Search results

This paper is focused on developing an integrated algorithmic approach named as data envelopment analysis and multicriteria decision-making (DEA-MCDM) for ranking decision-making units (DMUs) based on cross-efficiency technique and subjective preference(s) of the decision maker.

Self-evaluation in data envelopment analysis (DEA) lacks in discrimination power among DMUs. To fix this, a cross-efficiency technique has been introduced that ranks DMUs based on peer-evaluation. Different cross-efficiency formulations such as aggressive and benevolent and neutral are available in the literature. The existing ranking approaches fail to incorporate subjective preference of “one” or “some” or “all” or “most” of the cross-efficiency evaluation formulations. Therefore, the integrated framework in this paper, based on DEA and multicriteria decision-making (MCDM), aims to present a ranking approach to incorporate different cross-efficiency formulations as well as subjective preference(s) of decision maker.

The proposed approach has an advantage that each of the aggressive, benevolent and neutral cross-efficiency formulations contribute to select the best alternative among the DMUs in a MCDM problem. Ordered weighted averaging (OWA) aggregation is applied to aggregate final cross-efficiencies and to achieve complete ranking of the DMUs. This new approach is further illustrated and compared with existing MCDM approaches like simple additive weighting (SAW) and Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) to prove its validity in real situations.

The choice of cross-efficiency formulation(s) as per subjective preference of the decision maker and different orness levels lead to different aggregated scores and thus ranking of the DMUs accordingly. The proposed ranking approach is highly useful in real applications like R and D projects, flexible manufacturing systems, electricity distribution sector, banking industry, labor assignment and the economic environmental performances for ranking and benchmarking.

To prove the practical applicability and robustness of the proposed integrated DEA-MCDM approach, it is applied to top twelve Indian banks in terms of three inputs and two outputs for the period 2018–2019. The findings of the study (1) ensure the impact of non-performing assets (NPAs) on the ranking of the selected banks and (2) are enormously valuable for the bank experts and policy makers to consider the impact of peer-evaluation and subjective preference(s) in formulating appropriate policies to improve performance and ranks of underperformed banks in competitive scenario.

To the best of the authors’ knowledge, this is the first study that has integrated both DEA and MCDM via OWA aggregation to present a ranking approach that can incorporate different cross-efficiency formulations and subjective preference(s) of the decision maker for ranking DMUs.

There has been a persistent debate on measures of efficiency and ranking procedures of higher education institutions (HEIs). Deriving absolute efficiency measures and their ranking provide a critical input for the society to choose the appropriate educational institute. The purpose of this paper is to evaluate the relative performance of institutions in management education in different locations in India and propose a holistic efficiency measurement which can be applied to HEIs in general.

This study uses dynamic data envelopment analysis (DDEA) as the primary methodology of analysis. Multiple measures of inputs and output have been defined to assess efficiency in institutions of management education. Some of the output variables used for measuring relative effectiveness are: the number of students placed, number of entrepreneurs, median CTC of placed students, total number of students passed, number of research publications, number of students and faculty who have participated in international exchange, input variables used, student intake, faculty profile, resource allocation on the development of student, faculty and staff, industry linkages, alumni network. The institutions under study are in three different locations in India, having distinct characteristics. The multiple measures of inputs and outputs defined have been used to measure efficiency, following which DDEA was used to rank the efficiency measures.

Various agencies use their framework to evaluate and rank HEIs; however, they are either subjective or less researched methodologies. The proposed method acts as a new researched and objective methodology for ranking of HEIs operating across regions with different societal, economic and political contexts. Efficiency in education is of high relevance today for various stakeholders such as students, parents, industry, policy-makers and government. An objective, such as the one proposed in this paper, would be helpful in satisfying the needs of various stakeholders. Furthermore, the government has policies of allocating funds, in case of public-funded institutions, based on efficiency levels in HEIs. The measure using DDEA suggested in this study provides a better measurement of efficiency.

This research is based on the extension of DDEA with slight modification to the denominator portion of efficiency calculation. The modification is accentuated by taking an industry benchmark or government benchmark. This may lead to slight difficulty in the appropriation of input parameters. Hence, selection of appropriate input and output parameters is the key limitation. To demonstrate capabilities of the proposed approach, this framework is implemented for performance evaluation of institutions of higher education in India. Some helpful policy-making and managerial insights are derived from the numerical results.

