Inefficient Decision Making Units Research Articles

EFFICIENCY MEASUREMENT OF ARTIFICIAL INTELLIGENCE: A RESEARCH ON COMPANIES IN TÜRKİYE

The use of technology is increasing due to Industry 4.0. Both countries and organizations have had to invest in the field of artificial intelligence (AI) to compete with their rivals in global competitive conditions and to adapt to the ever-changing world. An organization or a country needs to evaluate its performance to ensure its sustainability constantly. The Data Envelopment Analysis (DEA) method is widely used in performance evaluation. This study aimed to evaluate Türkiye AI performance for the nine years between 2014 and 2022. In the research, years were included in the analysis as the decision-making unit. Two input and two output variables were used in the analyses. The study was carried out by using the input-oriented CCR DEA model and its super-efficiency model. According to the results of the analysis, efficient/inefficient decision-making units were determined. Several potential improvement suggestions have been put forward for inefficient decision-making units.

Finding alternative stepwise improvement paths with different improvement effects

Targets on the data-envelopment-analysis (DEA) efficient frontier show the direction of inefficient decision-making units’ (DMUs) improvements. Some authors have correlated the improvement effects of targets with management needs. Nevertheless, targets are unattainable for DMUs with poor performance. Hence, stepwise improvement paths with different improvement effects should be provided to solve the problem of unachievable targets and flexibly meet decision-makers’ (DMs) diverse management needs in performance improvement. For this purpose, we propose novel stepwise DEA benchmarking approaches and a search algorithm to find progress-improvement strategies with different improvement effects. The contributions of our approaches are as follows. (1) Several stepwise improvement paths with different improvement effects are found, which provides DMs with more abundant alternatives for performance optimization in various management situations. (2) The upper bound for efforts in all intermediate improvement steps can be effectively controlled, which overcomes the problem of unachievable targets and makes stepwise improvement paths more feasible. To illustrate our approaches, we conducted a case study on 18 ports in China. In this case study of Chinese ports, we further highlighted the advantages of our methods by comparing them with existing DEA models. In sum, our approaches are suitable for poorly performing units that aspire to align performance improvements with management needs.

Interval efficiency estimation using relational dynamic DEA approach: case of Indian banks

Data envelopment analysis (DEA) facilitates relative performance estimation of homogenous decision-making units (DMUs), whereas dynamic DEA pertains to intertemporal elements like carryovers for measuring DMUs’ efficiencies over time. To enhance DMU’s performance, there is a need to measure its efficiency with dynamic structure and further to determine input-output target points for making significant improvements in an inefficient DMU. Therefore, the present study proposes a relational dynamic DEA approach that comprises a dynamic structure in which periods are connected through good and bad links/carry-overs along with desirable and undesirable outputs and utilizes interval data. The system and period efficiency intervals are derived using a unique set of weights based on common weights methodology. Moreover, the relationship between the complement of the lower (upper) bound system and period efficiencies is established. Lastly, the input-output target points are suggested to improve DMUs in terms of upper-bound system efficiencies in a dynamic environment. To validate the usefulness of the proposed approach, a case study in Indian banks for the period 2017– 2021 is presented. This study investigates the impact of stressed assets (bad link/carryover) and loss due to non-performing assets (bad/undesirable output) on banks’ dynamic efficiency. The results are compared with the static structure. The findings, targets, and implications of the study can assist bank experts and policymakers in formulating policies/strategies for further improvements.

