A coherent approach to Bayesian Data Envelopment Analysis

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.

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.

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.

Data Envelopment Analysis (DEA) is employed as a tool to evaluate the efficiency score of Tehran Stock Exchange. For predicting financial distress, it is designed and tested a model base on efficiency score. Its accuracy was verified by employing another model designed by financial ratios and variables based on Multivariate Discriminant Analysis (MDA). Ultimately, to investigate the effectiveness of firm’s efficiency score on financial distress prediction, its score combined with financial ratios is entered in an MDA model. The results show that all the three proposed models, in this paper, have better ability of predicting financial distress in Tehran Stock Exchange for two years prior to its occurrence. It implies that, DEA efficiency score is an effective predictor variable. Keywords: Efficiency score, Data envelopment analysis, Financial distress, Tehran stock exchange.

An application of data envelopment analysis in telephone officesevaluation with partial data

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).

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.

In many applications of data envelopment analysis (DEA) approach, there is often a fixed cost or resource which is imposed on all decision making units (DMUs). One nature question then is: how can this cost or resource be assigned in a reasonable way to various DMUs? In this paper, we present a new DEA-based method for obtaining this cost or resource allocation. To illustrate our methods, numerical results for an example from the literature are presented.

Maintenance effectiveness in manufacturing facilities is a critical factor in achieving significant competitive advantage. Consequently, attaining higher levels of maintenanceeffectiveness has recently become a primary goal for managers of world-class organizations. Tbis paper presents a study, which will demonstrate bow managers can identify best inclass performers in terms of maintenance and how to conduct an improvement analysis using data envelopment analysis (DEA) approach. Data on 13 paper mills collected from a large U.S manufacturing company arc used to identify low performers and determine wbich high performer plants to benchmark in order to improve their efficiency. A discussion of results is also presented. The emphasis of this paper is on tbe application.

The harmonious interaction between humans and the biosphere determines how sustainable existence and coexistence are. However, when social challenges appear, sustainability is heavily compromised. Health is one of the areas affected by such challenges, with the delivery of health services being ensured by health systems. Consequently, understanding how sustainable health services are, especially those that consume the majority of resources—hospitals—is indispensable for a sustainable future. For this reason, we propose using a hybrid data envelopment analysis (DEA) approach to study hospital sustainability in Portugal under environmental, social, and economic perspectives, in cooperation with the Portuguese Ministry of Health. In particular, the proposed methodology incorporates the preference information of decision makers (via the construction of utility scales and the determination of Möbius coefficients) and criteria interactivity, due to the integration of the Choquet multiple criteria preference aggregation model in the DEA approach. In the end, despite approximately 30% of the sampled 29 assessed hospitals were deemed as efficient across the three perspectives in 2018, only 1 was entirely sustainable.

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

This study is aimed to examine the efficiency and productivity growth of Initial Public Offerings (IPOs) firms listed on Karachi Stock Exchange (KSE), Pakistan from 2000 to 2012 by using three stages - Data Envelopment Analysis (DEA) and Malmquist Productivity Index (MPI). The analysis and comparison of the efficiency and productivity growth of IPOs on sectoral basis in the pre and post period of IPOs is also investigated. This is ever first study to measure the pre IPO efficiency across the globe in the field of IPOs. The overall efficiency scores of IPO firms dismal as the percentage of optimum level of IPO firms remain between 5% and 20% in all the three stages in pre and post IPOs. In the analysis of broader categories of sectors; private, state-owned enterprises (SOEs), manufacturing, financial, other services sectors, the results of DEA model of three stages suggest that neither of the sector is constant return to scale (CRS) efficient nor and Variable Returns to Scale (VRS) in pre and Post IPO. Even the efficiency scores are decreased in post IPO after one year. However, in detail sector-wise analysis the only oil and gas sector showed optimal level under VRS in stage 2. Further, SOEs showed some better efficiency than private IPO firms. The overall results of declining trend in total productivity growth of IPOs after three years period in KSE are observed. The overall results suggest that, after acquiring further resources, IPO firms did not improve their efficiency level and productivity growth after the period of three years of IPOs. DOI: http://dx.doi.org/10.5755/j01.ee.27.2.10062

Despite the establishment and revitalization of small-scale irrigation schemes, input subsidies and tractor hire schemes in the rural Eastern Cape Province of South Africa productivity among small-scale farmers is recorded low and anticipated to decline. For survival, small-scale farmers have resorted to cultivating high value crops including vegetables. However, their vegetable productivity is far less than the estimated potential yields, and information regarding their production efficiency is limited. Therefore, this study was aimed at determining farmer’s production efficiency to generate meaningful information necessary for designing feasible pro-poor policies aimed at catalysing increased the productivity and rural household incomes. The study was carried out at Qamata and Tyefu irrigation schemes, and approximately 158 farmers were interviewed. The Data Envelopment Analysis (DEA) approach was used to generate results. The findings in this article indicated that most farmers are old aged with low literacy levels. Farmers were also allocating few hectares of land for cabbage production with far less application of fertilizers and pesticides compared to the recommended amounts. Farmers at Qamata and Tyefu irrigation schemes are technically, allocatively and economically efficient at 98%, 72% and 77% level, respectively. Thus, for improving the productivity, farmers need to maintain the same technologies and adjust on the amounts of fertilizers, seeds and pesticides used for improving allocative and economic efficiency. Results suggested that this can be achieved through encouraging more youth participation in farming, improved input-agronomic and agribusiness skills, catalysing processes of land reforms, and construction of more dams.

In this study, the financial performance of 10 logistics companies operating in the storage, transport and logistics services sector in Turkey is evaluated for years from 2011 to 2017 by using the Data Envelopment Analysis (DEA) method. In accordance with the purpose of this study, the input variables are selected as total assets, equity and number of employees whereas the output variables are net sales, profit before interest and tax and amount of exports. In measuring the financial performances of firms listed in the data set, the Charnes, Cooper, Rhodes (CCR) model based on the assumption of constant returns to scale and the Banker, Charnes, Cooper (BCC) model based on the assumption of variable return to scale are employed in the input-oriented DEA model. As a result of our study, efficient and ineffective logistics companies are identified for years from 2011 to 2017. The reference sets for ineffective companies are determined, and the extent to which ineffective companies should improve according to the companies they refer to is determined. The results of DEA CCR-I model suggests that 2015 has the highest number of active companies (7). The year in which the average efficiency scores of the companies is the highest (89.2%) is 2016. According to the DEA BCC-I model, 2015 is associated with the largest number of active firms (9) and the average productivity scores of these firms are the highest (99.1%) in that particular year.

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).