Measuring efficiencies of parallel systems with shared inputs/outputs using data envelopment analysis

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.

Data envelopment analysis (DEA) is a tool for measuring the relative efficiency of decision-making units (DMUs) that perform similar tasks. One of the uses of the DEA is the establishment of benchmarks for inefficient DMUs. These benchmarks play the role of target for inefficient DMUs. However, traditional DEA (TDEA) models fail to rank the efficient DMUs, and as a result, they cannot determine a benchmark for the inefficient DMUs. To overcome this shortcoming, this paper proposes virtual network DEA (NDEA) approach. The proposed method not only ranks the whole inefficient and efficient DMUs but also establishes a virtual benchmark called ideal DMU. The case study demonstrates the applicability of the proposed model.

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.

Traditional data envelopment analysis (DEA) is used under the premise that inputs and outputs are exact values. If it is not true, the DEA approach is unavailable. However, it is common that some of the entries in the data are missing in practice. As a result, the current paper performs efficiency evaluation with missing data considering the missing-data properties (missing-data patterns and missing-data mechanisms). A multiple imputation (MI) approach is used to estimate the missing values. The MI approach is applied to a forest reorganisation problem for reliability. An example of public secondary schools is given to illustrate the proposed technique. When input or output values for decision making units (DMUs) continuously vary under an interval, the current paper characterises a DMU’s pessimistic and optimistic efficiency functions of an input or output of most interest. A Monte Carlo simulation technique is used to obtain a DMU’s efficiency distribution.

DEA model with shared resources and efficiency decomposition

Efficiency evaluations with context-dependent and measure-specific data envelopment approaches: An application in a World Bank supported project

Production trade-offs represent simultaneous and possible changes to the inputs and outputs in the technology under consideration. However, since trade-offs are illative and subjective, in many real applications, the data of production trade-offs cannot be precisely measured. Occasionally, a crisp trade-off cannot reflect desirable judgment of expert. This paper develops the trade-off approach in data envelopment analysis (DEA) using imprecise trade-offs represented by fuzzy sets. We develop some fuzzy versions of trade-off DEA models by using some ranking methods based on the comparison of α-cuts. We show in numerical examples how our models become useful for detecting sensitive trade-offs. In other words, our approach can be seen as extension of the trade-off approach that provides users with models, which represent real evaluation of decision making units (DMU) with good judgments as possible trade-offs.

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).

How to assess the greenness of products from the viewpoint of LCA (Life Cycle Assessment)is one of the important issues in PLCA (Products Life Cycle Analysis). In this report, we present a non-uniform assessment method for greenness assessment of products based on DEA (Data Envelopment Analysis)model after analyzing the shortcomings of the existing comprehensive assessment methods for green products. This method not only assesses the greenness of given products with regard to technical reference product constructed by means of the information of products, but also illustrates how to improve the greenness of products quantitatively by utilizing the project theory on DEA efficient frontier of DMU (Decision Making Unit). Finally we take a numerical example concerning greenness assessment of refrigerators to show how to use the established method and prove its availability.

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.

This paper presents a new data envelopment analysis (DEA) target setting approach that uses the compromise programming (CP) method of multiobjective optimization. This method computes the ideal point associated to each decision making unit (DMU) and determines an ambitious, efficient target that is as close as possible (using an lp metric) to that ideal point. The specific cases p = 1, p = 2 and p = ∞ are separately discussed and analyzed. In particular, for p = 1 and p = ∞, a lexicographic optimization approach is proposed in order to guarantee uniqueness of the obtained target. The original CP method is translation invariant and has been adapted so that the proposed CP-DEA is also units invariant. An lp metric-based efficiency score is also defined for each DMU. The proposed CP-DEA approach can also be utilized in the presence of preference information, non-discretionary or integer variables and undesirable outputs. The proposed approach has been extensively compared with other DEA approaches on a dataset from the literature.

Background: Efficiency of production system is calculated by the input spent to amount of output produced. The Agronomic efficiency has been found to be below 48% than the world efficiency standard of 40 kg ha-1. The average fertilizer usage of India is 133 kg ha-1, 232% higher than the global standard fertilizer of 40 kg ha-1. The certified seeds and agriculture machinery usage has been increased by 15% and 13% in India but the efficiency of farm stays behind. Methods: With this back ground the present study applied cost approach variable return to scale Data Envelopment Analysis (DEA) model to evaluate the Indian farms resource using 200 rural farms of Tamil Nadu state. The data envelopment analysis models have the advantage of simultaneous measure of technical, allocative and cost efficiency of the individual farms. Result: The cost efficiency among the respondents ranged from 0 to 1 with a mean efficiency of 0.66. The study found that Decision-Making Units (DMU) have cost inefficiency, suggesting that most of the DMU operated very far away from the efficiency frontier. Allocative inefficiency (0.55) is worse than the technical inefficiency (0.80), implying that the lower economic efficiency is the result of high allocative inefficiency.

A distance friction minimization approach in data envelopment analysis: A comparative study on airport efficiency

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

In many countries, rising demand for diagnostic services coupled with decreasing healthcare budgets makes it necessary to increase the technical efficiency of radiology departments. This study examines how the expertise of radiology technologists—specifically their education, training, and specialization—affects the technical efficiency of 168 radiology sub-units within a leading university hospital in Italy which performed over 700,000 tests between 2021 and 2022. In our study, each Decision-Making Unit (DMU) represents an organizational unit operating only one type of imaging technology (X-ray, Magnetic Resonance Imaging, or Computerized Tomography). Each piece of equipment can be allocated to only one DMU during the time horizon, while radiology technologists overseeing these examinations can be assigned to multiple DMUs without restrictions. The bootstrapped two-stage Data Envelopment Analysis (DEA) approach was taken as a reference since it provides a robust way to draw statistical inferences. To account for the specificity of the case, in the first stage, we enhanced the DEA model by incorporating non-discretionary inputs and applying the metafrontier approach. The integration of non-discretionary inputs reflects the fact that equipment allocation is beyond managerial control in the short term due to structural or external constraints. The metafrontier approach was selected because it enables valid efficiency comparisons across DMUs using different technologies. The second stage involves a truncated regression analysis through which we identify efficiency determinants. Our findings show that while technologists’ experience can negatively affect efficiency, their specialization has a positive effect. Staff training is crucial for maximizing the positive impact of new technologies on efficiency. Additionally, the presence of elderly patients and emergency referrals further reduces efficiency. This study offers healthcare managers insight into how to optimize resource allocation and enhance efficiency and provides a robust method for evaluating efficiency in settings with units employing different technologies and non-discretionary inputs.