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The concept of productivity is central to performance management and decision-making, although it is complex and multifaceted. This paper aims to describe a methodology based on the use of Big Data in a cluster analysis combined with a data envelopment analysis (DEA) that provides accurate and reliable productivity measures in a large network of retailers.

The methodology is described using a case study of a leading kitchen furniture producer. More specifically, Big Data is used in a two-step analysis prior to the DEA to automatically cluster a large number of retailers into groups that are homogeneous in terms of structural and environmental factors and assess a within-the-group level of productivity of the retailers.

The proposed methodology helps reduce the heterogeneity among the units analysed, which is a major concern in DEA applications. The data-driven factorial and clustering technique allows for maximum within-group homogeneity and between-group heterogeneity by reducing subjective bias and dimensionality, which is embedded with the use of Big Data.

The use of Big Data in clustering applied to productivity analysis can provide managers with data-driven information about the structural and socio-economic characteristics of retailers' catchment areas, which is important in establishing potential productivity performance and optimizing resource allocation. The improved productivity indexes enable the setting of targets that are coherent with retailers' potential, which increases motivation and commitment.

This article proposes an innovative technique to enhance the accuracy of productivity measures through the use of Big Data clustering and DEA. To the best of the authors’ knowledge, no attempts have been made to benefit from the use of Big Data in the literature on retail store productivity.

Conventional approaches such as Data Envelopment Analysis (DEA) and Fuzzy Data Envelopment Analysis (FDEA) cannot effectively account for uncertainty, which can lead to imprecise decision-making. Furthermore, these methods frequently rely on precise numbers, ignoring the inherent uncertainty of real-world data. To address this gap, the research question arises: How can we develop a methodology that combines Z-number theory and FDEA to provide a comprehensive assessment of residency preferences in European countries while accounting for uncertainty in information reliability? The proposed methodology aims to fill this gap by incorporating Z-number theory and FDEA.

The proposed study assesses residency preferences across 39 European countries, focusing on key factors like environment, sustainability, technology, education, and development, which significantly influence individuals' residency choices. Unlike conventional DEA and FDEA approaches, the proposed method introduces a novel consideration: dependability. This inclusion aims to refine decision-making precision by accounting for uncertainties related to data reliability. The proposed methodology utilizes an interval approach, specifically employing the a-cut approach with interval values in the second step. Unlike using crisp values, this interval programming resolves formulations to determine the efficiencies of decision-making units (DMUs).

The comprehensive findings provide valuable insights into the distinctive factors of European nations, aiding informed decision-making for residency choices. Malta (75.6%-76.1%-75.8%), Austria (78.2%-78%-76.1%), and the United Kingdom (79.3%-78.4%-77%) stand out with distinct characteristics at levels of a = 0-a = 0.5-a = 1, assuming the independence of variables of the overall evaluation. Individual consideration of each factor reveals various countries as prominent contenders, except for the environmental factor, which remains consistent across countries.

Traditional DEA models encounter challenges when dealing with uncertainties and inaccuracies, particularly in the evaluation of large systems. To overcome these limitations, we propose integrating Z-numbers—a powerful mathematical tool for modeling uncertainty—into the conventional DEA process. Our methodology not only assesses the effectiveness of countries across various socio-economic and environmental metrics but also explicitly addresses the inherent uncertainties associated with the data. By doing so, it aims to enhance the precision of decision-making and provide valuable insights for policymakers and stakeholders.

This study aims to investigate environmental efficiency based on energy change by using energy-related or nonenergy-related variables by reckoning with months and years as decision-making units (DMUs) for a hospital under radial and nonradial models.

The non-oriented slack-based measures (SBM)-data envelopment analysis (DEA) model considering desirable and undesirable outputs has been embraced in this study, where its obtained results were compared with the results of other DEA models are output-oriented SBM-DEA and Banker, Charnes, & Cooper-DEA. For this purpose, this research has used a data set covering the 2012–2018 period for a reference hospital, which includes energy-related and nonenergy-related variables.

The results demonstrate that environmental efficiency based on energy reached the highest level in the winter months, whereas the summer months have the lowest efficiency values arising from the increasing electricity consumption due to high cooling needs. According to results of the non-oriented SBM model, the month with the highest efficiency in all periods is January with a 0.936 average efficiency score, the lowest month is August with a 0.406 value.

This paper differs from other studies related to energy and environmental efficiencies in the literature with some aspects. First, to the best of the authors’ knowledge, this study is the first one that takes into account time periods (months and years) as (DMUs for a single organization. Second, this study investigates environmental nonefficiencies, which are derived from energy uses and factors affecting energy use.

