Decision Making Units Research Articles

Advancing Efficiency Sustainability in Poultry Farms through Data Envelopment Analysis in a Brazilian Production System.

The production efficiency factor is widely used to measure the zootechnical performance of a batch of broilers. The unit cost of production brings new elements to improve efficiency evaluation and financial sustainability for this activity in agriculture. This research aims to evaluate the production efficiency level of the crop to maximize the return on investment. This study uses Data Envelopment Analysis (DEA) with the computational processing of the SIAD software (Integrated Decision Support System). The variables selected were poultry housing, age at slaughter, feed consumed, mortality, and unit cost. The chosen output variable was the total available weight. The analysis spans 31 decision-making units (DMUs) composed of integrated producers, unveiling a frontier of efficiency delineated by the most exemplary DMUs. Notably, only two DMUs, specifically DMU 4 and DMU 23, approached the threshold of maximum relative efficiency. This research illuminates the critical role of unit cost in enhancing the assessment of production efficiency and financial sustainability within the agriculture environment. By setting benchmarks for efficient management and operational protocols, our findings serve as a cornerstone for improving practices among less efficient DMUs, contributing significantly to the literature on agricultural efficiency and offering actionable insights for the poultry farming sector.

Inverse DEA based resource allocation method for nonhomogeneous decision-making units

Inverse data envelopment analysis (DEA) method is a useful tool for analyzing the optimization of resource allocation of a set of homogeneous decision-making units (DMUs). However, the assumption of homogeneity for DMUs is not necessarily applied in reality. For example, universities with the same input structure cultivate students with different orientations; power plants consume different resources to generate electricity. Therefore, how to handle nonhomogeneous DMUs in inverse DEA becomes an issue when analyzing the optimization of resource allocation. In this study, an inverse DEA method for nonhomogeneous DMUs under constant returns to scale (CRS) is proposed to handle the aforementioned issue. Furthermore, the inverse DEA method for nonhomogeneous DMUs is extended to the case of variable returns to scale (VRS). The adjustment range of inputs-outputs under VRS is discussed to avoid the problem of infeasibility, and relevant properties of our method are proved. Moreover, the effect of frontier changes on the proposed method is also discussed. Finally, numerical examples are provided to illustrate the effectiveness of our method.

A DEA game cross-efficiency based improved method for measuring urban carbon emission efficiency in China.

An accurate evaluation of carbon emission efficiency (CEE) at the city level can provide guidelines for understanding low carbon performance, which is crucial to achieving dual carbon targets. Existing CEE studies focused on national, industrial, and provincial scales while neglecting the city level and failing to consider competing relationships among decision-making units in their measurement models. To fill these gaps, this paper introduces the data envelopment analysis game cross-efficiency model (DEA-GCE) to measure urban CEE performance and compares it with the traditional Super-SBM model using the data from 283 Chinese cities between 2006 and 2019. The results show that (1) the DEA-GCE method provided more intensive and stable results. (2) Overall CEE of Chinese cities declined slightly amidst fluctuations during this period. (3) CEE in cities exhibits spatial clustering characteristics. CEE performance in Northeast China has improved, while CEE in Northwest China continues to lag behind. This study introduced an innovative method for calculating urban CEE and conducted an empirical study of 283 Chinese cities, which has implications for formulation of emission reduction policies.

Dynamic DEA with Common Weights: A Case Study of Iranian Airlines

The traditional data envelopment analysis (DEA) models allow each decision-making unit (DMU) to designate the most advantageous weights to maximize its relative efficiency score. Furthermore, for evaluating the performance of DMUs with consecutive processes during specified periods, dynamic data envelopment analysis (DDEA) has received considerable attention from researchers. Considering the dynamic effects, this paper develops a novel dynamic DEA approach based on the analysis of weight restrictions to achieve a common set of weights in dynamic structures. Essentially, the contribution of this research is fourfold: (1) Generating strictly positive weights. (2) Evaluating overall and periodic efficiencies simultaneously. (3) Preventing weight dissimilarity using the proposed Model. (4) Simplifying the comparison and ranking of each unit in a dynamic network nature. To elucidate the details of the proposed approach, the real data set of fourteen Iranian airlines is used to analyze their dynamic performance levels during the period 2018–2020. The results highlight that Iran Air Tour has had the best total efficiency in the years under evaluation. The findings further demonstrate the superiority and applicability of the proposed method in analyzing the dynamic structures with the carryover flows that link the consecutive periods.

