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The aim of this paper is to classify suppliers into some clusters based on sustainability factors. However, there might be some unqualified suppliers and we should identify and remove those suppliers before clustering.

First, using fuzzy screening system, the authors identify and remove the unqualified suppliers. Then, the authors run their proposed clustering method. This paper proposes a data envelopment analysis (DEA) algorithm to cluster suppliers.

This paper presents a two-aspect DEA-based algorithm for clustering suppliers into clusters. The first aspect applied DEA to consider efficient frontiers and the second aspect applied DEA to consider inefficient frontiers. The authors examine their proposed clustering approach by a numerical example. The results confirmed that their method can cluster DMUs into clusters.

The main contributions of this paper are as follows: This paper develops a new clustering algorithm based on DEA models. This paper presents a new DEA model in inefficiency aspect. For the first time, the authors’ proposed algorithm uses fuzzy screening system and DEA to select suppliers. Our proposed method clusters suppliers of MPASR based on sustainability factors.

Productivity improvement is key to sustainability performance improvements of organizations. In a real-world scenario, the nature of inputs and outputs is likely to be imprecise and vague, leading to complexity in comparing firms' efficiency measurements. Implementation of fuzzy-logic based measurement systems is a method for dealing with such cases. This paper presents a fuzzy weight objective function to solve Data Envelopment Analysis (DEA) CCR model for measuring paper mills' performance in India for 15 years.

An integrated methodology is proposed to solve DEA models having fuzzy weights. The fuzzy DEA methodology is an extended version of the DEA approach that researchers have used for performance measurement purposes in imprecise and vague scenarios. The ecological performance of the paper industry is evaluated, considering some desirable and undesirable outputs. The effect of non-discretionary input on the performance of a paper mill is also analyzed.

Analysis suggests that the productivity of the paper industry is improving consistently throughout the period. The comparative evaluation of methods suggests that a diverse cluster of DMUs and integration of DEA with the fuzzy logic increases the diversity in the efficiency score while DEA-DE imitates the results of CCR DEA.

Proposed a fuzzy DEA-based analytical framework for measuring the paper industry's ecological performance in an imprecise and vague scenario. The model is tested on data from the paper industry in a developing country context and comparative performance analysis using DEA, fuzzy DEA and DE algorithm is done.

Evaluating the performance of supply chains is a convoluted task because of the complexity that is inextricably linked to the structure of the aforesaid chains. Therefore, the purpose of this paper is to present an integrated approach for evaluating and sizing real-life health-care supply chains in the presence of interval data.

To achieve the objective, this paper illustrates an approach called Latin hypercube sampling by replacement (LHSR) to identify a set of precise data from the interval data; then the standard data envelopment analysis (DEA) models can be used to assess the relative efficiencies of the supply chains under evaluation. A certain level of data aggregation is suggested to improve the discriminatory power of the DEA models and an experimental design is conducted to size the supply chains under assessment.

The newly developed integrated methodology assists the decision-makers (DMs) in comparing their real-life supply chains against peers and sizing their resources to achieve a certain level of production.

The proposed integrated DEA-based approach has been successfully implemented to suggest an appropriate structure to the actual public pharmaceutical supply chain in Morocco.

The originality of the proposed approach comes from the development of an integrated methodology to evaluate and size real-life health-care supply chains while taking into account interval data. This developed integrated technique certainly adds value to the health-care DMs for modelling their supply chains in today's world.

Data envelopment analysis (DEA) and analytical hierarchy process (AHP) are two widely applied methods to evaluate and rank suppliers in terms of sustainability. In this study, to investigate the extent to which potential differences in the outcomes of these two methods influence the benchmarking strategies, a comparative analysis based on a common set of data gathered from 19 logistics service providers is implemented.

