Carbon efficiency evaluation:an analytical framework using fuzzy DEA

Data Envelopment Analysis (DEA) is a powerful analytical technique for measuring the relative efficiency of alternatives based on their inputs and outputs. The alternatives can be in the form of countries who attempt to enhance their productivity and environmental efficiencies concurrently. However, when desirable outputs such as productivity increases, undesirable outputs increase as well (e.g. carbon emissions), thus making the performance evaluation questionable. In addition, traditional environmental efficiency has been typically measured by crisp input and output (desirable and undesirable). However, the input and output data, such as CO2 emissions, in real-world evaluation problems are often imprecise or ambiguous. This paper proposes a DEA-based framework where the input and output data are characterized by symmetrical and asymmetrical fuzzy numbers. The proposed method allows the environmental evaluation to be assessed at different levels of certainty. The validity of the proposed model has been tested and its usefulness is illustrated using two numerical examples. An application of energy efficiency among 23 European Union (EU) member countries is further presented to show the applicability and efficacy of the proposed approach under asymmetric fuzzy numbers

A framework for measuring global Malmquist–Luenberger productivity index with CO2 emissions on Chinese manufacturing industries

China has achieved significant progress in terms of economic and social developments since implementation of reform and open policy in 1978. However, the rapid speed of economic growth in China has also resulted in high energy consumption and serious environmental problems, which hindering the sustainability of China's economic growth. This paper provides a framework for measuring eco-efficiency with CO2 emissions in Chinese manufacturing industries. We introduce a global Malmquist-Luenberger productivity index (GMLPI) that can handle undesirable factors within Data Envelopment Analysis (DEA). This study suggested after regulations imposed by the Chinese government, in the last stage of the analysis, i.e. during 2011–2012, the contemporaneous frontier shifts towards the global technology frontier in the direction of more desirable outputs and less undesirable outputs, i.e. producing less CO2 emissions, but the GMLPI drops slightly. This is an indication that the Chinese government needs to implement more policy regulations in order to maintain productivity index while reducing CO2 emissions

Direct mailing decisions based on the worst and best practice cross-efficiency evaluations

The problem argued in the literature of direct mailing decisions generally contains three parts: 1) forecasting customers\u27 future purchase/non-purchase responses; 2) evaluating the effectiveness of various strategies for increasing customers purchase responses; 3) prioritising the customers in terms of their values. A significant body of the literature has been dedicated to the first two components, and in particular, to purchase/non-purchase prediction modelling. However, in the current paper, we do not address these two components, but rather we focus on the third component. To this end, data envelopment analysis (DEA) technique and particularly cross-efficiency formulation of the best practice frontier Charnes, Cooper and Rhodes (CCR) (Charnes et al., 1978) (BPF-CCR) is used to determine those customers who should be put on the first priorities of marketing mailing list. In addition, the cross-efficiency formulation of worst practice frontier CCR (WPF-CCR) is developed to exclude the worst customers from mailing list and save the mailing expenses for the best practice ones. Using a numerical example, the application of the proposed model is demonstrated. Copyright 2013 Inderscience Enterprises Ltd

A new approach for considering a dual-role factor in data envelopment analysis

The conventional data envelopment analysis models deal with dualrole factor as non-discretionary (uncontrollable) factor. However, there might be dual-role factor which is under control of decision-maker. In addition, despite the fact that there are several publications addressing dual-role factors, it seems that their idea of classifying a factor as an input or an output within a single model has a limitation. They also do not consider non-zero input and output slacks and cannot fully measure the inefficiency of decision-making units. To resolve these limitations and to consider dual-role factor as well, this paper proposes a slacks-based measure model which does not consider dualrole factor as a non-discretionary factor. To compare the results of proposed approach with conventional model, a statistical analysis is run. A numerical example demonstrates the application of the proposed method. Copyright 2012 Inderscience Enterprises Ltd

Suppliers ranking in the presence of undesirable outputs

In conventional usage of Data Envelopment Analysis (DEA) for supplier selection, it is assumed that producing more outputs relative to fewer inputs is a criterion of efficiency. However, in the presence of undesirable outputs, suppliers with more good (desirable) outputs and less bad (undesirable) outputs relative to fewer inputs should be recognised as efficient. In addition, to get a complete ranking among suppliers and also eliminate unrealistic weighting schemes among them, this paper proposes a cross-efficiency formulation of DEA, which can treat undesirable outputs. A numerical example demonstrates the application of the proposed model in supplier ranking context. 2012 Inderscience Enterprises Ltd

