Decision Making Units Research Articles

A novel ranking method in data envelopment analysis: a real case on Chinese banking industry

Purpose This study aims to address the lack of discrimination between fully efficient decision-making units in nonparametric efficiency analysis models by introducing a new ranking technique that incorporates contextual variables. Design/methodology/approach The proposed method combines Data Envelopment Analysis (DEA) and Ordinary Least Squares (OLS). First, DEA evaluates the partial efficiency of each unit, considering all inputs and only one output. Next, OLS removes the influence of contextual variables on the partial efficiencies. Finally, a ranking criterion based on modified partial efficiencies is formulated. The method is applied to data from 100 Chinese banks, including state-owned, commercial and industrial institutions, for the year 2020. Findings The ranking results show that the top six positions are assigned to highly esteemed banks in China, demonstrating strong alignment with real-world performance. The method provides a comprehensive ranking of all units, including nonextreme efficient ones, without excluding any. It resolves infeasibility issues that arise during the ranking of efficient units and ensures uniqueness in efficiency scores, leading to a more reliable and robust ranking process. Contextual variables exerted a greater influence on the first partial efficiency compared to the second. Notably, Total Capital Adequacy (TCA) significantly impact bank efficiency. Originality/value This study introduces a novel ranking method that effectively integrates contextual variables into DEA-based efficiency analysis, addressing limitations of existing methods. The practical application to Chinese banks demonstrates its utility and relevance.

EFFICIENCY OF URBAN PASSENGER TRANSPORT BY BUS: AN APPLICATION TO THE CASE OF CUENCA-ECUADOR

Objective: The study aims to analyze the technical and scale efficiency of public transportation in Cuenca, Ecuador, using Data Envelopment Analysis (DEA). It evaluates both supply and demand aspects. Theoretical Framework: DEA measures the efficiency of decision-making units (DMUs) in using inputs to produce outputs. It's widely used to assess public transportation efficiency by comparing resource usage and service outcomes. Method: The study analyzes 37 DMUs, considering kilometers traveled, number of frequencies, and total fleet as inputs, and operating costs and passenger demand as outputs. Results and Discussion: DEA_Demand showed a 90% technical efficiency and 45% scale efficiency. DEA_Offer showed 94% and 96% efficiency, respectively, highlighting the potential for adjustments in both approaches. Research Implications: The findings help policymakers optimize resource allocation and improve public transport services. Originality/Value: The study uniquely combines supply and demand analysis using DEA in a mid-sized city like Cuenca, offering actionable insights.

Fuzzy task assignment in heterogeneous distributed multi-robot system

This study addresses the problem of coordination in cooperative multi-robot systems performing complex tasks. An analysis of cooperative behavior in mobile multi-robot systems in terms of task execution accuracy by heterogeneous robots is carried out. In addition, we evaluate the capacity and compatibility of tasks assigned to robots to optimize task execution without using direct communication with the robots or a central decision-making unit. A model for task selection in heterogeneous distributed multi-robot systems is proposed. It is based on two processes: the first decomposes complex tasks into elementary tasks, and the second assigns elementary tasks to mobile robots for real-time execution. The distribution of elementary tasks is NP-hard, which leads us to recommend approximate solutions. A fuzzy system called Fuzzy Decision Making in Task Selection is proposed, which uses fuzzy logic to solve this problem. This system allows robots to choose to perform any task in the future. An approach is presented that uses two cascading fuzzy systems. The first calculates the utility of the robot and then activates the second fuzzy system to calculate the utility of the task. By using the output of the fuzzy decision system in our model, each robot will be able to decide for itself which tasks to perform. The results of a simulation of mobile robots transporting goods demonstrate the effectiveness of this fuzzy decision-maker.

