Analyzing sustainable power supply chain performance

PurposeThe purpose of this paper is to propose a modified model in multi-stage processes when there are intermediate measures between the stages and in this sense, the new efficiency scores are more accurate. Conventional data envelopment analysis (DEA) models disregard the internal structures of peer decision-making units (DMUs) in evaluating their relative efficiency. Such an approach would cause managers to lose important DMU information. Therefore, in multistage processes, traditional DEA models encounter problems when intermediate measures are used for efficiency evaluation.Design/methodology/approachIn this study, two-stage additive integer-valued DEA models were proposed. Three models were proposed for measuring inefficiency slacks in each stage and in the system as a whole.FindingsThree models were proposed for measuring inefficiency slacks in each stage and in the system as a whole.Originality/valueThe advantage of the proposed models for multi-stage systems is that they can accurately determine the stages with the greatest weaknesses/strengths. By introducing an applied case in the Iranian power industry, the paper demonstrated the applications and advantages of the proposed models.

To examine the resource utilization in different phases such as development and sales within China's real estate industry, this paper employs a two-stage Data Envelopment Analysis (DEA) model to measure the production efficiency of the real estate industry across 31 provinces, municipalities, and autonomous regions of China from 2014 to 2022. By examining both overall and phase-specific trends, the study utilizes a panel Tobit model to explore the factors affecting efficiency. Empirical results indicate that the leverage ratio of companies, per capita GDP of regions, and real estate regulatory policies significantly impact production efficiency. Further analysis of regional heterogeneity and its effect on production efficiency revealed that the per capita residential building area, which reflects the housing stock configuration in different regions, exhibits a significant single threshold effect. This not only objectively assesses the utilization of real estate resources in different areas but also delves deeper into the principal factors and their mechanisms affecting the production efficiency of the real estate industry, thus providing theoretical support and policy recommendations for effectively enhancing production efficiency.

In a two-stage system with two divisions connected in series, fairly setting the target outputs for the first stage or equivalently the target inputs for the second stage is critical, in order to ensure that the two stages have incentives to collaborate with each other to achieve the best performance of the whole system. Data envelopment analysis (DEA) as a non-parametric approach for efficiency evaluation of multi-input, multi-output systems has drawn a lot of attention. Recently, many two-stage DEA models were developed for studying the internal structures of two-stage systems. However, there was no work studying the fair setting of the target intermediate products (or intermediate measures) although unreasonable setting will result in unfairness to the two stages because setting higher (fewer) intermediate measures means that the first (second) stage must make more efforts to achieve the overall production plan. In this chapter, a new DEA model taking account of fairness in the setting of the intermediate products is proposed, where the fairness is interpreted based on the Nash bargaining game model, in which the two stages negotiate their target efficiencies in the two-stage system based on their individual efficiencies. This approach is illustrated by an empirical application to insurance companies.

Bounded additive models in data envelopment analysis (DEA) under the assumption of constant returns to scale (CRS) were recently introduced in the literature (Cooper et al. in J Product Anal 35(2):85–94, 2011; Pastor et al. in J Product Anal 40:285–292, 2013; Pastor et al. in Omega 56:16–24, 2015). In this paper, we propose to extend the so far generated knowledge about bounded additive models to the family of directional distance function (DDF) models in DEA, giving rise to a completely new subfamily of bounded or partially-bounded CRS-DDF models. We finally check the new approach on a real agricultural panel data set estimating efficiency and productivity change over time, resorting to the Luenberger indicator in a context where at least one variable is naturally bounded.

PurposeThe purpose of this paper is to evaluate Chinese commercial banks efficiency based on different non-performing loans in the process. Moreover, we identified the difference among different types of banks (state-owned commercial banks, joint-stock commercial banks and city commercial banks) and different operation stages (deposit producing sub-stage, profit earning sub-stage and overall stage).Design/methodology/approachAssurance region (AR) restrictions are combined with a two-stage data envelopment analysis (DEA) model. The efficiency scores of 26 Chinese commercial banks (listed banks) are analyzed by a two-stage AR-DEA model in the study period of 2013–2017.FindingsThe results show that state-owned commercial banks had better performance than joint-stock commercial banks and city commercial banks over the five-year study period. The development of Internet finance has positive impact on deposit producing sub-stage and insignificant non-homogeneity existed among the different groups in the circumstances of considering different non-performing loans.Practical implicationsThe research findings provide practical insights that help bank managers find the defects in operation process, which need to be improved.Originality/valuePrevious studies viewed non-performing loans as an integrated whole variable. The paper divides non-performing loans into three categories based on the risk and investigates the effect of different types of loans on bank efficiency scores.

This study aims to explore whether a machine learning algorithm can be used to make improvements in assessing unit efficiencies via a data envelopment analysis (DEA) model. In this study, a DEA model is used to calculate the efficiency scores of Desicion Making Units (DMUs). Then, an ML algorithm is trained that aims to predict the single output using inputs. Ranking of input features based on relative feature importance values obtained from the trained ML model is fed to the DEA model as weight restrictions. As a result, the two DEA models are compared with each other. ML-based insights (feature importance ranking) improve the DEA model in the direction of fewer zero weights. The additional weight restrictions are data depdendent, and hence realistic. As a novel approach, this study proposes the use of machine learning-based feature importance values to overcome a limitation of a DEA model.

