Data Envelopment Analysis as a Decision-Making Tool for Transportation Professionals

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).

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

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.

The study introduces a new method for assessing differences in levels of environmental quality between neighborhoods via application of the management science evaluation procedure data envelopment analysis (DEA). DEA that has the capacity to evaluate activities and process situations where more than one outcome is simultaneously involved is employed here to estimate the relative differences in environmental quality which majority versus minority populations may face between communities within a community. A relative assessment procedure, DEA illustrates in the study how to assess whether minority populations relative to majority populations within the same larger community face different levels of environmental quality that cannot be explained by differences in education, income, or other non-race based characteristics. The article points out the danger of simply inspecting one environmental quality factor at a time. Such a single factor evaluation can as the study illustrates generate misleading conclusions of environmental quality conditions. The study shows that the DEA approach while not solving all the problems of multiple source environmental factor influences, provides a useful method for looking at some dimensions of this problem.

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.

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.

Objective: To synthesize the current evidence base concerning the application of Data Envelopment Analysis (DEA) in healthcare efficiency during the COVID-19 pandemic using a scoping review of 13 primary studies. Methods: We consulted databases including Web of Science (WoS) and Scopus, as well as manual search entries up to September 2022. Included studies were primary applications of DEA for assessing healthcare efficiency during the COVID-19 pandemic. Key findings derived from thematic analysis of repeating pattern observations were extracted and tabulated for further synthesis, taking into consideration the variations in DMU definitions, the inclusion of undesirable outputs, the influence of external factors, and the infusion of advanced technologies in DEA. Results: The review observed a diverse application of DMUs, ranging from healthcare supply chains to entire national health systems. There was an evident shift towards incorporating undesirable outputs, such as mortality rates, in the DEA models amidst the pandemic. The influence of external and non-discretionary factors became more pronounced in DEA applications, highlighting the interconnected nature of global health challenges. Notably, several studies integrated advanced computational methods, including machine learning, into traditional DEA, paving the way for enhanced analytical capabilities. Conclusions: DEA, as an efficiency analysis tool, has exhibited adaptability and evolution in its application in the context of the COVID-19 healthcare crisis. By recognizing the multifaceted challenges posed by the pandemic, DEA applications have grown more comprehensive, integrating broader societal and health outcomes. This review provides pivotal insights that can inform policy and healthcare strategies, underscoring the importance of dynamic and comprehensive efficiency analysis methodologies during global health emergencies.

Alsharif KA, Fouad G. 2012. Lake performance differences in response to land use and water quality: Data envelopment analysis. Lake Reserv Manage. 28:130-141. Comparisons of lake conditions to surrounding land use classifications and water quality can be used to assist management of lake resources. This study used Data Envelopment Analysis (DEA), a performance measurement tool used in economics and operations research and management science, to compare lakes based on land use classifications and water quality. Lake performance ratings reflecting land use and water quality conditions were generated by DEA models that assessed surrounding land use and the reciprocal of the chlorophyll a concentration in the Tampa Bay region, Florida, USA. Using the performance continuum established by the DEA, lakes were categorized according to their management demands. Increased catchment, natural land, and agricultural land area were all related to lower performance measurements. It was found that higher DEA performance measurements were estimated for lakes located in smaller catchments that had a lower possibility for runoff and transport of contaminants. Overall, DEA successfully functioned as an assessment tool for comparing lake conditions based on land use in the catchment. The results produced by this novel application of DEA can prioritize management efforts toward lakes that suffer from the least beneficial water quality and land use conditions.

Using a Decision Analysis (DA) model for decision-making under partial probability information, we derive the basic Data Envelopment Analysis (DEA) models. The DA model has two components: the choice of a strategy and the occurrence of events. There is an outcome associated with each strategy–event combination. A Linear Programming (LP) model is formulated to reveal the strategies that are called efficient for given event probability ranges. Viewing the strategies as entities and the events as activity categories with outcomes that may have positive or negative effects on the entities' performance, the LP model can be configured for gauging the entities' productive performances. One particular configuration of the LP model called the LP-DM leads to the basic models of DEA. The LP-DM proffers a new starting point for developing methods that gauge productive performance and a different perspective that reiterates the caution that must be exercised in the application of DEA and use of its results for policy making.