Circular economy efficiency in the context of waste management in the selected Central and Eastern European countries – evidence from DEA and fractional regression analysis

Sustainability and optimizing waste management efficiency through circular economy principles have become significant trends in recent research. This study aims to compare the circular economy efficiency within the waste management framework in selected Central and East European countries. To achieve this, the research employs Data Envelopment Analysis (DEA) and Fractional Regression Analysis, specifically using the Slack-Based Measurement model. This model measures efficiency by referencing the inefficient Decision-Making Units (DMUs) rather than the origin of the coordinate system. Key variables such as GDP per capita, GDP growth rate, and circular economy indicators were selected and analyzed using Eurostat data from 2010 to 2022. The findings reveal varying potentials for improving the efficiency of hazardous and non-hazardous waste management in the countries investigated. Additionally, differences in the mutual influence of selected variable indicators on waste efficiency are identified using Fractional Regression Analysis. These insights emphasize the pivotal role of waste management in advancing circular economy policies.

Determining the equilibrium efficient frontier by proportional frontier shifting for data envelopment analysis with fixed-sum outputs

The equilibrium efficient frontier data envelopment analysis (EEFDEA) has been extensively used to evaluate efficiencies of the decision-making units (DMUs) with fixed-sum outputs. This study develops a new EEFDEA approach based on a proportional frontier-shifting strategy. Our approach applies an iterative procedure to find the equilibrium efficient frontier (EEF). Each iteration uses a proportional frontier-shifting model to improve an inefficient DMU to the efficient frontier by increasing its fixed-sum outputs. Meanwhile, the DMUs on the efficient frontier decrease fixed-sum outputs proportionally to ensure the total fixed-sum outputs are unchanged. Our theoretical developments show that the proportional frontier-shifting strategy is feasible and can finally obtain a unique EEF. The new approach allows DMUs to use their preferred input and output weights when determining the EEF. This generates an EEF that better aligns with real-world practices and avoids the need to construct it as a single hyperplane, as required by conventional EEFDEA methods. It also avoids unfair adjustments in fixed-sum outputs among the DMUs and eliminates the problem of peculiar efficiency evaluation results (i.e., some DMUs obtain extremely high, or infinity, efficiencies). Finally, we apply our approach to a case study of Chinese vehicle industry companies to demonstrate its usefulness and compare it with the previous representative approach.

An Innovative Double-Frontier Approach to Measure Sustainability Efficiency Based on an Energy Use and Operations Management Perspective

China’s economic development has achieved great success in recent years, but the problems of energy scarcity and environmental pollution have become increasingly serious. To enhance the reliability and efficiency between energy, the environment and the economy, sustainable development is an inevitable choice. In the context of measuring sustainability efficiency, a network data envelopment analysis model is proposed to formulate the two-stage process of energy use and operations management. A double frontier is derived to optimize the available energy for sustainable development. Due to nonlinearity, previous linear methods are not directly applicable to identify the double frontier and calculate stage efficiencies for inefficient decision-making units. To address this problem, this study develops the primal-dual relationship between multiplicative and envelopment network models based on the Lagrange duality principle of parametric linear programming. The newly developed approach is used to evaluate the sustainability efficiency of 30 administrative regions in China. The results show that insufficient sustainability efficiency is a systemic problem. Different regions should take different measures to conserve energy and reduce pollutant emissions for sustainable development. To increase sustainability efficiency, regions should support energy-saving and emission-reducing technologies in production processes and strengthen their capacity for technological innovation. Compared with energy use efficiency, operations management efficiency in China has a wider range of changes. During the operations management stage, there is not much difference between the capacity and quantity of each region. Based on benchmark regions at the efficiency frontier, there is an opportunity to improve operations management in the near future. Blockchain technology can effectively improve energy allocation efficiency.

Improving technical efficiency in data envelopment analysis for efficient firms: A case on Chinese banks

Data Envelopment Analysis (DEA) as a data-oriented benchmarking tool is considered a powerful and promising instrument for performance evaluation in various application areas. In DEA, the set of all decision-making units (DMUs) is divided into efficient and inefficient subsets. Inefficient DMUs are improved by reducing the input and/or increasing the output, and as far as we know, efficient DMUs are abandoned with the conclusion that they are all technically and relatively efficient, and no further analysis has been suggested in the literature. In this article, we first show that there is a gap between the actual efficiency and the efficiency estimated using benchmarking tools such as DEA. This means that there is no guarantee that the efficient DMUs characterized by DEA are really efficient. Thus, there is a gap in improving the technical efficiency of efficient DMUs. In this paper, we attempt to close this gap by introducing a method to improve efficient DMUs. First, we introduce a random variable as a corrector of efficiency evaluation, and then an inverse DEA model (IDEA) is proposed to improve efficient DMUs. To demonstrate the actual applicability of the proposed approach, we present an illustrative empirical application using 106 Chinese bank data from 2021.

