Measuring paper industry's ecological performance in an imprecise and vague scenario: a fuzzy DEA-based analytical framework

Jauhar, Sunil Kumar; Singh, Natthan; Rajeev, A.; Pant, Millie

doi:10.1108/bij-06-2021-0319

Cited by 5 publications

(2 citation statements)

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“…Owing to the existence of uncertainty, DEA sometimes faces the situation of imprecise data, especially when a set of DMUs contains missing data, judgment data, forecasting data or ordinal preference information (Toloo and Ertay, 2014). Dealing with imprecise data is a perpetual challenge in DEA that can be addressed presenting interval data (Yu and Hou, 2016), stochastic data (Wanke et al, 2022), fuzzy data (Jauhar et al, 2022), rough data (Mei and Chen, 2021), grey data (Wang et al, 2022), robust DEA (Salahi et al, 2021) and so on.…”

Section: Grey Clustering and Grey Ranking Of Dmusmentioning

confidence: 99%

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

Karimi,

Ahmadian

2023

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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.

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Section: Grey Clustering and Grey Ranking Of Dmusmentioning

confidence: 99%

“…Dealing with imprecise data is a perpetual challenge in DEA that can be addressed presenting interval data (Yu and Hou, 2016), stochastic data (Wanke et al. , 2022), fuzzy data (Jauhar et al. , 2022), rough data (Mei and Chen, 2021), grey data (Wang et al.…”

Section: Introductionmentioning

confidence: 99%

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

Karimi,

Ahmadian

2023

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A deep learning-based approach for performance assessment and prediction: A case study of pulp and paper industries

et al. 2022

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The pulp and paper industry is critical to global industrial and economic development. Recently, India's pulp and paper industries have been facing severe competitive challenges. The challenges have impaired the environmental performance and resulted in the closure of several operations. Assessment and prediction of the performance of the Indian pulp and paper industry using various parameters is a critical task for researchers. This study proposes a framework for performance assessment and prediction based on Data Envelopment Analysis (DEA), Artificial Neural Networks, and Deep Learning (DL) to assist industry administration and decision-making. We presented a case study based on eight industries to demonstrate the methodology's applicability. This study analyses and predicts industry performance based on sample data observations over 30 years. The result suggests the DEA-DL-based efficiency prediction has an overall MSE of 0.08 compared with the actual efficiency. Furthermore, the efficiency rankings are compared between the three techniques. The results suggest that the integrated DEA-DL method is primarily accurate in most scenarios with the actual values. The findings of this study provide a comprehensive analysis of environmental performance for policymakers.

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