Nonparametric efficiency measurement of undergraduate teaching by university size

Conventional data envelopment analysis (DEA) models assume that all decision-making units (DMUs) are homogenous. While higher education institutions (HEIs) of very different sizes challenge the homogeneity of DMUs, DEA studies have paid relatively little attention to university size when assessing the performance of HEIs. This article proposes novel, effective methods for evaluating university performance and identifying useful benchmarks for improving the operations of inefficient performers. Specifically, DEA and cluster analysis (CA) are applied for the evaluation of the performance of traditional Spanish public universities. DEA is utilized to examine the relative performance of these universities in terms of undergraduate teaching output. CA is applied to find similar-in-scale universities prior to the DEA to facilitate peer-groupings. The advantage of this method is that when DMUs are clustered based on their size, one can obtain homogenous groups of units with comparable operating environments. Furthermore, using the meta-frontier framework, this research finds significant evidence that there is an efficiency advantage for medium- and large-sized universities over small ones in providing undergraduate teaching. A bootstrapped, non-parametric meta-frontier approach also verifies this latter result. Some of the factors that contribute to the differences in the relative efficiencies are identified as well.

Measuring the Efficiency of Water Supply Service: Application of Charnes, Cooper Rhodes and Slack-Based Measure Models

Measuring the efficiency of water supply services is very important, as the demands for water supply are increasing as the year goes by due to increasing population and development. Improving the efficiency of water supply services is also an economically viable way for water conservation. This paper examines the efficiency of water supply services using Charnes, Cooper, and Rhodes (CCR) output-oriented and Slack-Based Measure (SBM) models. SBM provides the input excesses and output shortfalls for the concerned decision-making units (DMUs). Meanwhile, the CCR output-oriented model interprets the efficiency by maximising the output produced by the DMUs whilst maintaining the input used. The results indicate that the optimal efficiency value for SBM model is lower than the optimal efficiency value of the CCR model. Furthermore, the SBM model provides the water supply providers with values for input excess and output shortfalls for future improvements.

Cross-efficiency aggregation based on interval conditional entropy: An application to forest carbon sink efficiency

Cross-efficiency evaluation is an extension of data envelopment analysis (DEA), which can effectively distinguish between decision-making units (DMUs) through self- and peer-evaluation. The cross-efficiency of each DMU in a set of DMUs is measured in terms of intervals when the input–output data are represented by the number of intervals. Based on the interval cross-efficiency matrix, the interval entropy is defined in terms of the likelihood. Then, considering the influence of peer evaluation, the interval conditional cross-efficiency entropy is proposed and an aggregation model of the interval conditional cross-efficiency entropy is presented to create a ranking index for DMUs. Finally, a simple example is provided to illustrate the effectiveness of the proposed method, which is applied to the evaluation of forest carbon sink efficiency in China. The results indicate that the final cross-efficiencies of all 30 provinces range from 0 to 0.6. Among these provinces, those with a relatively high efficiency include Guangdong, Guizhou, Hainan, Shandong, and Qinghai.