A cutting-edge data envelopment analysis model for measuring sustainable supplier performance like never before

One of the challenges for suppliers is to increase their market share due to the limited target market. In other words, in the supply chain, the demand for suppliers' products is limited. Therefore, suppliers produce a determined share of the required amount by producers. However, when it comes to the share in the amount of output among suppliers, the output of suppliers in this indicator is interdependent. In the classical data envelopment analysis (DEA) models, there are no models that assess the suppliers according to the dependence of at least one output on each other. In this paper, a model is presented that can assess sustainable suppliers in the presence of interdependent output among suppliers by using DEA models and their extension. It can also determine the total benchmarks of decision making units (DMUs) in such a way that satisfies the interdependent output constraint. In other words, benchmarks are not determined independently. Ultimately, an approach is presented to determine efficient projections for inefficient DMUs by considering the concept of interdependent output. To represent the applicability of our proposed model, a dataset for two consecutive years including 32 sustainable suppliers with consideration of interdependent output has been implemented using the model presented in this paper. The resulting sustainability has been compared with a classical model.

A hybrid stochastic data envelopment analysis and decision tree for performance prediction in retail industry

Assessing the retail industry's efficiency is pivotal for economic growth and corporate productivity. This study employs a novel approach, utilizing a regression-based Stochastic Data Envelopment Analysis (SDEA) model, Balanced Scorecard (BSC), and Decision Tree. The integration of these methods is a pioneering effort in the retail sector. This is a data-driven decision-making framework, aiding managers in predicting efficient and inefficient Decision-Making Units (DMUs). Results from a case study in 44 retail store chains in Iran indicate that the accuracy of the SDEA model is 99%. The Decision Tree highlights low branch efficiency due to a low customer count, a unique finding in comparison to prior studies.

Allocating Benefits Due to Shared Resources Using Shapley Value and Nucleolus in Dynamic Network Data Envelopment Analysis

Shared resources are common among supply chain partners and also occur in multiple linked stages of an internal network. The sharing of these resources impacts the organization’s profits. This study is focused on the potential benefits of resource sharing on a three-stage network system and on the profit improvement allocation. Previous treatments concentrate on defining optimal proportions to allocate resources and disregard the impacts of allocations to promote cooperation and are limited to static evaluations. Data Envelopment Analysis performs the decision-making units (DMUs) efficiency measurement. Methodological advances have resulted in models that analyze their internal structure and temporal impacts on efficiency. We propose an integrated cooperative game and dynamic network DEA that considers known quantities of resources used in each stage and the time effects to optimize the system’s profit. Each DMU stage is a player, and we investigate performance before and after resource sharing. Using Shapley value and Nucleolus, it is possible to allocate the benefits obtained based on the marginal contributions of each stage, providing incentives to motivate and maintain cooperation. A numerical example is used to illustrate the method. The results confirm the identification of inefficient DMUs and that sharing resources allows for profit increase for all of them.

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.

