Cherchye et al. (2013b) introduced a DEA methodology that is specially tailored for multi-output efficiency measurement. The methodology accounts for jointly used inputs and incorporates information on how inputs are allocated to outputs. In this paper, we present extensions that render the methodology useful to deal with undesirable (or “bad”) outputs in addition to desirable (or “good”) outpu...

This study estimates the environmental efficiency of international listed firms in 10 worldwide sectors from 2007 to 2013 by applying an order-m method, a non-parametric approach based on free disposal hull with subsampling bootstrapping. Using a conventional output of gross profit and two conventional inputs of labor and capital, this study examines the order-m environmental efficiency account...

The measure proposed in this paper is a new nonparametric data envelopment analysis (DEA) scheme, the hybrid measure, for determining efficiency in the presence of radial and nonradial inputs or outputs. Further extension of the scheme occurred to address nonseparable desirable and undesirable outputs. Applying the model to measure the overall efficiency of U.S. electric utilities in the presen...

Data envelopment analysis (DEA) is a methodology for measuring the relative efficiencies of a set of decision-making units (DMUs) that use multiple inputs to produce multiple outputs. The standard DEA models assume that all inputs and outputs are crisp and can be changed at the discretion of management. While crisp input and output data are fundamentally indispensable in the standard DEA evalua...

Efficiency reflects a scenario of higher quality product with fewer resource inputs and less pollution discharge. In terms of a manufacturing firm, it means a win-win strategy in both economic and environmental categories. The paper extends the Electric Arc Furnace (EAF) activity management by taking pollution discharge as undesirable outputs. After reviewing relative undesirable outputs DEA li...

We evaluate, by means of mathematical programming formulations, the relative technical and scale efficiencies of decision making units (DMUs) when some of the inputs or outputs are exogenously fixed and beyond the discretionary control of DMU managers. This approach further develops the work on efficiency evaluation and on estimation of efficient production frontiers known as data envelopment a...

In public discussion on environmental policy, the notion of eco-efficiency is often used. The joint production of goods and undesirable outputs, such as pollutants which may not be freely disposable without costs, cause difficulties to measure the overall performance of the firm. Because of the absence of market prices for the undesirable outputs, we are not able to estimate the environmental c...

The changing economic conditions have challenged many organizations to search for more effective performance measurement methods. Data envelopment analysis (DEA) is a widely used mathematical programming approach for comparing the inputs and outputs of a set of homogeneous decision making units (DMUs) by evaluating their relative efficiency. Performance measurement in the conventional DEA is ba...

Supply chain management is essential for the success of firms and customer satisfaction. In addition, a suitable performance evaluation system is vital to the effective management of the supply chain. Due to the importance of cost management in the supply chain, an approach based on Data Envelopment Analysis (DEA) is proposed in the current study for evaluating the cost efficiency of closed-loo...

The calculation of RTS amounts to measuring a relationship between inputs and outputs in a production structure. There are many methods to measure RTS in the primal space or the dual space. One of the main approaches is using the multiplier on the convexity constraint. But returns to scale measurements in DEA models are affected by the presence of regulatory constraints. These additional constr...