Emerging application of robust data envelopment analysis for optimization of graft copolymerization of poly(2-hydroxyethyl methacrylate) to Tamarindus indica seed polysaccharide

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

In this paper we provide the methodology for evaluating effectiveness of international sanctions using Data Envelopment Analysis (DEA), which we use for generating the network matrix for further analysis. DEA is a non-parametric technique used to compare performance of similar units, such as departments or organizations. DEA has wide applications in all industries, and has been successfully used to compare performance of hospitals, banks, universities, etc. The most important advantage of this technique is that it can handle multiple input and output variables, even those not generally comparable to each other. We use the “Threat and Imposition of Sanctions (TIES)” Data 4.0 for analysis. This database contains the largest number of cases of international sanctions (1412 from the years 1945–2005) imposed by some countries on others, takes into account simultaneous sanction imposition, and also estimates the cost of all sanctions - both for those who receive and those who impose them. As input variables for DEA model we use the impact of sender commitment, anticipated target and sender economic costs, and actual target and sender economic costs. As the output variable, we use the outcome of sanctions for senders. We describe how to use DEA cross-efficiency outputs to build the network of sanction episodes. Our proposed combination of DEA and network methodology allows us to cluster sanction episodes depending on their outcomes, and provides explanations of higher efficiency of one group of sanction episodes over the others.

This study conducts relative efficiency of fire and emergency services using a nonparametic technique called Data Envelopment Analysis (DEA). This study selects a set of inputs and outputs and examines the nature of information obtained from the DEA procedure. The study focuses on the interpretation and practical usefulness of such information for the fire and emergency services. The results of this study help fire and emergency managers and consultants understand how the DEA approach can be used to identify weaknesses and strengths of fire and emergency services and evaluate their efficiency.

Purpose The determination of input and output factors is a well-known source of pitfalls when applying data envelopment analysis (DEA). The purpose of this paper is to contribute to overcome the respective problems of input/output factor determination related to factor selection, dual-role factors and undesirable factors. Design/methodology/approach The problems of input/output factor determination are discussed from a goal-oriented perspective, shedding a new light on the role of input/output factors in DEA. This is exemplified by the case of measuring pharmacy stores’ efficiency concerning their goal of customer retention. Findings The findings suggest to applying a generalized DEA (GDEA). The three steps of this approach include the development of a system of objectives, the derivation of corresponding performance criteria as well as the construction of cost and benefit functions. These functions build the basis for GDEA models, of which one is exemplarily described and applied to the customer retention case. Research limitations/implications While traditional DEA implicitly assumes linear cost and benefit functions, GDEA requires to explicitly specifying these functions. In doing so, the approach contributes to solve the problem of factor selection, the problem of dual-role factors and the problem of undesirable factors. Practical implications For determining input/output factors in a consistent and transparent manner, it is recommended to apply GDEA in practical benchmarking studies. Originality/value GDEA integrates well-known concepts of multi-criteria decision making into traditional DEA. The new approach helps to cope with the challenges of input/output factor determination in DEA.

Financial distress is a company’s inability to meet their financial obligation, which finally leads to going bankruptcy. Financial distress is then used as an early warning signal before going bankrupt. Therefore, financial distress should be predicted as preventive actions. This study main objective is to compare the traditional predictions tools, Altman’s Z-score model, with the new propose Data Envelopment Analysis (DEA) approach method. Focusing on Indonesia steel and iron industry, this study examines using 7 steels and iron companies which listed in IDX from the period of 2013- 2018. Starting from constructing the model of DEA in predicting distress, the accuracy test of both models is compared. The results reveal that DEA’s approach prediction has a higher accuracy rate compared to the Altman’s model. DEA with a total of 39 correct predictions out of 42 samples generate an accuracy rate of 92.86%. This rate is higher than the Altman’s model with the accuracy rate of 85.71% which resulting from a total of 36 correct predictions out of 42 samples. The method, especially DEA to predict financial distress for Steel and Iron Companies in Indonesia is the significant contribution to science. Keywords: Altman (Z-Score), DEA, Financial Distress