Maximum slacks-based measure of efficiency in network data envelopment analysis: A case of garment manufacturing

The conventional slacks-based measure (SBM) model is widely applied to measure the relative efficiency of a set of decision making units (DMUs) because the measured efficiency is not affected by weakly efficient frontiers. This model seeks the target point on the production frontier that is farthest to the assessed DMU to calculate efficiency, which requires more effort for the DMU to reach to become efficient. This paper develops a model to calculate the maximum SBM efficiency for general network production systems. Different from the conventional minimum SBM efficiency that is always less than the radial efficiency for inefficient DMUs, the maximum SBM efficiency can be greater than the radial efficiency. The measured efficiency satisfies the monotonicity property. The efficiencies of the component divisions of the network system can also be calculated at the same time. A garment manufacturing company whose major operations comprise cutting and sewing is studied using the developed model. The results show that the average maximum SBM efficiency is 35 % higher than the average minimum SBM efficiency and the sewing operation contributes more to the system efficiency than the cutting operation does.

Program evaluation and review technique – Data envelopment analysis in benchmarking sustainable supply chain management of the potato chips industry

Supply chain benchmarking of agroindustry can be done by emphasizing the perspective of sustainability. This paper aimed to analyze the efficiency of Sustainable Supply Chain Management (SSCM) in Micro, Small, and Medium Enterprises (MSMEs) and provided a prospective benchmark with the potato chips industry as a study case. Program Evaluation and Review Technique (PERT) estimated future input and output values to obtain prospective benchmarks and be added to the DEA formula later. Analytical Hierarchy Process (AHP) and Data Envelopment Analysis (DEA) methods were used to measure SSCM performance. The results showed that 17 of 20 Decision-Making Units (DMUs) were efficient (score 1). The other 3 DMUs were classified and got an efficiency score of 0.965, 0.955, and 0.995. In future estimates calculation, the efficiency score of one of the inefficient DMUs has changed to 1, while the other two got the scores of 0.996 and 0.980. The limitation of this research mainly comes from the scope of assessment, which is limited to the supply chain’s downstream sector and assesses only a limited number of MSMEs in a particular region. SSCM efficiency measurement variables were adjusted to be assessed and applied to MSMEs. PERT was also beneficial to gain future estimates of the assessment scope.

Data envelopment analysis of two‐stage processes: an alternative (non‐conventional) approach

AbstractNetwork data envelopment analysis (NDEA) is an extension of standard DEA that models the efficiency assessment of decision‐making units (DMUs) by considering their internal structure. While in standard DEA, the DMU is regarded as a single process, in NDEA, the DMU is viewed as a network of interconnected sub‐processes (stages, divisions), where the flow of the intermediate products (measures) is essential in the efficiency assessment. In the prevalent conventional methodological approach to NDEA, the sub‐processes are assumed as distinct entities with distinct inputs and outputs. Thus, each sub‐process has its own production possibility set (PPS), which can be derived axiomatically from a set of assumptions using the minimum extrapolation principle. The PPS of the overall system is defined as the composition of the individual PPSs. The conventional approach comprises all the methods, in which the system and the divisional efficiencies are computed jointly in a single mathematical program. A fundamental property connecting the system with the divisional efficiencies is that a system is overall efficient if and only if its divisions are all efficient. In NDEA, regardless of the method used, there are cases where none of the observed DMUs is rendered overall efficient as corroborated by real‐world case studies. This is the main issue we discuss in this paper and the motivation to propose an alternative, non‐conventional, approach to address it in the frame of two‐stage processes. We consider the two‐stage process as a system that can be viewed from two perspectives depending on the role of the intermediate measures: the system as producer and as consumer of the intermediates. As our approach is based on standard DEA, it satisfies the basic desirable properties. The fundamental NDEA property, that the overall system is efficient if and only if both perspectives are efficient, is met. The efficient frontier of the system is explicitly defined by the overall efficient observed DMUs, and the inefficient DMUs are projected on the efficient frontier of the system. A convergent procedure is presented for this purpose. The proposed models are equivalently expressed in both the multiplier and the envelopment forms due to strict primal‐dual correspondence and can operate under both constant and variable returns‐to‐scale assumptions. We use the case of 22 automotive manufacturers for the fiscal year 2019 as an example to illustrate our approach. Comparison with other NDEA methods is also provided.

