Experimental comparison of results provided by ranking methods in Data Envelopment Analysis

Labijak-Kowalska, Anna; Kadziński, Miłosz

doi:10.1016/j.eswa.2021.114739

Cited by 15 publications

(6 citation statements)

References 73 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Moreover, we compute the expected distance Ed to the efficient unit and expected rank ER for each DMU [28]. This is performed by averaging the distances or ranks observed for all samples.…”

Section: Simulation-based Methodsmentioning

confidence: 99%

See 1 more Smart Citation

Robust Additive Value-Based Efficiency Analysis with a Hierarchical Structure of Inputs and Outputs

2023

Self Cite

View full text Add to dashboard Cite

We introduce a novel methodological framework based on additive value-based efficiency analysis. It considers inputs and outputs organized in a hierarchical structure. Such an approach allows us to decompose the problem into manageable pieces and determine the analyzed units’ strengths and weaknesses. We provide robust outcomes by analyzing all feasible weight vectors at different hierarchy levels. The analysis concerns three complementary points of view: distances to the efficient unit, ranks, and pairwise preference relations. For each of them, we determine the exact extreme results and the distribution of probabilistic results. We apply the proposed method to a case study concerning the performance of healthcare systems in sixteen Polish voivodeships (provinces). We discuss the results based on the entire set of factors (the root of the hierarchy) and three subcategories. They concern health improvement of inhabitants, efficient financial management, and consumer satisfaction. Finally, we show the practical conclusions that can be derived from the hierarchical decomposition of the problem and robustness analysis.

show abstract

“…Moreover, we compute the expected distance Ed to the efficient unit and expected rank ER for each DMU [28]. This is performed by averaging the distances or ranks observed for all samples.…”

Section: Simulation-based Methodsmentioning

confidence: 99%

“…The rankings generated by all four procedures for the level of inhabitants' health improvement are provided in Table 12. To quantify the agreement level of such rankings for all hierarchy nodes, we used Kendall's τ coefficient [28,32]. Its values are shown in Table 13.…”

Section: Complete Efficiency Rankings Of Voivodeshipsmentioning

confidence: 99%

Robust Additive Value-Based Efficiency Analysis with a Hierarchical Structure of Inputs and Outputs

2023

Self Cite

View full text Add to dashboard Cite

show abstract

“…The selection of the most suitable supplier can be achieved according to three processes: (a) total ranking, (b) outranking of the best suppliers, and (c) the full breakdown of suppliers into groups. The methods proposed in the literature mainly focus on the first process [12], [20], [21] and [23]. Articles that focus on segmentation tend to use methods that can be classified as the first or the second process (e.g., [2], [26] and [30]).…”

Section: Literature Reviewmentioning

confidence: 99%

Tiered Data Envelopment as method for clustering suppliers

Dobos

2023

Croat. oper. res. rev. (Online)

View full text Add to dashboard Cite

Effective supplier management requires continuous monitoring of the performance and capabilities of one’s supplier base. Although the literature often focuses on ranking, it has lately become increasingly important to group suppliers according to their capabilities. In this paper, we compare two clustering methods. The application of Cluster Analysis (CA) has been widely discussed in the literature. Tiered Data Envelopment Analysis (TDEA) is also well-known in the decision-making literature, but is nonetheless seldom employed in supplier evaluation. CA is only suitable for group formation on a nominal scale, whereas the TDEA method during group formation allows the groups to be formed on an ordinal scale. TDEA may therefore prove to be the more suitable method for ordinal group formation. This article attempts to bridge a research gap, which arises since the two methods are infrequently employed in supplier selection. A numerical example is used to compare their application.

show abstract

“…, 2021). Data Envelopment Analysis (DEA) is a subset of operation research and management science, designed to measure the relative efficiency of Decision Making Units (DMUs) (Labijak-Kowalska and Kadzinski, 2021). In traditional DEA models, DMUs are treated as black boxes, with just their inputs and outputs taken into account in the assessment.…”

Section: Introductionmentioning

confidence: 99%

“…Different methods of assessing performance have been developed to review the organization's performance and ranking (Phung et al, 2020;Fukuyama et al, 2020;Wasiaturrahma et al, 2020;Habib and Shahwan, 2020;Puri and Verma, 2020;Oztaysi et al, 2020;Ya et al, 2021;Yadav et al, 2021;Sun et al, 2021). Data Envelopment Analysis (DEA) is a subset of operation research and management science, designed to measure the relative efficiency of Decision Making Units (DMUs) (Labijak-Kowalska and Kadzinski, 2021). In traditional DEA models, DMUs are treated as black boxes, with just their inputs and outputs taken into account in the assessment.…”

Section: Introductionmentioning

confidence: 99%

Grey clustering and grey ranking of bank branches based on grey efficiency

Karimi,

Ahmadian

2023

View full text Add to dashboard Cite

PurposeCompetition in the banking sector is more complex than in the past, and survival has become more difficult than before. The purpose of this paper is to propose a grey methodology for evaluating, clustering and ranking the performance of bank branches with imprecise and uncertain data in order to determine the relative status of each branch.Design/methodology/approachIn this study, the two-stage data envelopment analysis model with grey data is applied to assess the efficiency of bank branches in terms of operations. The result of grey two-stage data envelopment analysis model is a grey number as efficiency value of each branch. In the following, the branches are classified into three grey categories of performance by grey clustering method, and the complete grey ranking of branches are performed using “minimax regret-based approach” and “whitening value rating”.FindingsThe results show that after grey clustering of 22 branches based on grey efficiency value obtained from the grey two-stage DEA model, 6 branches are assigned to “excellent” class, 4 branches to “good” class and 12 branches to “poor” class. Moreover, the results of MRA and whitening value rating models are integrated, and a complete ranking of 22 branches are presented.Practical implicationsGrey clustering of branches based on grey efficiency value can facilitate planning and policy-making for branches so that there is no need to plan separately for each branch. The grey ranking helps the branches find their current position compared to other branches, and the results can be a dashboard to find the best practices for benchmarking.Originality/valueCompared with traditional DEA methods which use deterministic data and consider decision-making units as black boxes, in this research, a grey two-stage DEA model is proposed to evaluate the efficiency of bank branches. Furthermore, grey clustering and grey ranking of efficiency values are used as a novel solution for improving the accuracy of grey two-stage DEA results.

show abstract

Experimental comparison of results provided by ranking methods in Data Envelopment Analysis

Cited by 15 publications

References 73 publications

Robust Additive Value-Based Efficiency Analysis with a Hierarchical Structure of Inputs and Outputs

Robust Additive Value-Based Efficiency Analysis with a Hierarchical Structure of Inputs and Outputs

Tiered Data Envelopment as method for clustering suppliers

Grey clustering and grey ranking of bank branches based on grey efficiency

Contact Info

Product

Resources

About