Turbofan aero-engine efficiency evaluation: An integrated approach using VSBM two-stage network DEA

Nowadays, the growth of industry can be seen as a nature of the world. Each company race again each other to increase productivity to produce new, high quality and product that fulfil customer demand. One can achieve the Key Performance Indicator (KPI) or targeted goal but without considering the cost, manpower, time or others elements is inefficient toward productivity. Upgrade production line in manufacturing industry needs huge investment to come out with good performance. The company can receive Return of Investment (ROI) and save more money from paying labor salary and increase productivity. However, the company also may have the risk of losing their money from the investment done. In this research, we studied the effectiveness of production line that equipped with automation usage to determine the productivity and quality of the product produced. We apply Data Envelopment Analysis (DEA) to measure efficiencies of the production line where DEA is one of an excellent tool that can evaluate efficiencies and have been using widely in many sectors. The model that will be used in this study is Two-Stage Network DEA. As a case study, this research focuses on the production line that producing a product with a high and continues demand to observe how the investment on automation can give good return or otherwise.

Performance or efficiency evaluation is of great importance for effective supply chain management, especially in the context of sustainable development and platform economy. In existing literature, two-stage network data envelopment analysis (DEA) has been widely used for supply chain efficiency evaluation. Although existing supply chain DEA usually assumes two-stage systems resolve the inherent conflicts between two stages, e.g. the supplier and manufacturer in a two-echelon supply chain, the relation of supply chain DEA to traditional ‘black box' DEA remains unclear. Moreover, the coordination effect between the two stages is ignored. In this paper, we define the coordination efficiency and propose a DEA-based approach for measuring the coordination effect of supply chain systems. We prove that the proposed multiplier DEA approach is equivalent to the envelopment DEA one. The proposed approach in this paper is verified via a numerical example from a supplier–manufacturer sustainable supply chain on resin companies finally.

Efficiency evaluation of homogenous decision making units (DMUs), is one of the primary objectives of data envelopment analysis (DEA), in which the large number of inputs or outputs compared with the number of DMUs leads to decreased evaluation precision of DEA models. Overcoming this problem is the main issue in two-level DEA. In the present study, we use the multi objective linear programming (MOLP) method proposed by Sumpsi et al., which is based on constructing the pay-off matrix and using goal programming (GP), to investigate this problem. Furthermore, we discuss the enhanced Russell's model for evaluating and benchmarking DMUs with two-level inputs and outputs. The proposed method is applied to 24 branches of an Iranian commercial bank.

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

One of the major research streams in data envelopment analysis (DEA) is ranking decision-making units (DMUs). Utilizing a multicriteria decision-making technique, we develop a novel approach to fully rank all units. Motivated by the convex cone-based total order for multiple criteria alternatives proposed by Dehnokhalaji et al. (Nav Res Logist 61(2):155–163, 2014), we consider DMUs in DEA as multiple criteria alternatives and obtain their total ordering. Initially, some pairwise preference information is provided by the decision maker for units and the concepts of convex cones and polyhedral sets are defined in a DEA framework, correspondingly. We apply a modification of Dehnokhalaji et al. method to extract additional preference information for each pair of units and consequently obtain a full ranking (strict total ordering) of DMUs. The benefit of our approach to their method is that we apply non-radial models to overcome the instability drawback of radial models and their infeasibility occurring in DEA applications. The proposed approach is implemented for two numerical examples, and the accuracy of it is investigated through a computational test.

PurposeThis paper aims to present two-stage network models in the presence of stochastic ratio data.Design/methodology/approachBlack-box, free-link and fix-link techniques are used to apply the internal relations of the two-stage network. A deterministic linear programming model is derived from a stochastic two-stage network data envelopment analysis (DEA) model by assuming that some basic stochastic elements are related to the inputs, outputs and intermediate products. The linkages between the overall process and the two subprocesses are proposed. The authors obtain the relation between the efficiency scores obtained from the stochastic two stage network DEA-ratio considering three different strategies involving black box, free-link and fix-link. The authors applied their proposed approach to 11 airlines in Iran.FindingsIn most of the scenarios, when alpha in particular takes any value between 0.1 and 0.4, three models from Charnes, Cooper, and Rhodes (1978), free-link and fix-link generate similar efficiency scores for the decision-making units (DMUs), While a relatively higher degree of variations in efficiency scores among the DMUs is generated when the alpha takes the value of 0.5. Comparing the results when the alpha takes the value of 0.1–0.4, the DMUs have the same ranking in terms of their efficiency scores.Originality/valueThe authors innovatively propose a deterministic linear programming model, and to the best of the authors’ knowledge, for the first time, the internal relationships of a two-stage network are analyzed by different techniques. The comparison of the results would be able to provide insights from both the policy perspective as well as the methodological perspective.

PurposePolice organizations' main goal is providing the security of the neighborhoods for citizens. This paper aims to investigate how efficiently the police stations in the city of Ankara work. It also seeks to determine how the efficiency of police organizations can be measured by using data envelopment analysis (DEA).Design/methodology/approachThe study uses DEA to measure the efficiency of police stations in the city of Ankara in Turkey. DEA measures the relative efficiency of decision‐making units (DMUs) based on their inputs and outputs.FindingsThe results showed that ten of 19 police stations in Ankara were efficient. In other words, 52.6 percent of police stations were found to be efficient.Research limitations/implicationsAll police stations in Ankara could not be included in the study. Since DEA analysis the relative efficiency of DMUs, including all police stations in Ankara, may have given a broader perspective for police managers.Practical implicationsInefficient police stations in this study should either reduce the number of incidents that occurred in the precinct or solve those crimes and increase the number of solved incidents. Preventing crimes may be achieved by employing community policing strategies, thus, police managers should focus on adopting community policing strategies.Originality/valueThe results of the DEA analysis can be used to help police stations in Ankara to be more efficient. Furthermore, applying DEA may be a new approach to measuring the efficiency of police services.

Energy efficiency analysis based on DEA integrated ISM: A case study for Chinese ethylene industries

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

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.

Free disposal hull efficiency scores of units with network structures

Operational and non-operational performance evaluation of thermal power plants in Iran: A game theory approach

Conventional applications of data envelopment analysis generally treat the Decision-Making Unit (DMU) as a black box in that the internal processes are not examined in detail. In some situations, such as the measurement of performance of a set of supply chains, the DMU can be viewed as exhibiting a network structure. A significant body of recent literature has examined a particular form of network structure, namely, where the DMU is a two-stage serial process in which the outputs from the first stage are intermediate variables that serve as inputs to the second stage. The current article extends this idea to include those situations where outputs from the second stage can be fed back as inputs to the first stage. Such feedback variables thus serve a dual role. Models are developed for examining performance in this feedback setting and are illustrated using an application involving the measurement of performance of a set of Chinese universities.

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.

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.