This study aims to present an innovative predictive methodology that transitions from traditional efficiency assessment techniques to a forward-looking strategy for evaluating working capital management(WCM) and its determinants by integrating data envelopment analysis (DEA) with artificial neural networks (ANN).

A slack-based measure (SBM) within DEA was used to evaluate the WCME of 1,388 firms in the Indian manufacturing sector across nine industries over the period from April 2009 to March 2024. Subsequently, a fixed-effects model was used to determine the relationships between selected determinants and WCME. Moreover, the multi-layer perceptron method was applied to calculate the artificial neural network (ANN). Finally, sensitivity analysis was conducted to determine the relative significance of key predictors on WCME.

Manufacturing firms consistently operate at around 50% WCME throughout the study period. Furthermore, among the selected variables, ability to create internal resources, leverage, growth, total fixed assets and productivity are relatively significant vital predictors influencing WCME.

The integration of SBM-DEA and ANN represents the primary contribution of this research, introducing a novel approach to efficiency assessment. Unlike traditional models, the SBM-DEA model offers unit invariance and monotonicity for slacks, allowing it to handle zero and negative data, which overcomes the limitations of previous DEA models. This innovation leads to more accurate efficiency scores, enabling robust analysis. Furthermore, applying neural networks provides predictive insights by identifying critical predictors for WCME, equipping firms to address WCM challenges proactively.

The purpose of this paper is to develop a decision-making protocol to meet the new requirements in an atypical panorama, such as the economic instability, in the textile industry.

The methodology consists of analyzing technical criteria, costing parameters and efficiency scores of knitted fabrics using the data envelopment analysis (DEA) and classification and regression (C&R) trees models, together with statistical techniques. From these tools, it is possible to guide the portfolio management of these products in a textile company, identifying those that are inefficient and require immediate management measures. The results are expected to be replicated in other companies because the DEA and C&R trees analytical procedures are applicable to different portfolios, whether in the same industry or not.

The results allowed identifying inefficient textile products regarding the input-output relationship and the main technical reasons related to the most significant inefficiencies, such as fiber composition and knitted fabrics rapports used in manufacturing.

DEA and C&R trees, in combination with the study of textile technical parameters, can be fundamental to investigating the efficiency and profitability of industries in periods of economic instability or other adverse situations. In addition, it is noteworthy that there are practically no studies in the literature on DEA applied in the textile industry, indicating excellent development potential.

As some data to evaluate the efficiency of bank branches is qualitative or uncertain, only grey numbers should be used to calculate the efficiency interval. The combination of multi-stage models and grey data can lead to a more accurate and realistic evaluation to assess the performance of bank branches. This study aims to compute the efficiency of each branch of the bank as a grey number and to group all branches into four grey efficiency areas.

The key performance indicators are identified based on the balanced scorecard and previous research studies. They are included in the two-stage grey data envelopment analysis (DEA) model. The model is run using the GAMS program. The grey efficiencies are calculated and bank branches have been grouped based on efficiency kernel number and efficiency greyness degree.

As policies and management approaches for branches with less uncertainty in efficiency are different from branches with more uncertainty, considering the uncertainty of efficiency values of branches may be helpful for the policy-making of managers. The grey efficiency of branches of one bank is examined in this study using the two-stage grey DEA throughout one year. The branches are grouped based on kernel and greyness value of efficiency, and the findings show that considering the uncertainty of data makes the results more consistent with the real situation.

The performance of bank branches is modeled as a two-stage grey DEA, in which the efficiency value of each branch is obtained as a grey number. The main originality of this paper is to group the bank branches based on two grey indexes named “kernel number” and “greyness degree” of grey efficiency value.

This paper aims to propose an integrated use of balanced scorecard (BSC), data envelopment analysis (DEA) and game theory approach as an enhanced performance measurement technique to determine and rank the importance of manufacturing indicators of a steel company as a real case study.

An efficiency change ratio is defined to examine the characteristic function of each coalition which is super-additive. Then, the Shapley value index is used as the solution of the cooperative game to determine the importance of the BSC indicators of the company and rank order them.

The results reveal that “profitability rate” is the most important BSC indicator, whereas “customer satisfaction” is the least significant one. The ranking order of the importance of all BSC indicators makes it possible for the senior managers of the organization to realize the importance of each index separately and to improve the profitability and the number of customers by presenting programs according to the budget and time constraints.

The main contribution of this paper lies in the adoption of a game theory approach to performance measurement in the industrial sector that determines and ranks the importance of manufacturing indicators.

