The benefit of performance indicators in efficiency assessment using data envelopment analysis

Data envelopment analysis (DEA) is a method for measuring efficiency of peer decision-making units (DMUs). Conventional DEA evaluates the performance of each DMU using a set of most favourable weights. As a result, traditional DEA models can be considered methods for the analysis of the best relative efficiency or analysis of the optimistic efficiency. DEA efficient DMUs obtained from conventional DEA models create an efficient production frontier. Traditional DEA can be used to identify units with good performance in the most desirable scenarios. There is a similar approach that evaluates the performance indicators of each DMU using a set of most unfavourable weights. Accordingly, such models can be considered models for analysing the worst relative efficiency or pessimistic efficiency. This approach uses the inefficient production frontier for determining the worst relative efficiency that can be assigned to each DMU. DMUs lying on the inefficient production frontier are referred to as DEA inefficient while those neither on the efficient frontier nor on the inefficient frontier are declared DEA inefficient. It can be argued that both relative efficiencies should be considered simultaneously and any approach with only one of them would be biased. This paper proposed the integration of both efficiencies as an interval so that the overall performance score would belong to this interval. It was shown that efficiency interval provided more information than either of the two efficiencies, which was illustrated using two numerical examples.

PurposeThe primary aim of this research is to conduct a comprehensive investigation into the essential elements of Industry 4.0 implementation within Indian Micro, Small and Medium Enterprises (MSMEs). Acknowledging the MSME sector as a crucial contributor to the Indian economy and industrial development, the study delves into the assessment of MSMEs based on Industry 4.0 components. Additionally, it explores the profound impact of these components on various performance factors, including organizational performance, sustainability performance and human-related aspects. The paper further ranks these identified components based on their significance within the MSME sector.Design/methodology/approachEmploying a combination of methodological approaches, the research utilizes the Best and Worst Method (BWM), Data Envelopment Analysis (DEA) and calculates the Maturity Index for Industry 4.0 components. The BWM, a recognized multi-criteria decision-making technique, is initially applied to determine the weights and rankings of the identified components. Furthermore, the study evaluates 30 MSMEs, spanning manufacturing and service sectors, through the DEA approach. Industry 4.0 components are treated as inputs, and performance factors serve as outputs. Data for the analysis are collected through questionnaires distributed to the selected MSMEs. Lastly, the Maturity Index for MSMEs is also calculated.FindingsFrom the result of the BWM method “assistive manufacturing” was found to be a highly weighted key component of Industry 4.0. From the DEA analysis out of 30 MSMEs 13 SMEs were highlighted as being efficient, whereas 17 MSMEs were judged to be inefficient. Furthermore, from the maturity index calculation, overall Maturity Index was determined to be 3.33 which shows that Industry 4.0 is in its initial stage of implementation, but it has gained pace in its implementation.Practical implicationsThe research contributes to practical implications by offering a more accurate assessment of the state of Industry 4.0 implementation within MSMEs. The introduced maturity index proves instrumental in pinpointing key components that have received inadequate attention. This information is crucial for MSME managers and policymakers, guiding them in allocating resources effectively, addressing areas requiring attention and facilitating progress in the implementation of Industry 4.0. The study serves as a valuable tool for MSMEs to enhance their overall operational efficiency.Originality/valueThe research’s originality lies in its application of a comprehensive approach, combining BWM, DEA and the introduction of a maturity index for Industry 4.0 components in the MSME context. By employing these methodologies, the study not only identifies influential components but also provides a nuanced understanding of their relative significance. The research contributes significantly to the broader understanding of Industry 4.0 adoption, particularly, in the vital MSME sector within the Indian context. The findings are valuable for researchers, practitioners and policymakers seeking insights into improving the efficiency and effectiveness of MSMEs in the era of Industry 4.0.

This paper examines the strategic fit of mergers and acquisitions (M&A) in Mozambican banks, using a two‐stage data envelopment analysis (DEA) approach to compute the impact of contextual variables on efficiency scores and returns to scale of the resulting virtual merged banks. In an ‘M&A DEA’ model, different pairs of bidder and target companies are considered. In the first stage an M&A DEA model is used. Simplex regression is adopted in the second stage. The results reveal that ownership (public or foreign) impacts virtual efficiency levels. However, the findings also show that care must be exercised with M&A because the resultant banking organization could be oversized when foreign ownership is predominant, especially when public ownership is low. Given the relative sizes of the markets in terms of productive resources, M&A involving Mozambican banks can easily lead to decreasing returns to scale. Thus, a greater emphasis should be given to merging purely public banks with foreign ones and vice versa.