A Bilevel DEA Model for Efficiency Evaluation and Target Setting with Stochastic Conditions

The effective allocation of limited resources and the establishment of targeted goals play a pivotal role in enhancing the overall efficiency of large enterprises and organizations. To achieve optimal organizational efficiency, managers seek dynamic strategies that adapt to the constraints of limited and uncertain historical data. This paper introduces an evaluation of organizational efficiency through a stochastic framework, employing a bilevel data envelopment analysis (DEA) approach. This decision-making process is centralized within a decision-making unit (DMU) overseeing subordinate decision-making units (subDMUs). Discrete scenarios, each associated with a realization probability, define the uncertain parameters in the bilevel DEA-based model. This stochastic approach allows for recourse actions upon scenario realization leading to an enhanced overall organizational strategy. Decision-makers acting within uncertain and dynamic environments can benefit from this research since it allows the investigation of efficiency assessment under alternative scenarios in the presence of volatility and risk. The potential impact of applying this methodology varies depending on the specific domain. Although, the context of this paper focuses on banking, in general, enhancing resource allocation and target setting under stochasticity, contributes to advancing sustainability across all its three dimensions (economic, environmental, social). As mentioned earlier, the practical application of our approach is demonstrated via a case study in the banking sector.

Malmquist–Luenberger Productivity Index for a Two-Stage Structure in the Presence of Undesirable Outputs and Uncertainty

Network Data Envelopment Analysis (NDEA) models assess the processes of the underlying system at a certain moment and disregard the dynamic effects in the production process. Hence, distorted efficiency evaluation is gained that might give misleading information to decision-making units (DMUs). Malmquist–Luenberger Productivity Index (MPI) assesses efficiency changes over time, which are measured as the product of recovery and frontier-shift terms, both coming from the DEA framework. In this study, a form of MPI involving network structure for evaluating DMUs in the presence of uncertainty and undesirable outputs in two periods of time is presented. To cope with uncertainty, we use the stochastic p-robust approach and the weak disposability of Kuosmanen (American Journal Agricultural Economics 87 (4):1077–1082, 2005) proposed to take care of undesirable outputs. The proposed fractional models for stages and overall system are linearized by applying the Charnes and Cooper transformation. Finally, the proposed models are applied to evaluate the efficiency of 11 petroleum wells to identify the main factors determining their productivity, utilizing the data from the 2020 to 2021 period. The results show that the management of resource consumption, especially equipment and capital, is not appropriate and investment is inadequate. Although the depreciation rate of capital facilities in this industry is high, the purpose of the investment is not to upgrade the level of technology.

Multistage Malware Detection Method for Backup Systems

This paper proposes an innovative solution to address the challenge of detecting latent malware in backup systems. The proposed detection system utilizes a multifaceted approach that combines similarity analysis with machine learning algorithms to improve malware detection. The results demonstrate the potential of advanced similarity search techniques, powered by the Faiss model, in strengthening malware discovery within system backups and network traffic. Implementing these techniques will lead to more resilient cybersecurity practices, protecting essential systems from hidden malware threats. This paper’s findings underscore the potential of advanced similarity search techniques to enhance malware discovery in system backups and network traffic, and the implications of implementing these techniques include more resilient cybersecurity practices and protecting essential systems from malicious threats hidden within backup archives and network data. The integration of AI methods improves the system’s efficiency and speed, making the proposed system more practical for real-world cybersecurity. This paper’s contribution is a novel and comprehensive solution designed to detect latent malware in backups, preventing the backup of compromised systems. The system comprises multiple analytical components, including a system file change detector, an agent to monitor network traffic, and a firewall, all integrated into a central decision-making unit. The current progress of the research and future steps are discussed, highlighting the contributions of this project and potential enhancements to improve cybersecurity practices.

