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Safety efficiency is the key to balance safety and production in construction industry; but the existing safety efficiency evaluation methods have the limitations of overestimating efficiency and ignoring undesirable outputs; therefore, according to the characteristics of safety production in construction industry, this paper innovatively develops a new cross-efficiency data envelopment analysis method to analyze safety efficiency, which can solve the limitations of traditional methods; and then the safety efficiency and its influencing factors of China's construction industry are analyzed, and some useful conclusions are obtained to improve its safety efficiency.

A new cross-efficiency data envelopment analysis method with undesirable outputs is proposed; and the two-stage efficiency analysis framework is designed.

First, the construction industries in different areas have different reasons for affecting their safety efficiency; second, the evaluation results of global safety priority tend to be more acceptable; third, frequent safety accidents and low resource utilization lead to a slow downward trend of the safety efficiency of China's construction industry in the long run; fourth, construction engineering supervision, construction industrial scale, and construction industrial structure have the significant impact on safety efficiency.

Theoretically, a new cross-efficiency data envelopment analysis method with undesirable outputs is proposed for evaluating safety efficiency; practically, the safety efficiency and its influencing factors of China's construction industry are analyzed.

The purpose of this paper is to present a case study in which data envelopment analysis (DEA) is used to evaluate and classify the suppliers of a Colombian motorcycle assembly company. This tool allows the integration of several attributes into single performance measures (cross-efficiency and diversity efficiency) and subsequent classification based on the values obtained for these two metrics.

The classification uses a methodology based on two main tools. The first is an input-oriented cross-efficiency DEA model with ordinal variables to evaluate the suppliers’ performance, and the second is a classification of these into categories that identifies those with good performance for features that make them outstanding.

The assembly company segments its suppliers according to supply frequency. The results show that suppliers working under a just-in-time system achieve superior performance with respect to other suppliers.

The application of this methodology in a real-world case illustrates how DEA can be a useful tool to support the evaluation and classification of suppliers (a process of increasing complexity given the current trend to include multiple strategic measures together with classical operational measures). Moreover, the methodology illustrated in the study can be adapted to other similar settings.

The main contributions of this paper are twofold. First, to the best of our knowledge, this is the first study to illustrate the use of DEA in a real case related to supplier evaluation. Second, the presence of ordinal variables (e.g. quality or environmental ratings) gives rise to DEA variants seldom used in this context.

Este artículo presenta un caso de estudio en el que se utiliza análisis envolvente de datos (DEA) para evaluar y clasificar los proveedores de una ensambladora colombiana de motocicletas. Dicha herramienta permite integrar múltiples atributos en dos medidas de desempeño (eficiencia cruzada y de diversidad) y la posterior clasificación de éstos con base en los valores obtenidos para ambas medidas.

La clasificación usa una metodología basada en dos herramientas. La primera es un modelo DEA de eficiencia cruzada orientado a las entradas con variables ordinales que se usa para evaluar el desempeño de los proveedores. La segunda es una clasificación de los proveedores en categorías para identificar aquellos con buen desempeño en algunas características que los hacen sobresalientes.

La compañía segmenta sus proveedores de acuerdo con la frecuencia de abastecimiento. Los resultados muestran que los proveedores que operan bajo justo a tiempo (Just-in-time, JIT) tienen un desempeño superior con respecto a los demás proveedores.

La aplicación de esta metodología en un caso real ilustra como DEA es una herramienta útil para apoyar la evaluación y clasificación de proveedores (un proceso de complejidad creciente gracias a la tendencia actual de incluir medidas estratégicas junto a las medidas operacionales comúnmente utilizadas). Además, la metodología utilizada puede adaptarse fácilmente a otras situaciones similares.

Las contribuciones de este artículo son dos. Primero, hasta donde sabemos, este es el primer estudio que ilustra el uso de DEA en un caso real de evaluación de proveedores. Segundo, la presencia de variables ordinales (por ejemplo, evaluaciones de calidad y medioambiente) resultan en modelos DEA que son poco utilizados en este contexto.

The paper aims to carry out a performance benchmarking study of the zones of Indian Railways (IR) to develop an alternate approach for measurement of aggregate operational performance of the railway zones and to envisage its operations in a supply chain perspective, so as to gain academic and practical insights.

A case study research employing data envelopment analysis (DEA) methodology has been used, with the help of data obtained from the IR annual statistical statements published by the Ministry of Railways, Government of India.

Within the set of inputs and outputs considered, the exercise identified the best performing railway zones over the years and the efficiency trends. Some weaknesses of the conventional DEA were addressed by including the concept of cross‐efficiencies along with self‐efficiencies, by analyzing longitudinal data spread over four years and also by comparing the efficiencies with the operating ratios. To an extent, this study has also helped to understand the impact of the recent restructuring of the zones on their performance.