Data envelopment analysis (DEA) is a mathematical method based on production theory and the principles of linear programming. It enables one to assess how efficiently a firm, organization, agency, or such other unit uses the resources available (inputs) to generate a set of outputs relative to other units in the data set. Recent papers by different writers present different applications of DEA in the transportation engineering domain. All of these papers are published in transportation journals. These papers are mainly aimed at addressing the transportation-related issues and thus do not focus too much on the DEA concept itself. It can be asserted that DEA is very likely to be used more and more in the transportation engineering domain. Given this, there is a need for the transportation professionals to fully understand the DEA concept. It is essential for such a community to identify cases where the application of this innovative and powerful method can be useful to help the decision-making process, to accurately apply DEA in a particular setting, to derive meaningful conclusions from the obtained results, and to acknowledge the limitations of DEA in certain cases so as to approach the results with caution. The purpose of this paper is to illustrate to the civil engineering, more specifically to the transportation engineering community the use of this powerful approach in performing comparative performance measurement. Within this context, this paper will address a transportation-related problem by using the DEA approach. Different from the other papers containing transportation-related DEA applications (as mentioned above), this paper will discuss, in detail, the steps that need to be taken to generate the DEA model and solve it.

The main aim of the study was to evaluate the efficiency of Islamic Azad University branches in East Azerbaijan using stochastic frontier analysis (SFA) and to compare the findings with the results of data envelopment analysis (DEA). In this respect, SFA and DEA techniques were applied to measure production efficiency of 28 branches of East Azerbaijan Islamic Azad University considering the following variables as either output or input: 3 variables as output (including the number of graduated students and students accepted for the further pursuit of their studies, as well as the number of articles, books, and research projects) and 6 variables as input (including the number of university faculty members, educational fields, current students, staff working in different sections of the university, and the total university campus in square meters as well as university costs). Estimation of DEA and obtaining of the units' efficiency indicated that the average efficiency of the universities under investigation were 0.66 and 0.80 in SFA and DEA techniques, respectively. In addition, efficiency distributions in SFA and DEA methods were tremendously different from each other so that in SFA method nearly 40% while in DEA method more than 60% of unit efficiency was between 0.8-1 intervals. The minimum level of estimated efficiency (0.14) was observed for Hadishahr using SFA method, and in DEA approach, the minimum level of efficiency belonged to Khoda Afarin branch (0.065).

Purpose– The purpose of this paper is to measure the efficiencies of parallel subsystems with shared inputs/outputs. Each subsystem has not only a set of common inputs and outputs, but also some dedicated inputs and outputs as well as some shared inputs and outputs. A more general data envelopment analysis (DEA) approach is proposed to deal with this efficiency evaluation issue. Based on the proposed approach, mechanisms for shared inputs/outputs distribution and efficiency decomposition among sub-units are presented.Design/methodology/approach– To evaluate the efficiency of the parallel systems, this paper proposes a centralized DEA approach by assuming that the same input/output factor in a decision-making unit (DMU) has the same multiplier for all its sub-units. Furthermore, different proportions of shared inputs/outputs are imposed on sub-units within different DMUs in evaluating each DMU’s efficiency. The proposed approach is applied to evaluate the operational efficiencies of 18 railway firms in China.Findings– By using the proposed DEA approach, the efficiencies of the whole DMU and its sub-units can be measured at the same time, and the optimal allocation strategy of shared inputs/outputs can also be obtained. The proposed model is more reasonable and robust for measuring the operational performance of parallel systems with shared inputs and outputs. The efficiency of railway system in China is relatively low, and its inefficiency is largely caused by lower freight transportation performance. Great disparities among firms can be found in the passenger transportation efficiency and freight transportation efficiency.Research limitations/implications– This study develops the DEA model under the assumption of constant returns to scale, which can be directly extended to a situation with variable returns to scale.Practical implications– In this paper, the proposed approach is a more effective way to evaluate the efficiencies of parallel systems with shared inputs/outputs. With respect to the application, to improve the overall efficiency of China’s railway system, more efforts should be taken to improve its operational performance of freight transportation. Furthermore, firms’ disparities should also be considered when making these related policies.Originality/value– The proposed approach can evaluate the whole DMU and its sub-units at the same time. Considering simultaneously the common/dedicated/shared inputs/outputs, the proposed approach is more general than the existing approaches in the literature. In the described approach, the same type of input or output is assumed to have the same weight for all sub-units within one DMU. More importantly, the proposed model imposes different proportions of shared inputs/outputs on different DMUs’ sub-units when measuring the efficiency for each DMU.

