologies in the DEA literature, the new technologies not only can incorporate output-specific inputs, joint inputs, and outputs for the performance evaluation of DMUs but also obviate existing theoretical barriers in the MHRTS technology. The applicability and usefulness of the proposed method are validated using a case study in the energy sector