Integrated Data Envelopment Analysis Research Articles

A new composite indicator for Manufacturing efficiency

The efficiency of production processes is a topic of fundamental importance in the manufacturing sector. Different approaches for measuring the efficiency of manufacturing processes have been presented over time. However, the debate on the reliability of the tools and indicators currently available, as well as on their effectiveness and the ability to capture changes in dynamic contexts, is still open. This paper tries to overcome some of these limitations by proposing an innovative composite indicator to measure and control the performance of production processes. The composite indicator we introduced is based on an integrated approach based on Data Envelopment Analysis (DEA), and a new multi-criteria method such as Parsimonious Analytical Hierarchy Process (PAHP), taking also into account the opinion of the domain experts. The methodological approach we proposed has been tested in a production plant of an international company operating in the automotive sector. Results demonstrated that the integration of DEA and PAHP into a composite indicator allow to obtain a more reliable and effective control tool in the analysis of the efficiency of production processes.

An Integrated Data Envelopment Analysis and Non-Cooperative Game Approach for Public Transportation Incentive Subsidy Allocation

As an important national initiative, China’s public transport priority development strategy is conducive to the development of the public transport industry and urban economic construction. However, current public transport operation is inefficient due to the information asymmetry between the government and public transport enterprises, thereby inevitably generating losses. To address the issue of information asymmetry, this study designs a public transport subsidy allocation method from the perspective of incentives and discusses the allocation results using this method through a case study. The rationality of the proposed method and the scientificity and authenticity of the conclusions, are fully explained and demonstrated via the experimental simulation. Based on real published data of public transport enterprises, the proposed method can motivate public transport enterprises to improve their performance. In the case of reporting false data, the proposed method can motivate public transport enterprises to report accurate data.

A DEA cross-efficiency inclusive methodology for assessing water quality: A Composite Water Quality Index

• An inclusive DEA-based approach to compute a Composite Water Quality Index (CWQI). • CWQI fully ranks water sources via observed physicochemical factors. • OWA operator incorporates practical water treatment conditions into CWQI. • k-Means clustering analysis used to derive water quality ranges. • The new methodology is applied on 47 dams located in the Tellian region, Algeria. This paper introduces a new index, identified as Composite Water Quality Index (CWQI), for assessing water quality. The novelty of CWQI is rooted in the practical significance of the methodological approach that is developed for its computation. The CWQI is computed within an inclusive framework that integrates data envelopment analysis (DEA) Cross Efficiency (CE) and the Ordered Weighted Averaging (OWA) operator, using Optimistic Closeness Values (OCVs) as input variables. The OCV, which measures the potential of a water quality parameter to reach its best quality status, sets a solid preliminary ground for the assessment process. The DEA-CE approach enables a collective evaluation of the water quality, which bestows more inclusiveness on the quality assessment process and, hence, more robustness of the CWQI. The OWA operator extends the standard role of CWQI, as solely a water quality measurement device, to incorporate the practical conditions of water treatment for future decision plans. The new methodology has been applied on a sample of 47 dams, described with 10 physicochemical parameters, located in Northern Algeria. Adopting a wide range of water treatment conditions, the results reveal “Kissir” and “Bougara” as the best and the worst water sources, respectively. Meanwhile, the ranking patterns of the dams are found almost the same. The k-means clustering identified the Oranie–Chott–Chergui (OCC) basin as the poorest water quality zone and Algerois–Hodna–Sommam (AHS) basin as the best.

An integrated Data Envelopment Analysis and generalized additive model for assessing managerial ability with application to the insurance industry

Managerial ability plays a vital role in the performance of business enterprises; however, such efforts are not directly observable. This study estimates managerial ability within the insurance sector of the economy based on two data sets from two different Asian countries. We estimate insurer-wise efficiency using Data Envelopment Analysis (DEA) and regress the logarithm of the obtained technical efficiency on a set of contextual variables influencing insurer performance. In the next stage, we estimate managerial ability from regression residuals (the difference between the observed and fitted values of insurer efficiency). Furthermore, we use this measure of managerial ability as an independent variable for explaining return on equity based on ordinary least squares and general additive model (GAM) regression and show the relationship is highly significant. We compare and contrast the average technical efficiency and managerial ability in the two Asian countries.

Measuring sustainable technology R&D innovation in China: A unified approach using DEA-SBM and projection analysis

