Measuring the performance and returns to scale of forest management plans using data envelopment analysis approach (Case study; Iranian Caspian forests)

The aim of this study was to assess the relative efficiency of the Iranian forest management plans using the nonparametric method – Data Envelopment Analysis (DEA) as a well-known and robust technique for measuring the relative efficiency of organizations. The relative efficiency of forest management plans was calculated using the most frequency DEA models such as global technical efficiency (CCR), local pure technical efficiency (BCC) and Scale Efficiency (SE) on 12 units in Guilan Province, Iran. According to the results of CCR and BCC models, the efficiency averaged 0.83 and 0.93, respectively. The results of SE discussed a worrying aspect of these units efficiency; namely, there were only 3 efficient forest management plans (Shafaroud, Nav and Fiyab). However, the Scale Efficiency Index (SEI) brings out some interesting points; there were approximately 58% (7 units out of 12) under Increasing Returns to Scale (IRS). Therefore, the managers of forest management plans should focus more on the plans under IRS, so that they will have the opportunity to become more efficient through growth, otherwise managers will not be able to promote their overall productivity

Performance evaluation of forest management plans (Case study: Iranian Caspian forests)

The aim of this research was to measure the relative efficiency of forest management plans in north of Iran. In order to fulfill the research, data of 12 forest management plans were collected from the financial balance sheets of Shafaroud Forest Company during a ten years period. First of all, basic Data Envelopment Analysis (DEA) models (BCC and CCR) were used to determine the efficiency. Then, due to the structure of the forest management plan, cost efficiency and revenue efficiency models based on DEA were used in order to measure the efficiency. Results indicated that 8 forest management plans were efficient based on BCC and CCR models. Furthermore, the results indicated that only one forest management plan was efficient based on cost efficiency and revenue efficiency models. These results could be due to the input oriented properties of the models, rational management and optimal use of resources

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

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

Allocating the fixed cost:an approach based on data envelopment analysis and cooperative game

Allocating the fixed cost among a set of users in a fair way is an important issue both in management and economic research. Recently, Du et al. (Eur J Oper Res 235(1): 206–214, 2014) proposed a novel approach for allocating the fixed cost based on the game cross-efficiency method by taking the game relations among users in efficiency evaluation. This paper proves that the novel approach of Du et al. (Eur J Oper Res 235(1): 206–214, 2014) is equivalent to the efficiency maximization approach of Li et al. (Omega 41(1): 55–60, 2013), and may exist multiple optimal cost allocation plans. Taking into account the game relations in the allocation process, this paper proposes a cooperative game approach, and uses the nucleolus as a solution to the proposed cooperative game. The proposed approach in this paper is illustrated with a dataset from the prior literature and a real dataset of a steel and iron enterprise in China

A classification of DEA models when the internal structure of the Decision Making Units is considered

We classify the contributions of DEA literature assessing Decision Making Units (DMUs) whose internal structure is known. Starting from an elementary framework, we define the main research areas as shared flow, multilevel and network models, depending on the assumptions they are subject to. For each model category, the principal mathematical formulations are introduced along with their main variants, extensions and applications. We also discuss the results of aggregating efficiency measures and of considering DMUs as submitted to a central authority that imposes constraints or targets on them. A common feature among the several models is that the efficiency evaluation of the DMU depends on the efficiency values of its subunits thereby increasing the discrimination power of DEA methodology with respect to the black box approach

Efficiency measurement using nonparametric production analysis in the presence of undesirable outputs : an application to power plants

In order to deal with undesirable products in models of performance analysis, we need to replace the assumption of free disposability by weak disposability and this assumption has been used to model undesirable products as outputs. The traditional axiom of weak disposability of Shephard (1970) is given in a multiplier form and, in this sense, the level of bad outputs is equal to zero if and only if the level of the desirable outputs is equal to zero. An alternative definition of the weak disposability of outputs in additive form has been proposed. An axiomatic foundation has been introduced to construct a new production technology space in the presence of undesirable outputs. The model is illustrated using real data from 92 coal fired power plants

Slack-Based measurement with Rough Data

Abstract Rough data envelopment analysis (RDEA) evaluates the performance of the decision making units (DMU s ) under rough uncertainty assumption. In this paper, new discussion regarding RDEA is extended. The RSBM model is proposed by integrating SBM model and rough set theory. The process of reaching solution is presented and this model is applied to efficiency evaluation of the DMU s with uncertain information

Measuring the efficiency of two stage network processes: a satisficing DEA approach

NoRegular Network Data Envelopment Analysis (NDEA) models deal with evaluating the performance of a set of decision-making units (DMUs) with a two-stage construction in the context of a deterministic data set. In the real world, however, observations may display a stochastic behavior. To the best of our knowledge, despite the existing research done with different data types, studies on two-stage processes with stochastic data are still very limited. This paper proposes a two-stage network DEA model with stochastic data. The stochastic two-stage network DEA model is formulated based on the satisficing DEA models of chance-constrained programming and the leader-follower concepts. According to the probability distribution properties and under the assumption of the single random factor of the data, the probabilistic form of the model is transformed into its equivalent deterministic linear programming model. In addition, the relationship between the two stages as the leader and the follower, respectively, at different confidence levels and under different aspiration levels, is discussed. The proposed model is further applied to a real case concerning 16 commercial banks in China in order to confirm the applicability of the proposed approach at different confidence levels and under different aspiration levels