Stepwise benchmarking based on production function: Selecting path towards closest target

Data Envelopment Analysis (DEA) is used for not only assessing the performance of a set of homogeneous decision-making units (DMUs), but finding the target operating points to improve inefficient DMUs. When inefficient DMUs are far from their target, it will be impossible to reach those in a single move, therefore, a strategy of gradual improvements with intermediate targets has been proposed. In this study, we propose a method to determine benchmarking paths, which consider the improvement steps as the learning of the production functions, not the learning of inputs and outputs. Under the constant returns-to-scale (CRS) assumption, the paper, for each inefficient DMU, sets the ultimate target as the closest target and determines the shortest improvement path with existing DMUs by Dijkstra’s algorithm, and this is illustrated with a numerical experiment. As a real-world example, the method is introduced to the irons and steel enterprises in China, so that the specific steps to make efficient ones has been pointed out. The proposed method is the first study to discuss stepwise benchmarking path from the perspective of the production function.

Port environmental efficiency assessment using the one-stage and two-stage model DEA: comparison of Koper and Dublin ports

Ports are important and central hubs for logistical activities in transportation chains that contribute to economic growth. Nevertheless, port activities are associated with undesirable impacts such as energy consumption and air pollutant emissions. Various policy instruments and measures have been developed and adopted to reduce energy consumption and emissions from ports. In an effort to manage all undesirable impacts of port operations, the search for best practices has proven to be an effective approach. This study examines one- and two-stage Data Envelopment Analysis (DEA) models for measuring port environmental efficiency. The adequacy of the models was analyzed using two case studies, i.e., the port of Koper and port of Dublin. The results of the study provided the best practices and the main differences between one-stage and two-stage models. Distance metrics were used to identify the necessary improvements of inefficient decision-making units (DMUs) to achieve the best practices.

Measuring Efficiencies of Dairy Buffalo Farms in the Philippines Using Data Envelopment Analysis

This study aimed to measure the efficiency scores of 75 dairy buffalo farms in the province of Nueva Ecija, Central Luzon, Philippines, using an input-oriented, variable-return-to-scale Data Envelopment Analysis (DEA) model. The farmer-informants or decision-making units (DMUs) were categorized as smallholders, family modules, and semi-commercial in operations. Personal interviews using structured questionnaires were done to gather various information on the socio-economic and management practices of the DMUs. Output in the form of volume and value of milk produced and inputs such as quantities and costs of biologics, feeds, forage, and labor were also collected and evaluated among individual DMUs. The efficiency scores were computed using PIM-DEA software, which identified fully efficient DMUs lying on the frontier line (scores of 1.0) and those enveloped by it (inefficient DMUs with scores of less than 1.0). The overall mean Technical Efficiency (TE), Allocative Efficiency (AE), and Economic Efficiency (EE) scores among the DMUs were 0.80, 0.81, and 0.65, respectively. Most of the inefficient DMUs were in the smallholder category. In sum, smallholder DMUs classified under low and moderate TE clusters should reduce their inputs by 53.31% and 40.01%, respectively, to become fully efficient. Likewise, higher lambda values among efficient peer DMUs indicate the best practice frontiers that the inefficient peer DMUs can benchmark with. Extension and advisory services can help promote the best management practices of the frontiers to improve the TE, AE, and EE of the inefficient DMUs.

How to improve the future efficiency of Covid-19 treatment centers? A hybrid framework combining artificial neural network and congestion approach of data envelopment analysis