The uniqueness of this research is that it adds a well-researched methodology based on DDEA to measure efficiency and rank HEIs for effective assessment and benchmarking. The frameworks used so far have been either subjective or less researched methodologies.

Competition 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.

In 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”.

The 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.

Grey 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.

Compared 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.

This study aims to assign efficiency score and then ranking the Indian companies known for best practices to control carbon-emission in the environment. It is destined to benchmark one company for best performance on the basis of selected alternatives among its peer group companies.

The present study has used a hybrid model by applying data envelopment analysis (DEA)-technique for order performance by similarity to ideal solution (TOPSIS) to measure the efficiency and ranking of various decision units on the basis of specified variables.

The findings of DEA have given the best alternative or best decision-making unit (DMU) among the set of 25 DMUs considered for empirical testing. The DEA technique is used with TOPSIS, which is another popular multi-criteria decision model. The integrated DEA-TOPSIS model has helped to compute the efficiency score of all 25 DMUs of study and also provide a unique rank to each of the efficient unit identified with the help of DEA technique.

The findings of the study have provided Benchmark Company amongst the companies following best practices for saving energy and having best operating profits too. This benchmark business unit can be studied extensively by peer group companies to compare various parameters affecting their efficiency and profits both.

The findings of the study will promote the socially responsible practices by corporate citizens and adopt the practices to reduce their carbon footprints. It will also suggest to socially responsible investors to select the benchmark and most efficient companies for investment purpose.

The study is original in terms of measuring efficiency and ranking of companies known for best practices for controlling their carbon footprints and suggesting a benchmark company to its peer group. Also, the integrated approach of using DEA-TOPSIS for such type of studies also makes it distinctive from earlier work done in the related field.

The purpose of this paper is to present a tie-breaking procedure for computing performance efficiencies to improve benchmarking and performance evaluation process in a business situation.

The authors propose a unified approach based on data envelopment analysis (DEA) and technique for order of preference by similarity to ideal solution (TOPSIS), to overcome the difficulty of unique ranking in the prevalent benchmarking and performance evaluation processes such as DEA, Super efficiency DEA model, etc., under constant return to scale (CRS) assumption. This model is called as efficiency ranking method using DEA and TOPSIS (ERM-DT). In order to check the consistency of the approach, various input-output combinations (to calculate the efficiencies) have been illustrated. Further, the authors present a case of an Indian Bank to illustrate an application of the proposed approach.

The proposed approach, ERM-DT enables assign a unique rank to decision making units and provides a tie breaking procedure. Results obtained using the proposed approach are statistically compared with those obtained from the CRS DEA approach and super efficiency DEA approach using Friedman’s test.

The proposed model provides an efficiency ranking method based on a score obtained by considering the minimum distance from the best value and maximum distance from the worst value. The proposed methodology is capable of handling negative data and undesirable output variables. This approach is unit invariant and makes the calculations simple. The authors present an application to compute the efficiency of various branches of an Indian bank. The authors hope the proposed method can enhance the decision-making ability of the management in complex situations.

The authors propose an integrated DEA and TOPSIS framework for better benchmarking and performance evaluation. This approach provides a tie-breaking procedure for the efficiencies computed using CRS DEA approach. Ranks are assigned based on score obtained by considering the distance from the worst and the best solution. The proposed approach can be used with non-positive data points and undesirable output variables.

Competition in dynamic markets requires operational excellence and effective operations strategy. Hence, organizations have to evaluate their operational performance in comparison to their rivals. The purpose of this paper is to present an operational performance evaluation framework for analyzing and ranking the organizations based on the effectiveness of their operations strategies.

In this study three uncertain data envelopment analysis (DEA) models consist of fuzzy DEA (FDEA), imprecise DEA (IDEA) and Grey DEA are applied to assess the performance of organizations in terms of the operations. The applicability of the proposed framework is demonstrated through a case study at Fars province cement manufacturers of Iran.

