Common Set Of Weights Research Articles

Enhancing group efficiency through two-stage data envelopment analysis with a modified common set of weights approach: an application in banking

This paper empirically evaluates group efficiency in the banking sector by applying an extended Common Set of Weights (CSWs) approach within a two-stage Data Envelopment Analysis (DEA) framework. Our study assesses efficiency across multiple production stages and adapts CSWs to capture the intermediate processes inherent in banking operations. We provide a comprehensive ranking of bank branches within seven prominent banking groups in Guilan province, Iran. By applying our modified methodology to real-world data, we demonstrate its practical utility in delivering precise efficiency assessments and actionable insights for enhancing operational performance. Our findings highlight the superiority of the extended CSWs methodology over traditional DEA models, offering a more accurate and fair evaluation of group efficiency in the complex and interdependent context of banking processes.

Matrix-based network data envelopment analysis: A common set of weights approach

Performance measurement of decision-making units (DMUs) with network structure is one of the main challenges in data envelopment analysis (DEA) field. The main purpose of this paper is to propose a novel network data envelopment analysis (NDEA) approach based on matrix of efficiency, common set of weights (CSW), multi-objective programming (MOP), and goal programming (GP) technique for performance measurement of peer DMUs in two-stage network structure. The advantages of the proposed NDEA approach can be summarized as follows: comparing all DMUs and sub-DMUs on the same base, considering all internal structures and relations and capability to extending this for all network structures, linearity of the proposed models, unique efficiency decomposing without any need to consider multiplicative, additive or leader-follower relations between overall and stages efficiency. To illustrate the usefulness and applicability of the proposed approach we applied it to a real application of non-life insurance companies in Taiwan.

The Evaluation of Climate Change Competitiveness via DEA Models and Shannon Entropy: EU Regions.

The purpose of this paper is to assess the efficiency of climate change competitiveness via a case study on EU regions by using the data envelopment analysis (DEA) model and Shannon entropy. First, on the same premise as similar composite indicators, we develop a DEA model to assess the relative performance of the regions in climate change competitiveness. Then, we extend our calculations with a DEA-like model and Shannon entropy to derive global estimates of a new competitiveness index by using common weights. Results show that the proposed DEA-Entropy model enables the construction of a regional climate change competitiveness index among all regions via a set of common weights. The proposed model's common weight structure demonstrates more discriminative power compared to the weights obtained through pure DEA or DEA-like methods. In order to validate the proposed DEA-Entropy model, it was applied to 120 EU regions. The results are meaningful for the regions to improve their competitiveness.

Exploring supply chain and regional resilience through the analysis of the transport dimension

Despite the key role that global supply chains (GSC), and more specifically, the transport and logistics function, might play in the performance of regions, there is a lack of analytical tools that turn the data generated by supply chain operations into quantitative metrics allowing policymakers to identify early signals of potential vulnerabilities and assess how resilient the GSC are at regional level. In an effort to fill this gap, we present a Transportation Resilience Index (TRI) that permits evaluating how transport chains affect supply chain resilience and, consequently, regional resilience. Our index, based on publicly available data –statistics on foreign trade flows provided by the Spanish Customs, the World Bank’ Logistics Performance Index and the UNCTAD’ Bilateral Liner Shipping Connectivity Index − differentiates between the determinants over which the company has direct decision-making control, and those linked to the selection of the supply market (market and macro dimensions). The TRI is applied to the Spanish regions (NUTS2) by using the Benefit-of-the-Doubt (BoD) approach to Data Envelopment Analysis (DEA) and applying a Common Set of Weights (CSW) for the selection of the weights to the index components. In our specific analysis, the market and macroeconomic components account for 84% of the total weight of the TRI. Despite the lower relative weight of the company dimension, the high degree of homogeneity in the market and macroeconomic scores means the company dimension plays a key role in the relative resilience of the Spanish regions.

Performance Evaluation of Hospitals under Data Uncertainty: An Uncertain Common-Weights Data Envelopment Analysis.