Measuring Green Technology R&D Efficiency (GTR&DE) and identifying improvement potentials are of paramount importance for governmental policymaking on sustainable technology R&D investment. However, comprehensive evaluation of GTR&DE and integration with projection analysis are largely lacking. To support government policymaking, we propose a new DEA-SBM-PA approach integrating Data Envelopment Analysis – Slack Based Measurement (DEA-SBM) and projection analysis (PA) to identify how well sustainable R&D innovations are performing and how much improvements are needed. Based on the theory of decoupling, this study constructs a systemic framework of GTR&DE including economic, energy, and environmental performance indicators. Subsequently, the GTR&DE scores at provincial, regional, and national levels are measured by DEA-SBM model based on panel data from China’s 30 provinces during 2011–2017, and the potential GTR&DE improvements for the provinces deemed inefficient are assessed by projection analysis. Our findings reveal that implementation of green technology R&D innovation has positive impact in achieving comprehensive benefits covering economy, energy, and environment. Furthermore, the major contribution of this research is to develop a unifying framework to provide insights for policymakers, including assessment of GTR&DE at provincial, regional, and national levels, analysis of both spatial and temporal differences of GTR&DE scores at different levels, identification of efficient and inefficient provinces, and finally projecting GTR&DE improvement potentials for the provinces deemed inefficient. The findings have significant policy implications, particularly because they demonstrate the impact of an important government policy adjustment in 2015 by analyzing both before and after effects. Finally, it is discussed how government initiatives for sustainable technology R&D innovation may be supported with additional analysis in future.

A Novel Integrating Data Envelopment Analysis and Spherical Fuzzy MCDM Approach for Sustainable Supplier Selection in Steel Industry

Supply chain sustainability, which takes environmental, economic, and social factors into account, was recently recognized as a critical component of the supply chain (SC) management evaluation process and known as a multi-criteria decision-making problem (MCDM) that is heavily influenced by the decision-makers. While some criteria can be analyzed numerically, a large number of qualitative criteria require expert review in linguistic terms. This study proposes an integration of Data Envelopment Analysis (DEA), spherical fuzzy analytic hierarchy process (SF-AHP), and spherical fuzzy weighted aggregated sum product assessment (SF-WASPAS) to identify a sustainable supplier for the steel manufacturing industry in Vietnam. In this study, both quantitative and qualitative factors are considered through a comprehensive literature review and expert interviews. The first step employs DEA to validate high-efficiency suppliers based on a variety of quantifiable criteria. The second step evaluates these suppliers further on qualitative criteria, such as economic, environmental, and social factors. The SF-AHP was applied to obtain the criteria’s significance, whereas the SF-WASPAS was adopted to identify sustainable suppliers. The sensitivity analysis and comparative results demonstrate that the decision framework is feasible and robust. The findings of this study can assist steel industry executives in resolving the macrolevel supplier selection problem. Moreover, the proposed method can assist managers in selecting and evaluating suppliers more successfully in other industries.

Application of a decision support system for providing better mental health care: the case of the Basque Country (Spain)

IntroductionDecision Support Systems (DSS) are appropriate tools for guiding policymaking processes in Mental Health (MH) management, especially where a balanced and integrated care provision is required.ObjectivesTo assess the performance of a MH ecosystem for identifying benchmark and target-for-improvement catchment areas according to the Balanced Care model.Methods The MH provision, distinguishing inpatient, day and outpatient main types of care, has been assessed in the Mental Health Network of Gipuzkoa (Basque Country, Spain) using a DSS, integrating Data Envelopment Analysis, Monte-Carlo Simulation and Artificial Intelligence. 13 catchment areas, defined by a reference MH centre, are the units (universe) for the analysis. The indicators for MH ecosystem performance were: relative technical efficiency, stability and entropy, for identifying both benchmarking and target-for-improvement areas. The analysis of the differences between the two groups can be used to design organizational interventions.ResultsThe Mental Health Network of Gipuzkoa showed high global efficiency scores, but it can be considered statistically unstable (small changes in variable values can have relevant impacts on its performance). For a global performance improvement, it is recommended to reduce admissions and readmissions in inpatient care, increase workforce capacity and utilization of day care services and, finally, increase the availability of outpatient care services.ConclusionsThis research offers a guide for evidence-informed policy-making to improve MH care provision in the main types of care and provide aftercare. The characteristics of the area to be improved are critical to design interventions and assess their potential impact on the MH ecosystem.DisclosureNo significant relationships.

Use of a decision support system for benchmarking analysis and organizational improvement of regional mental health care: Efficiency, stability and entropy assessment of the mental health ecosystem of Gipuzkoa (Basque Country, Spain)

Decision support systems are appropriate tools for guiding policymaking processes, especially in mental health (MH), where care provision should be delivered in a balanced and integrated way. This study aims to develop an analytical process for (i) assessing the performance of an MH ecosystem and (ii) identifying benchmark and target-for-improvement catchment areas. MH provision (inpatient, day and outpatient types of care) was analysed in the Mental Health Network of Gipuzkoa (Osakidetza, Basque Country, Spain) using a decision support system that integrated data envelopment analysis, Monte Carlo simulation and artificial intelligence. The unit of analysis was the 13 catchment areas defined by a reference MH centre. MH ecosystem performance was assessed by the following indicators: relative technical efficiency, stability and entropy to guide organizational interventions. Globally, the MH system of Gipuzkoa showed high efficiency scores in each main type of care (inpatient, day and outpatient), but it can be considered unstable (small changes can have relevant impacts on MH provision and performance). Both benchmark and target-for-improvement areas were identified and described. This article provides a guide for evidence-informed decision-making and policy design to improve the continuity of MH care after inpatient discharges. The findings show that it is crucial to design interventions and strategies (i) considering the characteristics of the area to be improved and (ii) assessing the potential impact on the performance of the global MH care ecosystem. For performance improvement, it is recommended to reduce admissions and readmissions for inpatient care, increase workforce capacity and utilization of day care services and increase the availability of outpatient care services.