DEA approach for unified efficiency measurement: Assessment of Japanese fossil fuel power generation

PurposeThe purpose of this paper is to propose a benchmarking framework to evaluate the efficiency and effectiveness of the hotel industry, in a multi‐period context, with consideration of perishable traits and carry‐over activities. The sustained high performers in the case study are identified and their business strategies are discussed.Design/methodology/approachThe dynamic DEA (data envelopment analysis) approach is used to identify the multi‐period sustained high performers. The super‐efficiency DEA approach is employed to conduct a thorough ranking under an input‐output‐consumption structure. The supplementary analysis is further implemented to help elucidate the benchmarking results.FindingsIn total, nine out of 80 international tourist hotels in Taiwan during 2006‐2010 are identified as the sustained high performers. These hotels have diverged business strategies in terms of employees (intensive versus economical labor forces), products (room versus F&B (food and beverage) services), prices (extremely expensive versus very inexpensive room rates), guests (business versus tourism guests), and others (e.g. location advantages).Originality/valueThis study contributes to benchmarking literature and to hotel industry in several aspects. Compared with conventional single‐period benchmarking in efficiency aspect only, the proposed multi‐period benchmarking framework under input‐production‐consumption structure can take into account the carry‐over activities, account for perishable traits, provide more robust results, and add more useful information to facilitate the hotel managers to ameliorate the efficiency and effectiveness. The proposed framework should be readily applied to other service industries (e.g. transport).

Financial derivatives products are believed to be significant tools for mitigating market risks. Accounting practice treats transactions on financial derivatives to highlight its role on the items being hedged (hedged items) but it does not represent a holistic context of the derivative instrument used, particularly as a risk management tool. This study develops an extension of hedge effectiveness analysis tested by dollar-offset ratio integrated with a non-parametric approach (Data Envelopment Analysis — DEA) to provide an overall evaluation of derivatives for risk management. Twelve banks in Asia-Pacific region are selected as a sample. The twofold results of analysis first indicate the implications on the extension of hedge effectiveness test using DEA, and second, discuss the differences in bank's risk management efficiency between developed and developing markets.

A comparative analysis of DEA as a discrete alternative multiple criteria decision tool

The current economic crisis has put ever more to the forefront the need to achieve educational goals in the most efficient way. Therefore, this report provides an empirical analysis of the efficiency in education in the EU. Efficiency is measured first by using two different but related traditional frontier approaches (Data Envelopment Analysis and Free Disposal Hull) and then the robustness of our findings is checked by means of multi-criteria evaluation. The analysis is based on a number of standard variables from the literature. The results show, among others, that not the amount, but the specific use of resources is what matters; and that the efficiency of an educational system could also contribute to long-term benefits in terms of adults’ skills and competences.

Effective assessment of power grid investment efficiency is crucial for optimizing resource allocation and improving operational performance. However, existing evaluation methods typically fail to account for two critical factors: inherent uncertainties in input–output data and temporal delays in investment returns. To address these limitations, this study introduces an integrated evaluation framework combining robust optimization techniques for uncertain variables with a time-lag Data Envelopment Analysis (DEA) approach to capture the multi-period dynamics and ensure resilience against external shocks and data perturbations. An empirical analysis conducted on panel data from 31 provincial power grid enterprises in China (2015–2023) reveals significant regional disparities in efficiency, particularly between coastal and resource-rich provinces. The findings highlight that excluding time-lag effects leads to systematic underestimation of efficiency and employing robust optimization yields more resilient efficiency scores amidst data uncertainties. The study contributes methodologically by advancing DEA frameworks to better reflect the complexities of power grid investments and empirically provides valuable insights for policymakers seeking to enhance investment strategies and achieve sustainable development goals.

