The paper aims to analyze the Technical Efficiency of 70 Commercial banks from 19 African countries from 2009-2020. Using the Data Envelopment Analysis (DEA) method of the two main approaches, Variable Return to Scale (VRS) and Constant Return to Scale (CRS) technique on a Panel Data. We find that African banks have a higher efficacy assessment with the VRS than the CRS technique, thus, with a Pure Technical Efficiency (PTE) score than Technical Efficiency (TE) . Our findings show that the majority of the banks are operating at very low levels of efficiency (not technically efficient), and inability to optimize the conversion of bank assets and liabilities into loan production for customers. Furthermore, the banks are operating inefficiently in scale, economic, and allocative manner due to mismatches in scale of production. Considering these findings, the implications of these inefficiencies extend to the overall economic development and financial stability of the region.

Variable selection is an important step in data envelopment analysis (DEA) when the number of decision making units (DMUs) is insufficient. This research thus proposes a two-stage variable selection method integrating a well-known supervised machine learning technique, the random forest algorithm. In the first stage, a baseline DEA model with full input and output variables is implemented and each DMU can be determined as being efficient or inefficient. In the second stage, a random forest is trained to learn how to classify efficient or inefficient DMUs well in high dimensions. Accordingly, the importance of each variable can be calculated based on permutation importance indices in random forest. This paper further discusses two issues about data augmentation in order to improve the robustness of permutation importance when the number of DMUs is quite small.

Data Envelopment Analysis (DEA) is an effective method for evaluating and improving the performance of Decision-Making Units (DMUs). It utilizes mathematical programming models to compare homogeneous units based on their inputs and outputs. One of the major challenges in this field is assessing the efficiency of DMUs over different time periods, where heterogeneity, arises due to various factors, such as, scientific and technological advancements, political and economic changes, system management updates, etc. These changes may lead to the addition or elimination of outputs, making unit comparisons more complex and creating significant differences in efficiency. Traditional DEA methods often fail to account for these changes across time periods simultaneously. Therefore, there is a need for new approaches, as to assess the efficiency of DMUs under such conditions. This paper addresses these challenges and presents a novel approach for analyzing the efficiency of DMUs undergoing substantial changes over time. As a practical application, the results of an empirical study evaluating conferences throughout three time periods, namely, (before the COVID-19 outbreak, during the pandemic, and after the pandemic) have been presented. These findings demonstrate the efficiency of the proposed approach effectively and can significantly assist organizations in more accurate evaluations and the enhancement of performance, relative to the evolving units.

The increasing population in Indonesia results in the rising demand for consumer goods, including salt. Meanwhile, salt production in Indonesia remains traditional, using direct methods by evaporating seawater in open ponds near the coast, producing a final product called “solar salt”. This process depends on sunlight, air, weather, and seasonal climate conditions. This research aims to analyze the technical, technological, and managerial disparities among traditional solar salt farming businesses operated by local communities across regions in Madura Island—the foremost solar salt-producing region in Indonesia. This study employs primary data collected through surveys conducted during the production season in 2023/2024 in three regencies in Madura: Pamekasan, Sampang, and Sumenep. The structured questionnaires captured the input and output data. Meta-frontier Data Envelopment Analysis (meta-DEA) was applied to assess the technical efficiency of conventional solar salt productions across the research regions. The efficiency analysis revealed that, with the current production methods, solar salt farmers achieved an efficiency rate of 46.98%, with an average technical efficiency of 80.83%. This result shows that the decision-making units (DMUs) can enhance their technical efficiency by 19.07%. Meanwhile, the technology gap ratio (TGR) analysis indicates that Sumenep Regency has the highest TGR value, nearing the threshold of 1, suggesting a relatively low technology gap in this regency. The meta-DEA decomposition indicates that the determinant of the average meta-technical inefficiency among solar salt farmers is the technological disparities, with average technological gap inefficiency (TGI) values surpassing managerial gap inefficiency (MGI) values. Conversely, Sumenep Regency has a larger MGI than TGI value, implying that solar salt farmers in Sumenep possess lower managerial decision-making skills than in other regions. The findings suggest the need to enhance the adoption of the latest production technology innovations to address technological gaps in the research locations.

The purpose of the study is to evaluate the efficiency of Islamic Rural Banks (BPRS) and analyze the factors that determine them using a two-stage approach to Data Envelopment Analysis (DEA). DEA in this study focuses on the production, intermediation, and inefficiency causes. This research was done on BPRS across Indonesia. The data were taken from a financial report for the 2013-2021 period. The source of the data was a publication from the Financial Services Authority of Indonesia. The data were analyzed using the non-parametric approach with a two-stage DEA method. The input variables were personnel costs, fixed assets, and third-party funds. The result shows that Revenue Sharing, ROA, and Growth have a significant positive effect on DEA. BOPO and inflation have a positive but insignificant effect on DEA. While NPF and FDR have negative but insignificant effects on DEA. Then CAR has a negative and not significant effect on DEA. It also shows that the variables of Revenue Sharing, NPF, ROA, CAR, FDR, BOPO growth, and inflation have a simultaneous effect on DEA.