The appreciative democratic voice of DEA: A case of faculty academic performance evaluation

Relevance. Public health effectiveness is crucial for population health, especially in the face of global challenges like the COVID-19 pandemic. The study applies the Data Envelopment Analysis (DEA) model to measure the efficiency of operating costs in the health care system across various regions of Kazakhstan from 2017 to 2021. Existing methods for assessing healthcare effectiveness often overlook the system's complexity, which turns DEA into a valuable tool to identify inequalities in healthcare availability and quality. Research objective. This study aims to employ the DEA model to measure the efficiency of operating costs in the health care system across regions of Kazakhstan in 2017-2021. Data and methods. The DEA model was chosen for its ability to analyze the efficiency of operating costs. Data were collected from the Agency for Strategic Planning and Reforms of the Republic of Kazakhstan. Results. Our findings indicate the need for increased healthcare financing in specific regions, emphasizing the importance of transparent spending. The study concludes that the DEA model can regularly assess health financing, ensuring resources are directed where most needed. The novelty lies in establishing a link between financing and health outcomes. Conclusions. The study's results and methodology can be used by public health authorities in assessing operating costs' effectiveness, allocating resources judiciously, and making informed decisions to enhance the healthcare system.

Assessing integrated water use and wastewater treatment systems in China: A mixed network structure two-stage SBM DEA model

Urban health efficiency is influenced by both urban planning decisions and the functioning of the public health system. However, there is currently a lack of effective methods to simultaneously examine urban planning and public health when assessing urban health efficiency. This study proposes a two-stage network data envelopment analysis (DEA) model for evaluating urban health efficiency. The urban planning and public health sectors act as interconnected stages where the public facilities and living quality are mediating variables. The urban health efficiencies of 303 Japanese cities are examined using the proposed model, showing that most Japanese cities still have room for urban planning improvement with pure technical efficiency ranging between 0.7 and 0.9. At the same time, most public health sectors of Japanese cities reach pure technical efficiencies larger than 0.95 but are limited by the scale efficiency. Further analysis points out that public health sectors are in decreasing-return-to-scale, meaning that excessive urban scale adversely affects urban health efficiency. Furthermore, cities that joined the Alliance for Healthy Cities (AFHC) performed better on average, suggesting that information sharing and national and international networking can boost urban health. The study shows that the proposed two-stage network DEA model can be applied to analyze urban health efficiency and identify the impact of technology and urban scale in different stages.

Assessing R&D efficiency using a two-stage dynamic DEA model: A case study of research institutes in the Chinese Academy of Sciences

Data Envelopment Analysis (DEA) models and Free Disposal Hull (FDH) models were proposed almost simultaneously in the scientific literature. In DEA models, the constraints generate a convex set. For this reason, optimization methods and software are widely used for modeling and computations for these models. Non-convexity of production possibility set of FDH models refrained significantly the development of these models. In this paper, two methods are proposed for two-and three-dimensional frontier visualization for DEA and FDH models. Computational experiments using real-life datasets documented reliability and effectiveness of proposed methods.

The directional distance function model is a generalization of the radial model in data envelopment analysis (DEA).The directional distance function model is appropriate for dealing with cases where undesirable outputs exist.However, it is not a units-invariant measure of efficiency, which limits its accuracy.In this paper, we develop a data normalization method for DEA, which is a universal solution for the problem of units-invariance in DEA.The efficiency scores remain unchanged when the original data are replaced with the normalized data in the existing units-invariant DEA models, including the radial and slack-based measure models, i.e., the data normalization method is compatible with the radial and slack-based measure models.Based on normalized data, a units-invariant efficiency measure for the directional distance function model is defined.

A generalized model for data envelopment analysis with interval data

This paper investigates the extent, dynamics, and factors influencing technical efficiency (TE) and capacity utilisation (CU) in small-scale fisheries (SSF) using a two-stage data envelopment analysis (DEA) approach covering the period 2010–2012. A considerable extent of boat-level technical inefficiency, capacity underutilisation and scale inefficiency were evident. On average, TE and CU levels under the constant returns to scale (CRS) and variable returns to scale (VRS) models declined over time. The TE and CU scores of 2010 remained unaltered with the addition of ‘fishing time’ as an input to the model. The proportion of boats with unitary scale efficiency (SE) decreased from 26 % in 2010 to 12 % in 2012. The underutilisation rates of the inputs ‘crew’ and ‘fishing time’ ranged from 15.5 % to 31.6 % and 15.8 % to 28.6 %, respectively. Among the species category, the extent of excess capacity was 70 % to 156 % and 47 % to 119 % under the CRS and VRS models, respectively. The second-stage DEA results indicated that the explanatory variables ‘fishing location’, ‘catch per unit of effort’ (CPUE), ‘fuel costs’ and ‘crew share’ significantly influenced CU under the CRS model. In contrast, the significant influence of subsidies and other operating costs were noted under the VRS model. For the TE case, ‘age’, ‘education’, ‘subsidy’ and ‘CPUE’ were found to be significant under the CRS and VRS models. Other significant variables were found in the study under CRS and VRS models. Finally, the results from the descriptive and empirical analysis under the two-stage DEA model are discussed together with policy implications.