A novel FMECA method for CNC machine tools based on D-GRA and data envelopment analysis

Failure Modes, Effects, and Criticality Analysis (FMECA) is a commonly used method for analyzing system reliability. It is frequently applied in identifying weak points in the reliability of CNC machine tools. However, traditional FMECA has issues such as vague descriptions of risk factors, equal treatment of risk factors, and unclear directions for improving weak points. In response to the issue of vague descriptions of risk factors, this paper further expands severity (S) into machine hazard (M) and personal hazard (P), and subdivides detectability (D) into functional structural complexity (D1) and detection cost (D2). In addressing the issue of treating risk factors equally, this paper integrates Distance Analysis Method (DAM) and Grey Relational Analysis (GRA) to propose Distance-Grey Relational Analysis (D-GRA). Subsequently, based on the D-GRA method, the weights of each risk factor were determined by comprehensively considering expert system scores and actual economic loss indicators. In response to the issue of unclear improvement directions for weak points, this paper introduces the BCC model. It treats common failure modes of CNC machine tools as decision-making units within the BCC model, refines risk factors as input indicators, and evaluates the efficiency values of each decision-making unit based on various actual losses as output indicators. Through efficiency value analysis, it proposes improvement directions for weak points. Then, based on the weights of risk factors and the efficiency values of failure modes, a modified calculation method for the new Risk Priority Number (RPN) is proposed to amend the traditional RPN, This paper takes the electric spindle system of a certain machining center as an example, applies the proposed method to rank common failure modes with the new RPN, and compares it with other RPN calculation methods to verify the rationality of the proposed approach. Finally, it presents improvement directions for reliability enhancement.

Optimizing Supply Chain Efficiency Using Cross-Efficiency Analysis and Inverse DEA Models

Our study employs cross-efficiency analysis (CEA) and machine learning techniques to optimize supply chain performance. By integrating inverse DEA models with directional distance functions, we measure operational efficiency across various decision-making units (DMUs), accounting for undesirable outputs such as excess costs and emissions. Our results indicate a 20% improvement in market recognition efficiency and a 15% increase in earnings persistence efficiency after model application. Additionally, machine learning classifiers, including Random Forest and Support Vector Machines, further enhanced predictive accuracy, with Random Forest achieving the lowest mean absolute error of 0.07. These findings underscore the effectiveness of advanced analytical models in improving supply chain resilience and decision-making accuracy, contributing to sustainable operational performance.

Efficiency analysis in bi-level on fuzzy input and output

To enhance the conventional framework of data envelope analysis (DEA), a novel hybrid bi-level model is proposed, integrating fuzzy logic with triangular fuzzy numbers to effectively address data uncertainty. This model innovatively departs from the traditional DEA’s ’black box’ approach by incorporating inter-organizational relationships and the internal dynamics of decision-making units (DMUs). Utilizing a modified Russell’s method, it provides a nuanced efficiency analysis in scenarios of ambiguous data. The study aims to enhance the accuracy and applicability of Data Envelopment Analysis in uncertain data environments. To achieve this, a novel hybrid bi-level model integrating fuzzy logic is presented. Validated through a case study involving 15 branches of a private Iranian bank, the model demonstrates improved accuracy in efficiency assessments and paves the way for future research in operational systems uncertainty management. The results indicated that, among the 15 branches of a private Iranian bank analyzed for the year 2022, branches 1, 10, and 11 demonstrated leader-level efficiency, while branch 3 exhibited follower-level efficiency, and branch 1 achieved overall efficiency. These branches attained an efficiency rating of E++, signifying a high level of efficiency within the model’s parameters.

Analysis of the relationship among technical efficiency, competitive strategy, and export performance in the cocoa agroindustry in Indonesia

The goals of the current study are to analyze the technical efficiency of the Decision Making Unit (DMU) of the cocoa agroindustry and the relationship among technical efficiency, export performance, and competitive strategy in the cocoa agroindustry in Indonesia. Respondents in this study are production and marketing managers at 42 active DMUs. The results show that the average DMU technical efficiency value is 0.963 and 0.985 based on the models of Constant Returns to Scale (CRS) and Variable Returns to Scale (VRS), with the distribution of the full technical efficiency category of 80.95%, the high category of 16.67%, the medium category of 2.38%, and the low category of 0%. This study finds a model for increasing the export performance of the cocoa agroindustry, in which the technical efficiency variable has a significant positive effect on the export performance variable with competitive strategy variables as an intervening variable.