What do you do when your career script runs out? How older workers decide whether and how to sustain their careers

Abstract This article explores executive career decision-making at the later career stages, against a background of precarious employment and increasing longevity, and the consequent need, and difficulty, for older workers to sustain their careers for longer. We address a gap in the literature on the careers of later stage workers by exploring older executives’ career options and demonstrating how a decision-making unit (DMU) of stakeholders actively participate in choosing between options, and which factors influence their choices. We furthermore highlight the key factors that can determine whether a career is sustainable, and the strategies that can optimize a career that is becoming unsustainable. We interviewed later stage executives and analyzed our interview data from the perspective of distributed decision-making for sustainable careers. We identified a range of decision-makers who contributed to the DMU at different levels for each decision, from proactively to reactively. Furthermore, we identified interacting strategies which could optimize the sustainability of a later stage career: personal, when individuals re-engaged actively with their careers; and contextual, when careers were reinvented by individuals and organizations. We synthesized these findings in a career sustainability matrix, designed to enable later stage workers to identify whether a career is sustainable, and how to extend career sustainability.

Efficiency of research in various fields: Evidence from Indonesia

Abstract This research was carried out to analyze the efficiency of research in Indonesian universities and explore performance differences between knowledge areas. Data Envelopment Analysis (DEA) was used to evaluate the efficiency based on the data of 47 universities in the Mandiri cluster between 2014 and 2018 and 11 research fields including 10 National Research Priority in Indonesia and one field of mathematics and natural sciences. For this assessment, a total of two inputs and five different outputs were defined. The results showed that the maritime sector consistently had an efficiency value below 1. In addition, the variable, number of employees had more influence on the efficiency value than budget. A significant degree of variety was observed in the difference between the lowest and highest efficiency for each research subject at the Decision-Making Unit (DMU) level.

Nash Bargaining Game Enhanced Global Malmquist Productivity Index for Cross-Productivity Index

The Global Malmquist Productivity Index (GMPI) stands as an evolution of the Malmquist Productivity Index (MPI), emphasizing global technology to incorporate all-time versions of Decision-Making Units (DMUs). This paper introduces a novel approach, integrating the Nash Bargaining Game model with GMPI to establish a Cross-Productivity Index. Our primary objective is to develop a comprehensive framework utilizing the Nash Bargaining Game model to derive equitable common weights for different time versions of DMUs. These weights serve as a fundamental component for cross-evaluation based on GMPI, facilitating a holistic assessment of DMU performance over varying time periods. The proposed index is designed with essential properties: feasibility, non-arbitrariness concerning the base time period, technological consistency across periods, and weight uniformity for GMPI calculations between two-time versions of a unit. This research amalgamates cross-evaluation and global technology while employing geometric averages to derive a conclusive cross-productivity index. The core motivation behind this methodology is to establish a reliable and fair means of evaluating DMU performance, integrating insights from Nash Bargaining Game principles and GMPI. This paper elucidates the rationale behind merging the Nash Bargaining Game model with GMPI and outlines the objectives to provide a comprehensive Cross-Productivity Index, aiming to enhance the robustness and reliability of productivity assessments across varied time frames.

Inverse Data Envelopment Analysis Model to Improve Efficiency by Increasing Outputs

Inverse Data Envelopment Analysis (IDEA) has gained significant attention among researchers as an analytical tool for assessing efficiency. Estimating input values while ensuring cost efficiency through changes in output quantities is complex and sensitive. In contrast to conventional data envelopment analysis methods, IDEA enables the quantification of input/output variations resulting from output/input reductions or expansions while preserving the measurement efficiency level. This paper aims to introduce a novel approach for estimating input values by incrementally increasing the value of each output of decision-making units (DMUs) during the evaluation process, thereby maintaining or improving cost efficiency. By utilizing IDEA and manipulating the output values of the DMU under evaluation, the input values are estimated while ensuring constant or enhanced cost efficiency. A simple numerical example and a case study from a Turkish automotive company are presented to validate the proposed method. The obtained results demonstrate significant improvements and hold promising prospects, indicating the potential applicability of this approach to other similar problems and research areas.