In the context of healthcare systems, the performance evaluation of hospitals plays a crucial role in assessing the quality of healthcare systems and facilitating informed decision-making processes. However, the presence of data uncertainty poses significant challenges to accurate performance measurement. This paper presents a novel uncertain common-weights data envelopment analysis (UCWDEA) approach for evaluating the performance of hospitals under uncertain environments. The proposed UCWDEA approach addresses the limitations of traditional data envelopment analysis (DEA) models by incorporating the uncertainty theory (UT) to model the inherent uncertainty in input and output data. Also, by utilizing a common set of weights (CSW) technique, the UCWDEA method provides a more robust and reliable assessment of hospital performance. The main advantages of the proposed UCWDEA approach can be succinctly summarized as follows. Firstly, it allows for the comparison of all hospitals on a consistent basis to calculate a realistic efficiency score, rather than an overly optimistic efficiency score. Secondly, the uncertain common-weights DEA approach exhibits linearity, enhancing its applicability. Thirdly, it possesses the capability to extend its utility under various other prevalent uncertainty distributions. Moreover, it enhances the discriminatory power of results, facilitates the ranking of hospitals in the presence of data uncertainty, and aids in identifying the sensitivity and stability levels of hospitals towards data uncertainty. Notably, in order to showcase the pragmatic application and efficacy of the uncertain common-weights DEA model, a genuine dataset has been utilized to evaluate the efficiency of 20 public hospitals in Tehran, all of which are affiliated with the Iran University of Medical Sciences. The results of the experiment demonstrate the efficacy of the UCWDEA approach in assessing and ranking hospitals amidst uncertain conditions. In summary, the research outcomes can offer policymakers valuable insights regarding hospital performance amidst data uncertainty. Additionally, it can provide practical recommendations on optimizing resource allocation, benchmarking performance, and formulating effective policies to augment the overall efficiency and effectiveness of healthcare services.

A novel model for merger analysis and target setting: A CSW-Inverse DEA approach

The inverse data envelopment analysis (DEA) is an advanced complementary method for efficiency analysis using the classical DEA approach. One of the inverse DEA (InvDEA) method applications is the mergers and acquisitions problem. It can be used for analyzing any under evaluation mergers and acquisitions. The current article is the first attempt to propose a novel inverse structure for the DEA model using the multiplier forms. This eventuates the possibility of incorporating decision maker preferences within the merger analysis.Moreover, compared with the existing models in the literature, the proposed novel models are capable of analyzing multiple merger scenarios simultaneously in a single method based on the common set of weights (CSW) rather than a series of models for studying multiple scenarios of mergers and acquisitions. Practically, this property enables decision-makers to consider and analyze multiple mergers and find possible potentials at the same time. The applicability of the proposed model is investigated by using a real-world dataset in banking.

Supply Chain Resilience: A Common Weights Efficiency Analysis with Non-discretionary and Non-controllable Inputs

Supply chain resilience (SCRes) as the supply chain network's (SCN) capacity is essential to recover from disruptions. The economic, environmental, and geopolitical regional characteristics of the Pacific region present many challenges and opportunities for building supply chain resilience. This study aims to measure the resilience of supply chains (SCs) considering the characteristics of the network under which they operate. In this study, we proposed a new common set of weights (CSW) model in data envelopment analysis to evaluate the resilience of SCNs. Many external variables beyond decision-makers’ direct control impact SC operations and their resilience. Therefore, the proposed CSW model formulates the non-discretionary and non-controllable inputs in measuring the resilience of SCNs and provides a complete ranking with a higher discrimination power. To improve SCRes, SC managers are recommended to enhance the clustering coefficient and node degree of their SCN by establishing more connections with other SCNs in order to pinpoint the essential capabilities that companies should prioritise in order to develop a stronger and more adaptable SC in the post-COVID-19 pandemic.