The study enables the reader to gain some valuable insights from a managerial perspective for IR so as to formulate strategies of its zones to foster better performance.

The aim of this article is to construct a performance evaluation framework that can be employed in companies to enhance their business operations and strengthen their financial advantage in the current environment. To validate the approach, a case example has been included to assess the practicality and validity of this approach when applied in a real environment.

This study focuses on an important part of the strategic planning process: internal scrutiny and environmental (external) scanning, in which an evaluation of company performance is divided into two stages by using network DEA and the cross-efficiency approach. In addition, this article employs Miles and Snow's typology for classifying the strategies used by companies.

The analytical results show that the proposed framework can be useful for companies seeking to evaluate which strategies may be the most appropriate, based on Miles and Snow's typology, to effectively reallocate limited resources.

The evaluation in this study only uses financial data and does not take other nonfinancial indicators into consideration.

This research provides value by classifying each company included in the study in terms of its capability and financial efficiency according to Miles and Snow's system of strategy classification. Second, an internal and external performance measuring framework is constructed. Finally, some propositions for top management are provided by analyzing the financial advantages of using a performance evaluation framework that can help top management make decisions more objectively.

The purpose of this paper is to ascertain the performance of Indian hospitals in recent past and derive meaningful insights for policy makers and practicing managers in this area.

This paper analyses the technical efficiency of select Indian private hospitals using three related methodologies: data envelopment analysis (DEA), Malmquist Productivity Index (MPI) and Tobit regression. Two output variables (i.e. total income and profit after tax) and four input variables (i.e. cost of labour, net fixed assets, current assets and other operating expenses) were selected for the purpose of the study.

DEA analysis has shown that 14 out of 37 hospitals are found to be efficient under the Cooper and Rhodes model of DEA and 20 out of 37 hospitals are efficient under the Banker, Charles and Cooper model of DEA. The empirical results pertaining to MPI indicate an overall productivity progress in the private Indian hospital industry during the study period, which is largely due to technological advancement in the industry. Tobit regression demonstrates that chain affiliated, specialized and multi-city located hospitals exhibit a higher technical efficiency.

This study has a limitation with reference to the unavailability of data on the input and output parameters of the model. The data related to the number of beds, number of doctors, number of nurses, etc., were not available for the period under consideration.

This study seems to be one of the few studies applying productivity and performance analysis using DEA, MPI and Tobit regression for the Indian private hospital industry.

The establishment of free trade zones (FTZs) is an important experiment as part of the reform and opening up policy. This paper aims to focus on the issue of production efficiency of environmental protection enterprises in FTZs. Changes in the external and internal environments of enterprises can easily lead to changes in the production efficiencies of enterprises. The establishment of FTZs can change the external environment of enterprises. Knowledge accumulation changes the internal environment of enterprises. However, traditional efficiency analysis has usually ignored the internal and external heterogeneity of decision-making units, resulting in the distortion of the evaluation results.

This paper examines the relationship between knowledge accumulation and development potential based on financial data of environmental protection enterprises that were listed in Shanghai and Shenzhen A stocks, from 2009 to 2016. Then, through data envelopment analysis cross-efficiency analysis of the listed environmental protection enterprises from external heterogeneity, BP neural network model is set up.

The results show that the model set up in this paper is stable and reliable. The paper makes policy suggestions such as stimulating trade inside industry, quickening technological progress and enhancing environmental protection.

This paper analyzes the economy, environmental protection, science and technology and education to simulate the external environment of enterprises. Based on the experience data from the completion of Shanghai FTZ, this paper predicts the future development potential of Hainan FTZ enterprises.

Environmental performance becomes a key issue for the sustainable development. Recently, incremental information technology is adopted to collect environmental data and improve environmental performance. Previous environmental efficiency measures mainly focus on individual decision-making units (DMUs). Benefited from the information technology, this paper develops a new environmental efficiency measure to explore the implicit alliances among DMUs and applies it to Xiangjiang River.

This study formulates a new data envelopment analysis (DEA) environmental cross-efficiency measure that considers DMUs' alliances. Each DMUs' alliance is formulated by the DMUs who are supervised by the same manager. In cross-efficiency evaluation context, this paper adopts DMUs' alliances rather than individual DMUs to derive the environmental cross-efficiency measure considering undesirable outputs. Furthermore, the Tobit regression is conducted to analyze the influence of exogenous factors about the environmental cross-efficiency.