As suppliers' sustainability cannot be improved in a single-step process due to several limitations, improvement needs to proceed gradually. Therefore, using the self-organising map method, the suppliers were classified into clusters within a novel framework for gradually improving their sustainability. Then, the two processes of gradual improvement based on the outcomes of DEA and AHP were compared.

The findings show that although the rankings of suppliers guided by the methods correlated to a high degree, the benchmarking strategies provided by the methods for gradually improving the sustainability of suppliers differed considerably. In particular, whereas AHP suggests a benchmarking policy better suited for unsustainable or less sustainable suppliers with limited access to resources, DEA proposes one for suppliers able to dramatically boost their sustainability with few quick, significant leaps in performance.

First, this study revealed a novel gradual improvement framework using the self-organising map method. Second, it clarified the extent to which the benchmarking policies are influenced by the type of evaluation method.

The purpose of this study is to propose a comprehensive benchmarking approach to help subsidiaries of a company to implement continuous improvement (CI).

The proposed approach classifies subsidiaries of a company into the three stages of CI. After that, the proposed approach employs data envelopment analysis (DEA)-super slacks-based measure (SBM) model and Malmquist Productivity Index (MPI) to evaluate the operational efficiency of the subsidiaries and selects benchmarking targets and sets benchmarking goals based on the operational efficiency for benchmarking of input and output criteria. Then, the proposed approach suggests a four-step benchmarking process for benchmarking of detailed efficiency. Finally, the proposed approach makes the subsidiaries achieve CI by implementing the input and output benchmarking and the efficiency benchmarking.

The results show that the proposed approach can help subsidiaries of a company to implement a staged benchmarking which considers input and output criteria and Malmquist productivity and efficiency comprehensively.

Generally, benchmarking is implemented in many aspects. However, due to the restriction of a company’s resource level, a practical and staged benchmarking is preferred. This study proposes a comprehensive approach to benchmark systematically and gradually, and provides a more reasonable benchmarking process for implementing CI.

Currently, there is a dearth of research studies regarding macro analysis of the workforce productivity of the US construction industry. The purpose of this paper is to calculate the workforce productivity changes of the US construction industry from 2006 to 2016, with the number of laborers as input and value of construction industry as output.

The present study introduced the data envelopment analysis (DEA) based Malmquist productivity index model to measure the workforce productivity of the US construction industry from 2006 to 2016.

The results indicated that the workforce productivity of the US construction industry experienced a continuous decline, except for the increases from 2011 to 2013 and from 2014 to 2015. It was also shown that there were gaps in the workforce productivity development level among all states and nine regions in the US construction industry. Besides, the relationship between workforce productivity and four aspects, including real estate price, workforce, climate distribution and economic factors, was analyzed.

The calculation of the productivity of the US construction industry is based on the premise that the external environment is fixed and unchanged from 2006 to 2016, but the multi-level DEA model for further calculation is required for obtaining more effective conclusions.

This paper measures the workforce productivity of the US construction industry over the past 11 years, which added latest analysis and knowledge into the construction industry, providing decision-makers with advice and data support to formulate policies to improve workforce productivity.

This study provided both government decision-makers and industrial practitioners with important macro background environment information, which will facilitate the improvement of workforce productivity in the construction industry in different regions of the US.

The purpose of this paper is to put forward the grey relational decision-making model of three-parameter interval grey number based on Analytic Hierarchy Process (AHP) and Data Envelopment Analysis (DEA), based on the previous study of grey relational decision-making model, and it considers the advantages of the decision-making schemes and the subjective preferences of decision makers.

First of all, through AHP, the preference of each index is analyzed and the index weight is determined. Second, the DEA model is adopted to obtain the index weight from the perspective of the most beneficial to each scheme and objectively reflect the advantages of different schemes. Then, assign the comprehensive weights to each index of the grey relational decision-making model of three-parameter interval grey number, and calculate the grey relation degree of each scheme to rank the schemes.

The effectiveness of the model is proved by an example of carrier aircraft selection.