Green supply chains and performance evaluation: A multiplier network analytics model with common set of weights

Supply chains (SCs) are considered complicated systems consisting of many interactions, components, and flows. Green SCs have forced policymakers and managers to pay more attention to the environment rather than only focus on the economic aspect. Performance evaluation of complex systems, including green SCs is a critical issue in organizations. Most of the current models are unable to evaluate the performance of green SCs in an integrated framework. To address this issue in organizations, in this paper, our objective is to propose a powerful network data envelopment analysis (NDEA) to evaluate green SCs. The NDEA method is considered an effective approach to deal with complex settings including SCs. Our multiplier NDEA model for cost efficiency evaluation is presented based on the cost production possibility set (PPS). A common set of weight model based on cost PPS is formulated. The proposed model maximizes the cost-efficiency of all decision making units (DMUs), simultaneously. In our model, the common set of weights and cost efficiency of each stage and the overall cost are obtained. Finally, the developed model for assessing the sustainably of SCs in the wire and cable industry is applied. The results show how well our developed model, based on common set of weights, can evaluate the performance of green SCs compared to the classic models.

Predicting the performance of MSMEs: a hybrid DEA-machine learning approach

AbstractMicro, small and medium enterprises (MSMEs) dominate the business landscape and create more than half of employment worldwide. How we can apply big data analytical tools such as machine learning to examine the performance of MSMEs has become an important question to provide quicker results and recommend better and more reliable solutions that improve performance. This paper proposes a novel method for estimating a common set of weights (CSW) based on regression analysis for data envelopment analysis (DEA) as an important analytical and operational research technique, which (i) allows for measurement evaluations and ranking comparisons of the MSMEs, and (ii) helps overcome the time-consuming non-convexity issues of other CSW DEA methodologies. Our hybrid approach used several econometric and machine learning techniques (such as Tobit, least absolute shrinkage and selection operator, and Random Forest regression) to empirically explain and predict the performance of more than 5400 Vietnamese MSMEs (2010‒2016), and showed that the machine learning techniques are more efficient and accurate than the econometric ones. Our study, therefore, sheds new light on the two-stage DEA literature, especially in terms of predicting performance in the era of big data to strengthen the role of analytics in business and management.

Sustainable Supply Chain Evaluation with Shared Resources and Shared Feedbacks: A Common Set of Weights Slack Based Model

Data envelopment analysis (DEA) has been recently employed for performance and efficiency evaluation of decision-making units (DMUs) with multiple inputs and outputs. Complex internal relations within a DMU required more accurate DEA models than existing classic DEA. In real world applications, shared resources, among stages of a supply chain, is of great interest for managers and decision makers. Unfair allotment of shared factors can render the assessment invalid. In this study, an innovative DEA model is formulated for efficiency evaluation of a supply chain with shared factors. In the first step, a linear DEA model is presented in the multiplier form, based on the slack-based measure for efficiency assessment, in order to obtain the optimal proportion of the shared resources and shared feedbacks which are dual-role factors are presented. Then, the aggregate efficiency is decomposed into its stage efficiencies. Next, the presented model is extended for deriving the Common Set of Weights (CSW) for efficiency evaluation of the entire chain and correspondence stages. The case of 20 sustainable supply chains in the oil industry is considered with the developed DEA approach.

Common weights analysis of renewable energy efficiency of OECD countries

Arising from the recent COP26 climate change conference, many governments have emphasised carbon neutrality targets for environmental sustainability by increasing renewable energy efficiency. This study develops a common set of weights (CSW) model for the additive model in data envelopment analysis using goal programming to analyse the energy efficiency of the Organisation for Economic Co-operation and Development (OECD) countries. The CSW model, which has better discrimination power, places Iceland as the most renewable energy-efficient member country in the OECD, followed by Luxemburg and Norway. Our findings suggest that OECD countries should increase their renewable energy consumption and reduce municipal waste and CO2 emissions. Investment subsidies should be provided to support the development and adoption of energy-efficient technologies and promote awareness in the community and industry to improve the efficiency of renewable energy to combat climate change.

Fuzzy efficiency evaluation in relational network data envelopment analysis: application in gas refineries

In contrast to classical data envelopment analysis (DEA), network DEA has attention to the internal structure of a production system and reveals the relationship between the efficiency of system and efficiencies of the processes. However, the flexibility of weights and the need for crisp input and output data in the evaluation process are two major shortcomings of classical network DEA models. This paper presents a common weights approach for a relational network DEA model in a fuzzy environment to measure the efficiencies of the system and the component processes. The proposed approach first finds upper bounds on input and output weights for a given cut level and then it determines a common set of weights (CSW) for all decision-making units (DMUs). Hence, the fuzzy efficiencies of all processes and systems for all DMUs are obtained based on the resulting CSW. The developed fuzzy relational network DEA and the proposed common weights approach are illustrated with a numerical example. The obtained results confirm that the fuzzy data affects over the efficiency scores and complete ranking of DMUs. The applicability of the proposed network model is illustrated by performance evaluation of gas refineries in Iran.