The findings show that (1) Chenzhou performs the best while Xiangtan performed the worst along Xiangjiang River. (2) The environmental efficiency of cities in Xiangjiang River is generally low. Increasing public budgetary expenditure can improve environmental efficiency of cities. (3) The larger the alliance size, the higher environmental efficiency. (4) The income level is negatively correlated with environmental efficiency, indicating that the economy is at the expense of the environment in Xiangjiang River.

This paper contributes to developing a new environmental DEA cross-efficiency measure considering DMUs' alliance, and combining DEA cross-efficiency and Tobit regression in environmental performance measurement of Xiangjiang River. This paper examines the exogenous factors that have influences on environmental efficiency of Xiangjiang River and derive policy implications to improve the sustainable operation.

This paper is focused on developing an integrated algorithmic approach named as data envelopment analysis and multicriteria decision-making (DEA-MCDM) for ranking decision-making units (DMUs) based on cross-efficiency technique and subjective preference(s) of the decision maker.

Self-evaluation in data envelopment analysis (DEA) lacks in discrimination power among DMUs. To fix this, a cross-efficiency technique has been introduced that ranks DMUs based on peer-evaluation. Different cross-efficiency formulations such as aggressive and benevolent and neutral are available in the literature. The existing ranking approaches fail to incorporate subjective preference of “one” or “some” or “all” or “most” of the cross-efficiency evaluation formulations. Therefore, the integrated framework in this paper, based on DEA and multicriteria decision-making (MCDM), aims to present a ranking approach to incorporate different cross-efficiency formulations as well as subjective preference(s) of decision maker.

The proposed approach has an advantage that each of the aggressive, benevolent and neutral cross-efficiency formulations contribute to select the best alternative among the DMUs in a MCDM problem. Ordered weighted averaging (OWA) aggregation is applied to aggregate final cross-efficiencies and to achieve complete ranking of the DMUs. This new approach is further illustrated and compared with existing MCDM approaches like simple additive weighting (SAW) and Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) to prove its validity in real situations.

The choice of cross-efficiency formulation(s) as per subjective preference of the decision maker and different orness levels lead to different aggregated scores and thus ranking of the DMUs accordingly. The proposed ranking approach is highly useful in real applications like R and D projects, flexible manufacturing systems, electricity distribution sector, banking industry, labor assignment and the economic environmental performances for ranking and benchmarking.

To prove the practical applicability and robustness of the proposed integrated DEA-MCDM approach, it is applied to top twelve Indian banks in terms of three inputs and two outputs for the period 2018–2019. The findings of the study (1) ensure the impact of non-performing assets (NPAs) on the ranking of the selected banks and (2) are enormously valuable for the bank experts and policy makers to consider the impact of peer-evaluation and subjective preference(s) in formulating appropriate policies to improve performance and ranks of underperformed banks in competitive scenario.

To the best of the authors’ knowledge, this is the first study that has integrated both DEA and MCDM via OWA aggregation to present a ranking approach that can incorporate different cross-efficiency formulations and subjective preference(s) of the decision maker for ranking DMUs.

Using the most recent dataset from 2013–2014 to 2017–2018, the study examines the efficiency of 75 coal-fired power plants in the Indian thermal power sector. The authors obtained robust estimates of efficiency scores by employing Seiford and Zhu’s (2002) DEA-based classification invariance technique to account for CO₂ emissions as an undesirable output. Meta-frontier analysis and the Tobit regression are used to compute technology heterogeneity across power plants belonging to public and private groups and investigate the factors driving carbon-adjusted efficiency, respectively. The results reveal that, on average, the efficiency of power plants during the study period is 78.26%, showing significant room for reduction in CO₂ emissions alongside augmentation in electricity generation. Private plants are more efficient than public ones, and relative performance inefficiency is the primary source of inefficiency in the thermal power sector. Regression analysis indicates that domestic-equipped plants perform with lesser levels of efficiency, and plants with more units are more inefficient than plants with fewer units. Carbon productivity significantly improves efficiency since fewer fossil fuels with high carbon will generate more electricity.

The purpose of this paper is to propose a data envelopment analysis (DEA) method for customers' evaluation.

This paper introduces a variable return to scale (VRS) cross‐efficiency (one of the DEA models) to evaluate customers. This new model can consider ratio values and give a complete ranking of customers.

It is found that the proposed model can evaluate customers in a multi criteria context; does not demand weights from the decision maker; and can consider both the ratio and absolute numbers. An aggressive form of the VRS model is formulated to evaluate the peer‐appraisal value of customers instead of self‐appraisal.

To the best of the authors' knowledge, there is no reference that uses cross‐efficiency model with the ratio values for customers' evaluation.