The applicability of this model is analyzed by taking carrier aircraft selection as an example. In fact, this model can also be widely used in agriculture, industry, economy, society and other fields.

In this paper, the combination of AHP and DEA is used to determine the index weight. Based on which, the grey relation degree under the three-parameter interval grey number is calculated. It intended the application space of the grey relational decision-making model.

The aim of this analysis is to review the Indian manufacturing organizations practicing Lean Six Sigma (LSS) tools/techniques with an objective of monitoring the performance of an organization and to develop recommendation for strategies to benchmark organizational operational efficiency.

This study offers insights of the LSS performance measurement aspects of the Indian manufacturing organizations based on Data envelopment analysis (DEA) approach. The five inputs and two outputs are considered on the basis of literature review and discussed with the practitioners.

In this analysis, the relative efficiency score of 18 Indian manufacturing organizations has been determined in order to assist evaluation of the impact of monetary investment on the outputs. The present analysis not only investigates the optimum level of input variables but also lays down a significant observation that an organization having higher profit and inventory turnover ratio is not necessarily an efficient organization.

The results assist to determine the best practice units, potential source of inefficiency and deliver beneficial data for the consistent enhancement of the operational efficiency. The DEA results assist managers and decision makers to derive appropriate strategies to enhance their performance with reference to the efficient organization and to regard it as their role model.

This analysis renders a DEA based framework of LSS practicing Indian manufacturing organizations. The framework is unique in terms of its input-outputs variable selection and measurement procedure.

Total factor productivity (TFP) has become a prominent concept in agriculture economics and policy over the last three decades. The main aim of this paper is to obtain a detailed picture of the field via bibliometric analysis to identify research streams and future research agenda.

The data sample consists of 472 papers in several bibliometric exercises. Citation and collaboration structure analyses are employed to identify most important authors and journals and track the interconnections between main authors and institutions. Next, content analysis based on bibliographic coupling is conducted to identify main research streams in TFP.

Three research streams in agricultural TFP research were distinguished: TFP growth in developing countries in the context of policy reforms (1), TFP in the context of new challenges in agriculture (2) and finally, non-parametric TFP decomposition based on secondary data (3).

This research indicates agenda of future TFP research, in particular broadening the concept of TFP to the problems of policy, environment and technology in emerging countries. It provides description of the current state of the art in the agricultural TFP literature and can serve as a “guide” to the field.

This paper empirically examines the short-term and long-term associations between risk, capital and efficiency (R-C-E) in the Indian banking sector across 2008–2019 to answer the presence of causation or contemporaneousness in the R-C-E nexus.

The paper focuses on three objectives. First, the authors determine short-term causality in the risk–efficiency relationship by studying the simultaneous influence of a wide array of banking risks on DEA-based technical and cost efficiency in static and dynamic situations. Second, the authors introduce bank capital and contemporaneously determine the interplay between R-C-E using seemingly unrelated regression equation (SURE) and three-staged least squares (3SLS). Last, the authors assess stability in inter-temporal associations using Granger causality in an autoregressive distributed lag (ARDL) generalized method of moments (GMM) framework.

The authors contend that high capital buffers reduce insolvency risk and increase bank stability. Technically efficient banks carry lesser equity buffers, suggesting a trade-off between capital and efficiency. However, capitalization makes banks more technically efficient but not cost-efficient, implying that over-capitalization creates cost inefficiencies, which, in line with the cost skimping hypothesis, forces banks to undertake risk. Concerning causal relationships, the authors conclude that inefficiency Granger-causes insolvency and increases bank risk. Further, steady increases in capital precede technical and cost efficiency improvements. The converse also holds as more efficient banks depict temporal increases in capitalization levels.

The paper is perhaps the first that acknowledges the influence of the “time” perspective on the R-C-E nexus in an emerging economy and advocates that prudential regulations must focus on short-term and long-term intricacies among the triumvirate to foster a stable banking environment.