Resource allocation for supply chains based on Pareto-optimal two-stage cross-efficiency model

The contradiction between the scarcity of common resources and the infinity of human demand for these resources has a significant impact on social development. Therefore, resource allocation can make the best use of limited resources in economic activities. Taking the two-stage supply chain where the outputs from the upstream supplier are taken as the inputs for the downstream manufacturer as an example, this paper applies the cross-efficiency model to comprehensively evaluate the efficiency scores of supply chains in the process of resource allocation and explores the relationship between the cross-efficiency of the supply chain and that of two enterprises within this supply chain. Furthermore, the self-interested behavior of enterprises is taken as the Pareto improvement principle to propose a Pareto-optimal two-stage cross-efficiency model, and this model can be used to optimally allocate the limited resources among two-stage supply chains. A common set of weights is determined to make all supply chains DEA efficient. Finally, the proposed model is illustrated to be feasible and effective through a practical application of 27 Iranian resin production companies.

New Approach in Fixed Resource Allocation and Target Setting Using Data Envelopment Analysis with Common Set of Weights

One of the mathematical programming techniques is data envelopment analysis (DEA), which is used for evaluating the efficiency of a set of similar decision-making units (DMUs). Fixed resource allocation and target setting with the help of DEA is a subject that has gained much attention from researchers. A new model was proposed by determining a common set of weights (CSW). All DMUs were involved with the aim of achieving higher efficiency in every DMU after the procedure. The minimum resources and targets allocated to each DMU were commensurate to the efficiency of that DMU and the share of DMU in the input resources and the output productions. To examine the proposed method, other methods in the DEA literature were examined as well, and then, the efficiency of the method was demonstrated through a numerical example.

Assessing sustainability of supply chains by fuzzy Malmquist network data envelopment analysis: Incorporating double frontier and common set of weights

Sustainable supply chain management (SSCM) is one of the most significant topics. Today, assessing sustainability and choosing the best supply chain is an important goal for companies. One of the techniques that can be used for evaluating the sustainability of supply chains is data envelopment analysis (DEA). This study contributes to the knowledge of sustainability assessment by (i) proposing a double frontier network DEA (NDEA) model with common set of weights (CSW), (ii) dealing with fuzzy data to assess sustainability, and (iii) deriving a CSW model for Malmquist productivity index (MPI). The CSW models consider the same set of weights for all decision making units (DMUs) so that all DMUs get the highest sustainability score. The fuzzy MPI (FMPI) is used to assess the sustainability of supply chains with the trapezoidal fuzzy number. The FMPI reflects the productivity change over time. The proposed model can assess the sustainability of supply chains and takes into account different confidence levels in two periods and can fully rank DMUs. Our model can measure the sustainability of supply chains via fuzzy MPI. Using our proposed approach, the decision-makers can determine the optimistic, pessimistic, and double frontier sustainability of supply chains. To prove the applicability of the proposed model, the sustainability of customs centers is assessed.

Eco-innovation analysis of OECD countries with common weight analysis in data envelopment analysis

