46 research outputs found

    Modelling generalized firms' restructuring using inverse DEA

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    The key consideration for firms’ restructuring is improving their operational efficiencies. Market conditions often offer opportunities or generate threats that can be handled by restructuring scenarios through consolidation, to create synergy, or through split, to create reverse synergy. A generalized restructuring refers to a move in a business market where a homogeneous set of firms, a set of pre-restructuring decision making units (DMUs), proceed with a restructuring to produce a new set of post-restructuring entities in the same market to realize efficiency targets. This paper aims to develop a novel inverse Data Envelopment Analysis based methodology, called GInvDEA (Generalized Inverse DEA), for modeling the generalized restructuring. Moreover, the paper suggests a linear programming model that allows determining the lowest performance levels, measured by efficiency that can be achieved through a given generalized restructuring. An application in banking operations illustrates the theory developed in the paper

    New data envelopment analysis models for assessing sustainability Part 2: A static data envelopment analysis approach

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    Sustainability implies business resilience over time through robust economic, social and environmental systems. Sustainable business practices lead to the creation of economic value, healthy ecosystems and strong communities. Sustainable business practices are fostered through engagement with stakeholders, effective environmental management systems and good governance, all underpinned by effective measurement and evaluation (Shabanpour, Yousefi, & Farzipoor Saen, 2017). Data envelopment analysis (DEA) is a technique, which helps decision makers to assess the efficiency of decision making units (DMUs) (Ahmady, Azadi, Sadeghi, & Farzipoor Saen, 2013; Charnes, Cooper, & Rhodes, 1978). Recently, new DEA models have been developed to assess sustainability issues. However, DEA research into sustainability is sparse and there is a need for greater focus on this important topic. In this special issue, ground‐breaking research into new DEA models to assess sustainability of DMUs is presented. Such research will assist decision makers to manage organizations with a focus on sustainability. The special issue also provides an opportunity for practitioners to develop and increase understanding of DEA models in sustainability evaluation. For maximum utility, authors should not only develop new DEA models but also show efficacy of real‐world applications. The responses to the call for new DEA models and applications are presented in the following five accepted articles published in Part 2 of this special edition. In the next section, we summarize the main contributions of these articles

    Eco-efficiency measurement and material balance principle:an application in power plants Malmquist Luenberger Index

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    Incorporating Material Balance Principle (MBP) in industrial and agricultural performance measurement systems with pollutant factors has been on the rise in recent years. Many conventional methods of performance measurement have proven incompatible with the material flow conditions. This study will address the issue of eco-efficiency measurement adjusted for pollution, taking into account materials flow conditions and the MBP requirements, in order to provide ‘real’ measures of performance that can serve as guides when making policies. We develop a new approach by integrating slacks-based measure to enhance the Malmquist Luenberger Index by a material balance condition that reflects the conservation of matter. This model is compared with a similar model, which incorporates MBP using the trade-off approach to measure productivity and eco-efficiency trends of power plants. Results reveal similar findings for both models substantiating robustness and applicability of the proposed model in this paper

    Environmentalism in the EU-28 context: the impact of governance quality on environmental energy efficiency

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    Environmental policies are a significant cornerstone of a developed economy, but the question that arises is whether such policies lead to a sustainable growth path. It is clear that the energy sector plays a pivotal role in environmental policies, and although the current literature has focused on examining the link between energy consumption and economic growth through an abundance of studies, it does not explicitly consider the role of institutional or governance quality variables in the process. Both globalization and democracy are important drivers of sustainability, while environmentalism is essential for the objective of gaining a “better world.” Governance quality is expected to be the key, not only for economic purposes but also for the efficiency of environmental policies. To that end, the analysis in this paper explores the link between governance quality and energy efficiency for the EU-28 countries, spanning the period 1995 to 2014. The findings document that there is a nexus between energy efficiency and income they move together: the most efficient countries are in the group with higher GDP per capita. Furthermore, the results show that governance quality is an important driver of energy efficiency and, hence, of environmental policies.University of Granad

    An improvement to multiple criteria ABC inventory classification

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    In this paper we presented an extended version of the Ng-modelg [W.L. Ng, A simple classifier for multiple criteria ABC analysis, European Journal of Operational Research 177 (2007) 344-353] for multi-criteria inventory classification. The proposed model is a nonlinear programming model which determines a common set of weights for all the items. Our model not only incorporates multiple criteria for ABC classification, but also maintains the effects of weights in the final solution, an improvement over the model proposed by Ng. An illustrative example is presented to compare our model and the Ng-model.ABC inventory classification Multiple criteria analysis

    Ranking the Alternatives With a Modified TOPSIS Method in Multiple Attribute Decision Making Problems

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