Covid-19 virus (severe acute respiratory syndrome coronavirus 2, SARS-CoV-2) threatens the health of human beings worldwide, imposing a concern for the world and prompting governments to control the contagion. Although vaccination is a proper tool to control the transmission, the efficient allocation of limited health-care resources to massive patients can improve the effectiveness of medical services. Relying on the Artificial Neural Network (ANN), the aim of this research is to enhance the future efficiency of Covid-19 treatment centers by forecasting their efficiency and providing benchmarks. To do this, we use the congestion approach of data envelopment analysis (DEA) based on the theory of economies of scale principles. In the traditional input-oriented DEA, inefficient decision-making units (DMUs) can become efficient merely by reducing the inputs. However, this may not always be true in real-world applications such as improving the efficiency of COVID-19 treatment centers (DMUs). Meaning that the treatment centers with less congested inputs (e.g., ventilators, test equipment, pulmonologists, and nurses, etc.) normally have higher mortality rates. For this reason, in this study, we take the congested inputs approach into account to provide proper benchmarks for the inefficient treatment centers. According to the congestion approach of DEA, an optimum increase in congested inputs can lead to a greater than a proportional increment in outputs. In other words, if more respiratory equipment, pulmonologists, patient rooms, nurses and beds, etc. are allocated to Covid-19 treatment centers, not only the number of deaths (undesirable outputs) are decreased, but also the number of recoveries (desirable outputs) are increased. Such an optimal rise in the congested inputs is determined in pairwise comparisons derived from the model. Accordingly, in this study, first, considering the congestion approach of DEA and historical data of five periods, we identify the initial efficiency of Iranian Covid-19 treatment centers. Then, by running ANN, we forecast the future inputs and outputs, the overall efficiency, and rank of the treatment centers. By doing this, the prospective efficient and inefficient DMUs are identified, and appropriate benchmarks are determined.

Wood harvesting efficiency analysis of regional forest directorates in Turkey: k-means clustering and data envelopment analysis approach

ABSTRACT Wood harvesting is a crucial element of sustainable forest management. Harvesting of wood and non-wood forest products has gained importance for increasing environmental services, economic development and sustainability of forests. The aim of this study was to reveal the efficiency values of regional forest directorates (RFD) for wood harvesting between 2015 and 2020 in Turkey using six input and two output variables. Data envelopment analysis (DEA) and k-means clustering analysis were used. From the results obtained, 35.7% of the 28 RFDs were efficient using the results of the input- and output-oriented Charnes, Cooper, and Rhodes (CCR) model. In the input-oriented CCR model, for inefficient decision-making units (DMUs) to become efficient, on average the variable that needs to be reduced the most is forest area, and the variable that needs to be reduced the least is allowable cutting. Conversely, in the output-oriented CCR model, an average potential increase of 23.84% in the harvest of industrial wood, and of 29.48% for fuelwood, was determined for the inefficient DMUs. In addition, according to the results of the k-means cluster analysis, it was found that the DMUs in the cluster distribution of inefficient DMUs were similar to each other in terms of location. It will make a positive contribution for inefficient DMUs to consider the efficient DMUs shown as references to reach the targeted input/output values to become efficient. In addition, the results of this study are important for enabling forest managers to make performance evaluations based on efficiency analyses.

Stability analysis with general fuzzy measure: An application to social security organizations.

An effective method for evaluating the efficiency of peer decision-making units (DMUs) is data envelope analysis (DEA). In engineering sciences and real-world management problems, uncertainty in input and output data always exists. To achieve reliable results, uncertainties must be taken into account. In this research, a General Fuzzy (GF) approach is designed to cope with uncertainty in the presence of fuzzy observations for categorizing and specifying stability radius and alterations ranges of efficient and inefficient DMUs, which is applicable to real-world decision-making problems. For this purpose, a DEA sensitivity analysis model is presented, which will be modeled by fuzzy sets. Then, by applying the General Fuzzy (GF) approach, the fuzzy DEA sensitivity analysis model is transformed into the equivalent crisp form of fuzzy chance constraints according to specific confidence levels. Finally, a numerical example and a case study of branches of the social security organization are presented to illustrate sensitivity and stability analysis in the presence of fuzzy data. The obtained results provide the input and output changes of the evaluated units according to the attitude and preference of the decision maker with different confidence levels so that the data changes in the fuzzy environment do not change the units’ classification from efficient to inefficient and vice versa.