The ranking results assist the mangers of service and manufacturing enterprises in finding out the current positions of their companies and the effectiveness of their operations strategies in comparison to their rivals. Accordingly they can make proper strategic and operational decisions to improve the performance of the operations.

Based on the previous literature, this is the first study which uses FDEA and IDEA methods in an operational performance evaluation problem. Furthermore this study proposes a novel framework for evaluating and ranking the operational performance of firms from the perspective of operations strategy.

Data envelopment analysis (DEA) is used to determine the relative efficiency of the top-ranked gynecology departments in the United States as designated by the U.S. News & World Report ranking. DEA is a linear programming base procedure used to determine the relative efficiency of operating units that have similar characteristics. Efficiency scores are calculated by comparing two different input sets to the performance of each gynecological department. Ranking based on DEA more completely and accurately represents gynecological departments. Further, DEA makes it possible to fairly compare specific departments. The new ranking coupled with the efficiency score accrued by each hospital will motivate and guide hospital administrators to improve the performance of hospital gynecology departments by better utilizing expensive resources.

This study focuses primarily on the business operating departments (hereinafter, DMUs) of a case telecom company (hereinafter, the Company) in the northern and eastern areas of Taiwan. In 2007, the Company finished the first stage of its reorganization by consolidating 14 DMUs into 12. In 2011, the Company completed the second stage of its reorganization by consolidating the 12 remaining DMUs into 8. This study intends to explore the effects of each stage of the Company’s reorganization on the efficiency and ranking of the various DMUs. The results show that the DMUs became more efficient after each stage of the Company’s reorganization. Moreover, the efficiency and ranking of the new DMUs, A6, A7, and B7 increased post-consolidation. This suggests that both the first and second stages of the reorganization were necessary. The findings of this study could help the Company and other telecom companies to design strategies for the future consolidation of other units, and thereby maintain their competitiveness and continued growth.

The purpose of this paper is to present an objective decision-making framework and conduct a benchmarking study in the air cargo industry.

The decision-making framework and benchmarking methodology evaluates the aircraft value for money (VfM) as a benefit-to-cost ratio calculated adopting a measure of relative efficiency. This efficiency score is measured as a comprehensive efficiency index obtained by combining several efficiency scores calculated by implementing four data envelopment analysis (DEA) models.

The framework is used to carry on a benchmarking study in the air cargo industry on a sample of 27 airplanes. The average VfM is 67.04 percent, with measurements between 39.96 and 116.03 percent. Only three airplanes achieve full VfM and behave as benchmarks to the remaining airplanes. Boeing B727-200 is a broad player in the market. Some old cargo models (DC 9-30F) deliver the same amount of VfM as more recent aircraft models (i.e. MD-11F and A300-600F).

The decision-making framework and benchmarking methodology can usefully support managers to make sound decisions and plans. Even though DEA generates attributes weights to different alternatives that are independent of the buyer preferences, the framework flexibility allows introducing a weighting scheme to take into account the managers preferences for certain aircraft performance/functional features. It can easily include new functional/performance measurements and adapt the VfM measurement to the particular economic context, strategy, and business model of the airlines, or be transferred to different industries.

The framework combines technical, functional performance, and economic cost measurements to get a unique efficiency index to evaluate the airplane VfM.

The purpose of this paper is to propose an innovative algorithm for ranking suppliers in the presence of volume discount offers, with regard to various criteria, based on super‐efficiency analysis.

This paper introduces an innovative approach, which is based on super‐efficiency analysis (one of the data envelopment analysis models).

To rank the suppliers in the conditions that they offer volume discounts, an algorithm was introduced.

The results of this paper can be applied from both a buyer's and supplier's perspective. The buyer can use it as a tool in ranking the suppliers. The supplier can use these results from a marketing perspective. A specific supplier who achieves a high mean score, when compared to the other suppliers, can use these results for promoting its product. On the other hand, if a particular supplier is poorly performing, then the supplier can use the analysis for benchmarking purposes. This result may mean that the supplier must provide better performance levels at the same input.

To the best of the author's knowledge, there is no comprehensive and feasible model that deals with supplier ranking by super‐efficiency analysis in the presence of volume discount offers.