Today’s one of the most important difficulties is tackling climate change and its effects on the environment. The Paris Agreement states that nations must balance the amount of greenhouse gases they emit and absorb until 2050 to contribute to the mitigation of greenhouse gases and to support sustainable development. According to the agreement, each country must determine, plan and regularly report on its contributions. Thus, it is important for the countries to predict and analyze their net zero performances in 2050. Therefore, the aim of this study is to evaluate European Continent Countries' net zero performances at the targeted year.

The European Continent Countries that ratified the Paris Agreement are specified as decision making units (DMUs). Input and output indicators are specified as primary energy consumption, freshwater withdrawals, gross domestic product (GDP), carbon-dioxide (CO₂) and nitrous-oxide (N₂O) emissions. Data from 1980 to 2019 are obtained and forecasted using autoregressive integrated moving average (ARIMA) until 2050. Then, the countries are clustered based on the forecasts of primary energy consumption and freshwater withdrawals using k-means algorithm. As desirable and undesirable outputs arise simultaneously, the performances are computed using Pure Environmental Index (PEI) and Mixed Environmental Index (MEI) data envelopment analysis (DEA) models.

It is expected that by 2050, CO₂ emissions of seven countries remain constant, N₂O emissions of seven countries remain stable and five countries’ both CO₂ and N₂O emissions remain constant. While it can be seen as success that many countries are expected to at least stabilize one emission, the likelihood of achieving net zero targets diminishes unless countries undertake significant reductions in emissions. According to the results, in Cluster 1, Turkey ranks last, while France, Germany, Italy and Spain are efficient countries. In Cluster 2, the United Kingdom ranks at last, while Greece, Luxembourg, Malta and Sweden are efficient countries.

In the literature, generally, CO₂ emission is considered as greenhouse gas. Moreover, none of the studies measured the net-zero performance of the countries in 2050 employing analytical techniques. This study objects to investigate how well European Continent Countries can comply with the necessities of the Agreement. Besides CO₂ emission, N₂O emission is also considered and the data of European Continent Countries in 2050 are estimated using ARIMA. Then, countries are clustered using k-means algorithm. DEA models are employed to measure the performances of the countries. Finally, forecasts and models validations are performed and comprehensive analysis of the results is conducted.

This study aims to assess the efficiency of managing working capital in 1,388 Indian manufacturing firms from 2008 to 2019 and investigate the effects of firm-specific and macro-level determinants on working capital management (WCM) efficiency.

The current study accommodates a slack-based measure (SBM) in data envelopment analysis (DEA) for computing WCM efficiency. Further, we implement a panel data fixed-effects model that controls for heterogeneity across firms in determining the relationships of selected variables with WCM efficiency.

The results highlight that manufacturing firms operate at around 50 percent efficiency, which is constant throughout the study period. Furthermore, among the selected variables, yield, earnings, age, size, ability to create internal resources, interest rate and gross domestic product (GDP) significantly affect WCM efficiency.

Instead of the traditional models used for assessing efficiency, the SBM-DEA model is unit-invariant and monotone for slacks, implying that it can handle zero and negative data, which overcomes the incapability of prior DEA models. Hence, this provides accurate efficiency scores for robust analysis. Additionally, this paper provides a holistic working capital model recognizing firm-specific and macro-level determinants for a more explicit estimation of the relationship between WCM efficiency and the selected determinants.

This paper contributes to the field of public services’ performance measurement systems by proposing a benchmarking-based methodology that improves the effective use of big and open data in analyzing and evaluating efficiency, for supporting internal decision-making processes of public entities.

The proposed methodology uses data envelopment analysis in combination with a multivariate outlier detection algorithm—local outlier factor—to ensure the proper exploitation of the data available for efficiency evaluation in the presence of the multidimensional datasets with anomalous values that often characterize big and open data. An empirical implementation of the proposed methodology was conducted on waste management services provided in Italy.

The paper addresses the problem of misleading targets for entities that are erroneously deemed inefficient when applying data envelopment analysis to real-life datasets containing outliers. The proposed approach makes big and open data useful in evaluating relative efficiency, and it supports the development of performance-based strategies and policies by public entities from a data-driven public sector perspective.

Few empirical studies have explored how to make the use of big and open data more feasible for performance measurement systems in the public sector, addressing the challenges related to data quality and the need for analytical tools readily usable from a managerial perspective, given the poor diffusion of technical skills in public organizations. The paper fills this research gap by proposing a methodology that allows for exploiting the opportunities offered by big and open data for supporting internal decision-making processes within the public services context.