Optimizing public sector resources: A centralized DEA model for effective allocation of shared inputs

This study presents a novel centralized Data Envelopment Analysis (DEA) model with shared inputs to optimize the allocation of public sector resources and enhance management efficiency. Recognizing the need for a comprehensive approach to public sector performance evaluation, the study integrates the strengths of the centralized DEA framework and the two-stage DEA model with shared inputs. The centralized DEA model shifts the focus from individual decision-making units to optimizing resources across the entire public sector system. This allows for the reallocation of shared inputs among decision making units, leading to potential efficiency gains and improved overall performance. Incorporating shared inputs within the centralized structure enables a more nuanced understanding of the interdependencies and interactions between distinct functions and stages within the public sector. The empirical application of the proposed model in the context of public sector management and cultural subsidies provides valuable insights. The findings highlight inefficiency and offer guidance for policymakers and administrators on optimizing shared resource use. The centralized DEA model with shared inputs serves as a practical decision-support tool, informing the development of targeted policies and strategies to enhance the efficiency and effectiveness of public service delivery, particularly in resource-constrained environments. This research contributes to public sector performance evaluation's theoretical and methodological advancement, offering a comprehensive framework for resource optimization and improved management practices.

Performance measurement and management in complex environments: a system of systems approach for the public sector

Issues related to rapidly changing digital environments and turbulent or complex settings, such as the public sector, often hinder successful implementations of Performance Measurement and Management (PMM) systems. Despite the availability of PMM methods, the challenge of implementing successful systems in turbulent settings persists. This article introduces a novel PMM framework employing a System of Systems (SoS) approach, integrating Data Envelopment Analysis and Machine Learning to guide organisational changes by enhancing the efficiency of Decision-Making Units (DMU). Validated by a case study within the Brazilian Navy, the framework demonstrates a significant capacity to interpret the impact of DMU characteristics on operational efficiency and draw actionable insights. Our findings suggest that leveraging SoS principles emphasises adaptability and ongoing improvement through a data-driven process, suggesting the framework’s potential to navigate through complexity by enabling more effective management practices and decision-making. Future research directions include longitudinal studies and the incorporation of technologies to support managerial decision-making.

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.

On value efficiency analysis and cone-ratio data envelopment analysis models

AbstractIn this paper we relate the Value Efficiency Analysis (VEA) efficiency scores with those of three different Cone-Ratio Data Envelopment Analysis (CR-DEA) models, when the chosen model Decision Making Units (DMUs) in CR-DEA jointly comprise the Most Preferred Solution (MPS) in VEA. In particular, we consider the cases where the set of feasible weight vectors is given respectively by (i) the weight vectors that are jointly optimal in DEA for all the model DMUs, (ii) every weight vector that is optimal in DEA for at least one of the model DMUs, and (iii) only those weight vectors with strictly positive input and output weights, each of which is jointly optimal in DEA for all the model DMUs. In each of these cases we show that the CR-DEA efficiency scores can be obtained or approximated by estimating either a VEA model or a series of VEA models, without the need of identifying a priori the set of feasible weight vectors. We illustrate the usefulness of our results by means of an empirical application using data on Japanese regional banks.

The solution of super-scale DEA models based on preference character

The basic relationships and intrinsic laws in the data envelopment analysis (DEA) are the basis for designing fast solutions for big data DEA models. By delving into these relationships, it becomes feasible to address super-scale DEA problems more swiftly, aligning with the need for timeliness in real-world efficiency evaluations and reducing time costs. This paper leverages the intrinsic characteristics of DEA to formulate a DEA model scale reduction approach, offering reliable theoretical support for solving super- scale DEA problems. Initially, the partially ordered set theory to prove the relationship between the efficient decision making units (DMUs) of DEA and the maximal element of the partially ordered set. Building on this, a DEA model based on maximal element set (DEA-PS) is constructed based on the theory of partially ordered set theory, and the equivalence between DEA-PS model and the original DEA model is proved. Furthermore, it is proved that the dominant theorem exists in the solution of multiple classical DEA models, and a method of super-scale DEA models based on dominant theorem is given. The conclusions of this paper theoretically reveal the relationship between efficient DMUs and maximal elements, from this relationship, two key insights emerge: (1) Regardless of how huge the number of DMU is, the proposed method in this paper can realize the scale reduction of the DEA model quickly, (2) The algorithm has good compatibility with all current big data DEA model algorithms.