Efficiency of funding to rural revitalization and regional heterogeneity of technologies in China: Dynamic network nonconvex metafrontiers

How to assess the performance of funds to support the rural revitalization is closely concerned by both academics and industries. In this paper, taking agricultural loans and financial allocations as inputs, we open the dynamic internal structure of the funding to support the rural revitalization system (FSRRS), including the agricultural support funds system (ASFS) and the rural revitalization system (RRS). With the consideration of regional heterogeneity, we propose the dynamic network data envelopment analysis (DEA) models based on convex and nonconvex metafrontiers to measure the metatechnology-specific technical efficiency (MTE), group-specific technical efficiency (GTE) and technology gap ratio (TGR) of the overall system and subsystems. The proposed models are empirically applied to assess the efficiency of funding to support rural revitalization and regional heterogeneity of technologies for 31 provinces in China during 2017–2020. The results show that: (i) compared to the nonconvex metafrontier model, its convex counterpart underestimates the efficiency of each decision making unit (DMU), resulting in the unfairness of evaluation, in particular for the central and western regions; (ii) there is a difference in the efficiency of the overall system and the subsystems, where the RRS performance is superior to the ASFS performance, implying that the inefficiency of FSRRS is mainly attributed to the low utilization of agricultural support funds; (iii) the period efficiency of the overall system and subsystems show an increasing trend over time, and the magnitudes of increases are different across provinces (regions); (iv) the TGRs of various provinces (regions) exhibit a significant heterogeneity.

Efficient Human Resource Allocation in Projects Using Centralized Data Envelopment Analysis

Human resource allocation is a critical component of project management, especially for projects that rely on the knowledge and expertise of skilled personnel. Data Envelopment Analysis (DEA) is a suitable method for this allocation since teams working for different tasks of a project can be considered as Decision Making Units (DMUs) that produce valuable outputs using different types and levels of personnel (inputs). We use Centralized DEA models for efficient allocation of human resources of the overall project among several teams. Using the software teams of a project undertaken in a large technological defense company as DMUs, we define appropriate outputs that measure effort, importance of work, and schedule performance. Factors relevant to the importance of work are aggregated using Rank Order Centroid weights according to expert evaluations. We differentiate between the permanent personnel of the company by their expertise level, and also subcontractors as different inputs. We define staffing policies with varying flexibilities that can be applied in different time periods, and suggest the transfer or removal of some personnel. Our results demonstrate that a centralized view on human resource allocation can bring noteworthy savings, especially in the long term when there is higher flexibility in staffing decisions.

Performance optimization of fourth-generation biorefinery departments: A novel mixed macroergonomics-sustainability framework

Today, crises caused by environmental pollution such as climate change have emerged as fundamental challenges with far-reaching and sometimes irreparable effects on the environment. Fourth-generation biorefineries (FGBs) have been recognized as a primary solution to these crises. There is a need to establish a comprehensive framework that can evaluate and enhance the performance of various departments in FGBs. This study introduces an integrated framework for evaluating and enhancing the performance of FGB departments, focusing on macroergonomics and sustainability indicators. First, the relative importance of each indicator was calculated using the best-worst method. The necessary data were collected using a validated questionnaire, and factor analysis was employed to assess its validity. The efficiency score of each decision-making unit was then computed using data envelopment analysis (DEA). Principal component analysis (PCA) was performed to validate the DEA results. In the next step, the positive or negative effects of each indicator on the performance of the refinery departments were investigated using sensitivity analysis and statistical tests such as Kolmogorov-Smirnov, Levene, paired t-test, and Wilcoxon. The results of sensitivity analysis indicated that the FGB had the highest performance in reducing injury rates but the lowest in reducing lost workdays caused by injuries or illnesses. In addition, appropriate strategies were developed using Strengths, Weaknesses, Opportunities, Threats (SWOT) analysis to improve the performance of the refinery's departments. To the best of our knowledge, this research is the first attempt to assess the qualitative and quantitative performance of FGB departments on the basis of sustainability and macroergonomics indicators.