PurposeDespite unanimity in the literature that eco-innovation (EI) leads to sustainable development, evidence remains limited on measuring EI efficiency with the Malmquist productivity index (MPI). In conventional data envelopment analysis (DEA) models, decision-making units (DMUs) are inclined to assign more favorable weights, even zero, to the inputs and outputs to maximize their own efficiency. This paper aims to overcome this shortcoming by developing a common set of weights (CSW).Design/methodology/approachUsing goal programming, this study develops a CSW model to evaluate the EI efficiency of the organization for economic co-operation and development (OECD) countries and track their changes with MPI during 2010–2018.FindingsAchieving a complete ranking of DMUs, findings show the higher discrimination power of the proposed CSW compared with the original DEA models. Furthermore, results reveal that Iceland, Latvia and Luxembourg are the only OECD countries that have incessantly improved their EI productivity (MPI > 1) from 2010 to 2018. On the other hand, Japan is the OECD country that has experienced the highest yearly EI efficiency during 2010–2018. This paper also found that Iceland has the highest MPI over 2010–2018.Practical implicationsMore investment in environmental research and development (R&D) projects instead of generic R&D enables OECD members to realize more opportunities for sustainable development through minimizing energy use and environmental pollution in any form of waste and greenhouse gas emissions.Originality/valueIn addition to developing a novel common weights model for DEA-MPI to measure and evaluate the EI of OECD countries, this paper develops a CSW model by including the undesirable outputs for EI analysis.

Eco-innovation analysis: A data envelopment analysis methodology

The examination of financial development and environmental effect has driven inquiry into creating assessment models on environmental and economic changes, particularly on eco-innovation goods. Here, a new approach is proposed to find the common set of weights (CSWs) in data envelopment analysis (DEA) to examine the eco-innovation in the presence of undesirable factors. For this purpose, first, the current models are reviewed and criticized, and their shortcomings are clarified. Then, a simple way is proposed to find CSWs in DEA. We show the proposed approach leads to a better solution compared to current methods in the literature. Finally, the proposed model is applied to the eco-innovation analysis of OECD countries. Our findings show that the Czech Republic has the highest rank in eco-innovation. • A novel common weights model in DEA is developed. • Eco-innovation of OECD countries is assessed. • Undesirable outputs are modeled in the proposed method.

A novel approach for determining common weights in two division network DEA: a case study of Science and Technology Parks in Turkey

ABSTRACT Data Envelopment Analysis is a popular tool for assessing the relative efficiency of units, treated as a whole unit, without considering the internal structure of decision-making units. In contrast to the conventional models, Network DEA models consider the internal operations of units. In both models, the most favourable weights of the input–output factors for each unit are used. One of the main pitfalls of these models is that they require a set of n different vectors of these weights for the units, thus the factors may not have the same importance for each unit. To overcome this drawback and avoid ignoring some factors, several methods have been proposed to determine a common set of weights (CSWs) of the factors. However, none of the proposed approaches can determine the CSWs in network DEA models. This paper contributes to the existing literature by proposing a novel approach for determining the CSWs based on Entropy Method. Finally, the proposed approach is applied to demonstrate their applicability in both numerical and illustration examples. Moreover, a case of assessment of Science and Technology Parks in Turkey was used for the purpose of illustration.

Prevailing narratives versus reality of a small and medium town decline in a CEE country

It is a common narrative that small and medium towns of formerly socialist countries of Central and Eastern Europe (CEE) are suffering a substantial decline due to better offerings in bigger metropolitan areas. Focused on Slovakia, this paper presents a methodology for assessing the future viability of small and medium towns, considering different socio-economic, infrastructural, and demographic aspects capturing their desirability. The proposed methodology aggregates different aspects into attributes by means of a data envelopment analysis model with common set of weights without explicit inputs (CSW DEA-WEI), and then converts these attributes into scores in the fashion of a multiple criteria decision analysis (MCDA). The methodology offers a new perspective for rating the viability and sustainability of small and medium towns in the CEE region, and is amenable to adaptations elsewhere. Contrary to popular belief, it is found that small and medium towns remain attractive for life.

Fixed cost allocation considering the input-output scale based on DEA approach

As independent and decentralized entities often share common resources, it is necessary to allocate common costs across these subunits. When there is a lack of specific price information, data envelopment analysis (DEA) approaches have been successfully used to resolve fixed cost allocation problems. To this end, this paper develops a fixed cost allocation scheme based on decision-making unit (DMU) input–output scales that considers both the input consumption and output production scales. The allocation scheme is derived from a one-dimensional case to a general case, in which the input–output scale is calculated based on a common set of weights (CSW) determined using a DEA approach. The proposed method is demonstrated using a classical numerical example from the prior literature and an empirical application that has been applied in previous studies, from which it is found that the fixed cost allocation scheme is more acceptable to the involved DMUs.