A new model for ranking suppliers in the presence of both undesirable and non-discretionary outputs

Data envelopment analysis (DEA) can be used for supplier selection problem due to its multiple criteria nature. In suppliers\u27 evaluation, there might be some factors, which are beyond the control of their management, that are needed to be modelled in an appropriate way. Also, there are some situations in which some factors are undesirable and they are favourable to be decreased. The aim of this paper is to propose a model for evaluation of suppliers\u27 performance in the presence of both undesirable and non-discretionary outputs. This model can rank efficient suppliers by a super-efficiency DEA model. A numerical example has sought to demonstrate that the proposed model is actually applicable.Copyright 2014 Inderscience Enterprises Ltd

Biotyping of Staphylococcus aureus isolated from milk and milk products in Tabriz city

Knowledge about phenotypic features of Staphylococcus aureus isolated from milk and their products is very limited in Tabriz region. The aim of this study was to determine the biotypes of S. aureus. For this purpose, 48 S. aureus strains which were previously isolated from cow raw milk (24), traditional cheese (12) and ice cream (12) in Tabriz region were considered. Biotyping was carried out by means of Staphylokinase production, β-hemolysis, coagulation of cow plasma and crystal violates reaction. Among 48 isolates, 23 and 2 strains were belonged to the human and ovine ecovars, respectively. The rest of the isolates were identified as non-host specific ecovars. Regarding the high prevalence rate of human ecovars in this study, it seems that these ecovars may have been transmitted to these products via human handling

A data envelopment analysis model for selecting suppliers in the presence of both dual-role factors and non-discretionary inputs

Supplier selection is the strategy adopted by the manufacturer, to evaluate and select suppliers, which can fulfil the requirements of the manufacturer. To this end, data envelopment analysis (DEA), as a multiple criteria decision-making tool, has been applied for several times. However, conventional DEA models cannot simultaneously consider dual-role and non-discretionary factors. The objective of this paper is to propose a DEA model for ranking suppliers in the presence of both dual-role factors and non-discretionary inputs. A numerical example demonstrates the application of the proposed model. 2012 Inderscience Enterprises Ltd

Suppliers ranking by cross-efficiency evaluation in the presence of volume discount offers

The performance of each supply chain is significantly related to the performance of the suppliers. Due to the multiple criteria nature of the supplier selection problem, data envelopment analysis (DEA), as a multiple criteria decision-making tool, seems to be an appropriate method. This paper specifically focuses on the supplier selection problem when the suppliers offer volume discounts to encourage the purchase of large volumes. However, in all the papers which deal with the volume discount concept in DEA models, each decision-making unit (DMU) is free to decide which outputs and inputs to emphasise that in turn cause to have many efficient DMUs. Therefore, the main purpose of this study is to use the cross-efficiency method when suppliers offer volume discounts. A numerical example demonstrates the application of the proposed method. Copyright 2011 Inderscience Enterprises Ltd

Using DEA cross-efficiency evaluation for suppliers ranking in the presence of non-discretionary inputs

Supply chain includes all activities associated with the flow and transformation of goods from the raw material stage through to the end user. Supplier selection is one of the most important parts of supply chain management (SCM). For selecting suppliers, data envelopment analysis (DEA), as a multiple criteria decision making tool, has been applied for several times. However, sometimes in supplier selection problem, there may exist some criteria that are beyond the control of a management. These criteria are called non-discretionary or exogenously fixed factors. Since in traditional treatment of non-discretionary inputs in DEA, free reign is given when deciding for each decision making unit (DMU) which outputs and inputs to emphasise, many different avenues are present by which a DMU can appear efficient. Therefore, it is common to have many DMUs that are relatively efficient. In addition, since each DMU has its own set of weights, all of its weight might be put on a single output and input. As a result, the objective of this paper is to propose a cross-efficiency model which is able to consider non-discretionary inputs. A numerical example demonstrates the application of the proposed model in supplier selection context. Copyright 2013 Inderscience Enterprises Ltd