Performance-Based Efficiency and Effectiveness Analysis of Technology Companies Listed on Borsa Istanbul Using Data Envelopment and Logistic Regression Analysis

This study analyses the performance-based efficiency and effectiveness of technology companies listed on Borsa Istanbul using Data Envelopment Analysis (DEA) and Logistic Regression (LR). The research focuses on evaluating the relative efficiency of selected technology firms over the period 2015-2023. DEA, a non-parametric technique, is used to measure the efficiency of decision-making units, while logistic regression helps identify the factors influencing efficiency. The study aims to assess the impact of financial ratios, such as current ratio, net profit margin, accounts receivable turnover, and others, on the firms' technical efficiency. The results indicate that Net Profit Margin (NPM) and Accounts Receivable Turnover (ADH) have the most significant positive effects on efficiency, while the Fixed Assets to Equity ratio (MDVOZK) negatively impacts efficiency. This analysis provides insights into the financial health and operational performance of technology firms, offering actionable recommendations for improving efficiency and guiding investment decisions. Key Words: Data Envelopment, Logistic Regression, BİST JEL: C45, C88, G21

Digital twin-based multi-granularity synchronisation for production-warehousing under batch processing mode

Batch production is a cost-effective way that boosts efficiency in manufacturing systems. As customer demands become increasingly customised, there is a growing need to consolidate numerous small-batch orders. However, if batch production exceeds the optimal level, it may lead to the premature completion of customer orders, causing temporary congestion in the finished goods warehouse and prolonged resource occupation. Consequently, an essential focus of this study pertains to coordinating decision-making units like workshops and warehouses within batch production setups to ensure stable system operation amidst dynamic customer demands. The proposed solution encompasses a Digital Twin-based Multi-level Synchronised Control System (DTMCS) framework, featuring the Multi-granularity Synchronised Control Mechanism (MgSCM) and a collaborative optimisation model for batch production and warehouse planning. By examining a practical case study from a paint manufacturing company, the research delves into the effects of four synchronised control patterns on various system performance metrics, such as operating costs, equipment and warehouse utilisation rates, and system stability. Overall, this study provides an adaptable and cost-effective solution for enhancing production-logistics synchronisation.

A compromise solution approach for efficiency measurement with shared input: The case of tourist hotels in Taiwan

Shared input occurs often when a production system performs two or more functions and some of the functions share the same input. To determine the proportion of the shared input devoted to each function, the conventional data envelopment analysis (DEA) models that allow each decision making unit (DMU) to select the most favorable value to attain the highest efficiency score is usually used. The result is that many DMUs assign proportions of different bound values to the same function, which is not reasonable. This paper proposes a compromise solution approach that aggregates the viewpoints of all DMUs to obtain a consensus regarding the proportion of the shared input used by each function. The approach is actually a parametric DEA approach for constructing the production function. To increase the explanation power, transcendental functions are considered. The tourist hotel, where the accommodation and catering functions share the input of management employees is used for illustration. The results show that a transcendental function has stronger explanation power, and reasonable compromised values for the proportions of the effort of the management employees devoted to the two functions are obtained.

Efficient optimization of the multi-response problem in the taguchi method through advanced data envelopment analysis formulations integration

This study introduces an innovative strategy to address the multi-response challenge inherent in the Taguchi Method, frequently encountered in the enhancement of production processes and productivity indices. By integrating the Taguchi Method with Data Envelopment Analysis (DEA), experimental trials are transformed into Decision Making Units (DMUs), with response variables categorized as inputs and outputs. Advanced DEA models, including simultaneous DEA and super-efficiency models, are employed to determine the DMUs’ relative and cross efficiencies, thus retaining the advantages of the Taguchi Design of Experiments (DoE) while offering an optimal solution for enhancing multiple quality responses. The superiority of this approach is validated by the Addictive Model for Factor Effects, demonstrating enhanced outcomes and reduced computational effort compared to existing empirical and scholarly solutions. These findings have significant implications for advancing manufacturing practices that require multi-response optimization, marking a notable contribution to the field.