Financial system stability is not only supported by the banking sector, but also the role of insurance companies that operate efficiently. The study aims to analyze the efficiency performance of general insurance companies using two stages of data envelopment analysis during the 2017 – 2018 period. The first stage of efficiency measurement using a non-parametric data envelopment analysis (DEA) approach shows the efficiency level of general insurance companies experiencing a positive trend. The performance of general insurance companies in 2018 was more efficient than in 2017 based on the value of technical efficiency (CRS) and the value of pure technical efficiency (VRS). This means that in general there has been an increase in the efficiency of general insurance companies in Indonesia from 2017 to 2018. Testing the efficiency determinants in the second stage using the Tobit regression model found that the cost ratio is the only factor that significantly influences the efficiency level of general insurance companies in Indonesia. Meanwhile, company ownership and investment adequacy ratio have no significant effect on the efficiency level of general insurance companies in Indonesia. The results of the study provide recommendations to the management of general insurance companies that efficiency performance has not reached the maximum, and to improve it, it is necessary to control costs without disturbing routine operations and development activities.

The purpose of this article is to make an assessment on infrastructure development efficiency based on Data envelopment analysis (DEA) method. The proposed method allows assessing the level of current infrastructure conditions which provides production and social dimension of its stable development. The article substantiates the necessity of conducting an analytical assessment of infrastructure development level which identifies its weaknesses and determines potential of improving its performance indicators. An integral assessment of transport infrastructure efficiency is proposed. To assess the problems of infrastructure development provision, a comparative analyses and ordering the objects according to growth or decrease of the indicators is performed. The results of the research allow discovering the causes of harmful phenomena in the system of management and development, and identifies major trends and prospects of development.

A new approach is applied in the process of measuring the efficiency of decision-making units (DMUs) through the cross-efficiency evaluation method. Ideal and Anti-Ideal models are generated to form a comprehensive method based on the cross-efficiency evaluation method. The two models are formulated and combined to the Data Envelopment Analysis using the CRITIC method. In a comparative analysis based on three numerical examples, the proposed approach can lead to achieving a more reliable result than one based on an individual method.

The paper addressed the following research problem: Is the DEA (Data Envelopment Analysis) a suitable method of measuring and improving the performance of a company? Is it possible to calculate the goal values of the key performance indicators based on the BSC (Balanced Scorecard) principles using the DEA results? Is it possible to link the results of the BSC method and the DEA method to increase business performance? The aim of the research was to find out which financial indicators of the company are key performance indicators. We selected the key performance indicators using the BSC principles. These indicators were then applied as inputs/outputs in the DEA model. The partial aim was also to propose goal values for selected key performance indicators. The analysis was carried out on a sample of companies operating in the sphere of heat industry in Slovakia. The sample was created to represent businesses that have problems with their performance as well as those who do not have any problem. When choosing the sample, we made use of Altman's Z-score. We then calculated the values of the selected financial indicators. The correlation matrix and the BSC principles were applied to select key performance indicators. We also made use of the DEA method. This nonparametric method can be applied in the field of business efficiency, financial health, and performance evaluation. The DEA CCR input-oriented model was designed. We see the application of the DEA method in assessing financial health and performance of a company as well as the DEA CCR model itself as the main contributions of this paper. The research also paid attention to the calculation of the goal values of key performance indicators that enter the BSC as goal values. Using these goal values, a company may become more stable and therefore competitive. This research confirmed the possibility of integrating BSC and DEA.

This paper evaluates the efficiency of five Indian pharmaceutical Industries using Data Envelopment Analysis (DEA) approach. The paper uses Net Block, Cash and Bank Balance, Share Capital, Reserve and Surplus, Secure Loan and Unsecured Loan as input variables and Investments and ‘Loans and Advances’ as output variables. In this study, the Basic Radial Models input oriented with Constant Returns to scale (CRS) are used to estimate the efficiency of pharmaceutical Industries. The DEA tool assists the administrators to identify the inefficient measures and take necessary actions for improvement. The results are indicative of the scenario that changed patent laws in India are not detrimental to the financial and overall health of Indian pharmaceutical companies in general. The Indian pharmaceutical companies have suitably modified their business model to cope up with the changed legal environment. It is a positive sign for all Indian pharmaceutical companies that they are dynamic in management and operational policies to face any new situation and shall flourish more in the coming days.