Congestion: relation between traditional DEA congestion and two-stage processes congestion

Congestion is a kind of inefficiency because it causes the decision-making units (DMUs) to be inefficient and reduces their output. Identifying inefficient DMUs and determining the cause of their inefficiencies has been one of the most important reasons for referring to the internal structure of DMUs and analyzing the effect of intermediate products on subDMUs (or stages) performance. In this paper, to characterize the cause of the DMU’s congestion, we refer to its internal structure as a two-stage network data envelopment analysis (DEA) and decompose the DMU’s congestion into black-box (BB) and two-stage structure congestion. Also, inputs have two separate and simultaneous roles; the black-box’s inputs role and the stage1’s input role, so three congestion types occur. Thus, we sought to analyze the relation between two types of initial inputs congestion, and intermediate products congestion, and express their effects on BB congestion. Finally, we define three congestion definitions and model the relation between two types of input congestion, intermediate products congestion and BB inputs congestion. Finally, a practical example illustrates the proposed method.

Taking a deeper look at the priority of agricultural industry efficiency through the use of data envelopment approach

The research aims to analyze the technical efficiency of the chocolate industry, which has outstanding performance in producing chocolate products in East Java, Indonesia. The research sample includes all small and large-scale chocolate industries in East Java, with 42 Decision Making Units (DMUs). Efficiency research uses Data Envelopment Analysis (DEA), which is usually used in agricultural production research. However, in this research, DEA was used to analyze the efficiency of the chocolate industry making this concept as an empirical novelty. The analysis results show that there are industries operating at the Constant Returns to Scale (CRS) level (0.971), with a percentage of 66.67%, consisting of 28 DMUs, and the Variable Returns to Scale (VRS) level (0.992), with a percentage of 85.71%, which is represented by 36 DMUs. The Technical Efficiency (TE) is categorized as full technical efficiency at 85.72%, with a high category at 14.28%, and no DMUs fall under the moderate and low categories. This indicates that the allocation of inputs in each DMU is significantly different. Six DMUs require improvement in the distribution of industrial capital and raw materials input to achieve full efficiency category by making decisions based on the DMU's recommendations as a reference (benchmarks). This article concludes that industrial efficiency is a priority for the establishment of a business to achieve its goals easily. When the industry can control the quality and quantity of its products, it can be highly beneficial. The policy implications required for this case research to maintain and improve the efficiency of the chocolate industry in East Java, Indonesia, are for industries to prioritize joint management, increase the scale of operations, increase production frequency, expand innovative chocolate processing technology, prioritize product quality, and be able to penetrate export markets.

Železničke stanice u Republici Srbiji u funkciji prevoza robe - efikasnost po sistemu DEA

Introduction/purpose: Data Envelopment Analysis (DEA) is commonly used to calculate the efficiency of similar Decision-Making Units (DMUs), which as such are elements of one set. In the article, it is considered that each such element of a set (of similar elements) is at the same time an element of a system (of various elements). An example of DMUs are 27 railway stations in the Republic of Serbia (RS) as an element of a set of railway stations and as an element of the railway transportation system, in the function of transporting goods, after division of the company Serbian Railways in 2015 (into "passengers" and "goods"). For the sake of better service, attraction and retention of clients, in the newly opened, free, transport market, the purpose of this article is to find the efficiency of the RS stations iin the period of 2018-2022. Methods: Set-systemic-model comparative DEA analysis of railway stations as a DMUs. A unit is an element of the set, a unit is an element of the system, and a unit is the subject of the mathematical DEACCR/BCC/SE model. Results: The final efficiency, the average of all average values, is 0.7666, as a result of a triple comparative DEA analysis: 27 DMU, three DEA models and five years of functioning. Conclusion: Stations are functionally different in terms of efficiency and each station functionally differs by years and by model. The final aim is an input-output balance and the 27/27 option which is achieved with corrective actions - reduction/addition, input or output.