Protocols for MPS choices in VEA models with variable returns to scale

AbstractIn this paper, we examine the implications of using different strongly efficient Decision Making Units (DMUs) as the Most Preferred Solution (MPS) in Value Efficiency Analysis (VEA) models with variable returns to scale. We provide protocols for consistent MPS choices, where a MPS choice is consistent if and only if the resulting VEA technology does not allow for free and/or unlimited production. In particular, we show that either a DMU that belongs to the set of strongly CCR-efficient DMUs or a combination of jointly strongly CCR-efficient DMUs constitute the only consistent MPS choices for both constant-returns-to-scale and variable-returns-to-scale VEA models. In addition, we show that the scale properties of the efficient VEA frontier are affected by the choice of the MPS and in essence depend on the range of the interval between the MPS’s right- and left-side scale elasticities.

A novel two-stage network data envelopment analysis model for kidney allocation problem under medical and logistical uncertainty: a real case study.

Organ transplantation is one of the most complicated and challenging treatments in healthcare systems. Despite the significant medical advancements, many patients die while waiting for organ transplants because of the noticeable differences between organ supply and demand. In the organ transplantation supply chain, organ allocation is the most significant decision during the organ transplantation procedure, and kidney is the most widely transplanted organ. This research presents a novel method for assessing the efficiency and ranking of qualified organ-patient pairs as decision-making units (DMUs) for kidney allocation problem in the existence of COVID-19 pandemic and uncertain medical and logistical data. To achieve this goal, two-stage network data envelopment analysis (DEA) and credibility-based chance constraint programming (CCP) are utilized to develop a novel two-stage fuzzy network data envelopment analysis (TSFNDEA) method. The main benefits of the developed method can be summarized as follows: considering internal structures in kidney allocation system, investigating both medical and logistical aspects of the problem, the capability of expanding to other network structures, and unique efficiency decomposition under uncertainty. Moreover, in order to evaluate the validity and applicability of the proposed approach, a validation algorithm utilizing a real case study and different confidence levels is used. Finally, the numerical results indicate that the developed approach outperforms the existing kidney allocation system.

Regret cross-efficiency evaluation using attitudinal entropy approach

Data envelopment analysis (DEA) is a popular nonparametric tool for measuring the performance of decision making unit (DMU). Cross-efficiency evaluation is an efficient means to rank DMUs, which compensates for efficiency overestimation and incomplete ranking of conventional DEA models. With respect to incomplete rational cross-efficiency evaluation, the common methods construct models with two extreme efficiencies as reference, such as efficiency values of the aggressive and benevolent models. In addition, they only explore the irrational preferences of decision maker (DM) at a certain stage in the evaluation process. They fail to investigate the cross-efficiency evaluation synthetically under incomplete rationality and characterize the complexity of the individuals’ decision making. To fill this gap, a new method must be proposed. This paper proposes a new cross-efficiency termed regret cross-efficiency model using attitudinal entropy approach (RACE), which constructs a secondary goal model from a comprehensive perspective under the framework of regret theory, and introduces attitudinal entropy to aggregate the cross-efficiencies. Besides, this paper presents some empirical examples to illustrate the validity and robustness of the RACE method.

Machine Learning for Optimizing Macro-ergonomics in Pharmaceutical Supply Chain

This study endeavors to enhance the macro-ergonomics of pharmaceutical supply chains by introducing an innovative hybrid AI methodology, incorporating fuzzy data envelopment analysis (FDEA). A comprehensive case study is conducted to evaluate the efficiency of the pharmaceutical supply chain, focusing on macro-ergonomic work system assessment. This evaluation aims to identify design flaws contributing to communication delays between physicians and patients. The proposed integrated approach utilizes a hybrid AI framework, specifically FDEA, to accurately measure macro-ergonomic influences on the healthcare supply chain under uncertain conditions. The case study involves a prominent urban outpatient medical facility with 20 clinics, selecting 20 Decision-Making Units for a holistic perspective. Results uncover factors causing delays, emphasizing weak or absent feedback structures as critical elements affecting the healthcare supply chain’s effectiveness. In conclusion, the study recommends modifications to optimize the pharmaceutical supply chain, enhancing overall healthcare efficiency. The findings provide valuable insights for healthcare management, underscoring the crucial role of advanced AI methodologies in addressing complex challenges within healthcare supply chains.