The theoretical and empirical debate on the effect of managerial autonomy on the efficiency of urban utilities is still ongoing, yet limited research has been conducted in the water sector in developing countries. To address the gap, this paper gives empirical evidence on the effect of managerial autonomy on the efficiency of urban water service providers in Uganda and Tanzania. Using the managerial autonomy index and Data Envelopment Analysis (DEA), information from performance and management contracts, utility annual financial reports, partnership deed agreements, water policy statements, and results of 66 respondents was used to determine the level of managerial autonomy and efficiency in the 30 Decision Making Units (DMUs) of NWSC in Uganda and DAWASCO in Tanzania. The effect of managerial autonomy on efficiency was determined using Tobit regression analysis. The results show the mean managerial autonomy level of 47% implying that Head Office (HO) of water utilities has given limited autonomy to the different Decision Making Units (DMUs). Most of the DMUs have no or limited autonomy on procuring assets for their units, entering loan agreements, approving the annual report, and determining staff compensation. The fully efficient towns (1.00) are four and the mean efficiency level is 0.63. The Tobit regression model indicates that the adoption of managerial autonomy significantly (Prob>chi2 = 0.0006) contributes to efficiency. The findings indicate a high likelihood of managerial autonomy causing a change in the efficiency of urban water utilities. The position that an increase in managerial autonomy will cause the likelihood for technical efficiency to decrease, could be expected in the event that the principal (HO) who grants autonomy may not keenly and continuously supervise and monitor the activities of the agent (DMU). Urban water utility managers should put in place an appropriate structural framework to govern the relationship between the water utility HO and the DMU. Besides this, the utility head office should delegate decision making powers to the DMUs based on a clear framework.

Traditional data envelopment analysis (DEA) and diversification DEA are two common data-driven evaluation approaches, which have been widely used in the estimation of portfolio efficiency. The above two DEA approaches usually use the risk and expected return indicators to build the input-output process of portfolios. However, this input-output process derived from the risk and expected return is inconsistent with the actual investment process, since the real input should be the initial wealth, and the output should be the terminal wealth. To address this problem, we propose a novel input-output process based on the initial and terminal wealth of portfolios. We transform the terminal wealth into the rate of return and construct a stochastic attainable set by using portfolio returns. We provide three deterministic estimation approaches to deal with the stochastic attainable set, and then obtain three deterministic attainable sets. We further propose three stochastic DEA models to estimate the portfolio efficiency by using the above three deterministic attainable sets. Finally, we provide an empirical analysis to assess the portfolio efficiency of 50 open-ended funds in China. The results show that there are some differences in the portfolio efficiency and its ranking between the proposed DEA models and the existing DEA models, which further verify the rationality of the proposed DEA models.

Seaport network efficiency is very crucial for global maritime economic trades and growth. In this work, data of three years (2018–2020) with input variables (time in port, age of vessels, size of vessels, cargo carrying capacity of vessels) and output variables (Liner Shipping Connectivity Index (LSCI) and Gross Domestic Product (GDP)) are collected. Few screening tests are performed to ensure the data are fit for further analyses. Since none of the existing studies has ever considered LSCI as an output variable, the main purpose of this study is to measure seaport network efficiency based on LSCI using data envelopment analysis (DEA), both classical and fuzzy. In fuzzy DEA, triangular fuzzy number (TrFN) and trapezoidal fuzzy number (TpFN) are used to construct the fuzzy sets of efficiency scores with DEA. The comparison between DEA and triangular fuzzy data envelopment analysis (TrFDEA) shows the range of differences in the results ranges from −0.0274 to 0.0105, while the comparison between DEA and trapezoidal fuzzy data envelopment analysis (TpFDEA) yields the differences within the range of −0.0307 to 0.0106. Using DEA as the relative reference, it is further revealed that the TpFDEA has smaller standard deviations and variances than the TrFDEA in 2018 and 2019, whereas the opposites hold true during the pandemic year of 2020. With the use of fuzzy numbers, the uncertainty levels in the seaport network efficiency measurement can further be investigated as the minimum, mean, median and maximum values are taken into consideration. Moreover, the proposed TrFDEA and TpFDEA lead new insights on the boundedness concept of the efficiency scores, which were never reported before by any researcher, especially in the maritime industry research. Fuzzy regression modelling based on the Possibilistic Linear Regression Least Squares (PLRLS) method was also performed to determine the interval of minimum and maximum connectivity efficiencies, which gave a better estimation than the regular regression model.