Deriving weights of investment alternatives using DEAHP

Analytical hierarchy process (AHP) is known for its great flexibility and wide applicability. Because of ease of use, AHP has been studied extensively and used in all applications related to multi-criteria decision making (MCDM) since its inception. AHP converts qualitative opinion of experts into quantitative values and generates a comparison matrix. In this paper, multiple expert judgments have been considered for development of matrices for criteria and alternatives of an investment selection problem. Experts' opinion was sought on selection of four categories of investment avenues based on five different selection criteria. We have integrated data envelopment analysis (DEA) to generate local weights of criteria and alternatives of pairwise comparison matrices using fundamental scale developed by Saaty.

Measuring paper industry's ecological performance in an imprecise and vague scenario: a fuzzy DEA-based analytical framework

PurposeProductivity improvement is key to sustainability performance improvements of organizations. In a real-world scenario, the nature of inputs and outputs is likely to be imprecise and vague, leading to complexity in comparing firms' efficiency measurements. Implementation of fuzzy-logic based measurement systems is a method for dealing with such cases. This paper presents a fuzzy weight objective function to solve Data Envelopment Analysis (DEA) CCR model for measuring paper mills' performance in India for 15 years.Design/methodology/approachAn integrated methodology is proposed to solve DEA models having fuzzy weights. The fuzzy DEA methodology is an extended version of the DEA approach that researchers have used for performance measurement purposes in imprecise and vague scenarios. The ecological performance of the paper industry is evaluated, considering some desirable and undesirable outputs. The effect of non-discretionary input on the performance of a paper mill is also analyzed.FindingsAnalysis suggests that the productivity of the paper industry is improving consistently throughout the period. The comparative evaluation of methods suggests that a diverse cluster of DMUs and integration of DEA with the fuzzy logic increases the diversity in the efficiency score while DEA-DE imitates the results of CCR DEA.Originality/valueProposed a fuzzy DEA-based analytical framework for measuring the paper industry's ecological performance in an imprecise and vague scenario. The model is tested on data from the paper industry in a developing country context and comparative performance analysis using DEA, fuzzy DEA and DE algorithm is done.

Novel anaerobic digestion and carbon dioxide emissions efficiency analysis of food waste treatment based on SBM-DEA model

With the development of the world economy and society, the living standards of residents have been improved, along with a large amount of food waste and carbon dioxide (CO2) emissions. In the face of global warming and energy shortages, food waste can be used as high-value bio-energy raw materials which is also an effective way to reduce CO2 emissions. Therefore, this paper proposes a novel anaerobic digestion and CO2 emissions efficiency analysis based on a Slacks-Based Measure integrating Data Envelopment Analysis (SBM-DEA) model to evaluate and optimize the process structure of anaerobic treatment of food waste. The total feed volume and the discharge volume of liquid digestate are taken as inputs, and the total methane (CH4) production volume is taken as the desirable output and CO2 emissions are regarded as the undesirable output to build the biogas production and CO2 emissions evaluation model during the anaerobic digestion process. Finally, the proposed method is used in the actual anaerobic digestion process. The results show that the overall efficiency values in January, April, May, and June in 2020 are higher than those in other months. At the same time, due to the optimal allocation of slack variables of inputs and undesirable outputs, the efficiency values of other inefficient anaerobic digestion days can be improved.

An Integrated DEA and PROMETHEE II Method for Complete Ranking: An Application in Life-Insurance Sector

Globally, insurance and takaful companies play a crucial role in economic and financial development of a country. Malaysia’s insurance sector has undergone substantial changes compared to two decades ago. Due to intense competition observed in Malaysian insurance industry, efficiency measurement and a complete efficiency ranking of insurance companies are very important for the decision makers so that necessary changes and improvement can be made. Data Envelopment Analysis (DEA) is a non-parametric method that has been acknowledged as an effective method to measure the efficiency of homogeneous decision-making units (DMUs). The main advantage of DEA is its ability to handle multiple inputs and outputs. However, standard DEA has poor discrimination power since it generates too many efficient units especially when the number of DMUs under study is insufficient in comparison to number of inputs and outputs. It cannot discriminate efficient units and therefore unable to give a complete ranking of DMUs. This study aimed to overcome the ranking problem found in standard DEA by integrating DEA and Preference Ranking Organization Method for Enrichment Evaluations (PROMETHEE) II. The hybrid method was applied to evaluate the efficiency and ranking of 22 life insurance and takaful companies in Malaysia from the period of 2017 to 2018. Input variables used in this study comprised of fees and commission, and management expenses. Meanwhile, output variables were net premium and generated investment income. The proposed method involved two stages. In the first stage, DEA was applied to obtain the efficient scores for the DMUs. In the second stage, PROMETHEE II was implemented to rank the efficient units. This hybrid method has successfully obtained full ranking of all the insurance companies under study. The inefficient companies can learn strategies and practices from efficient companies to enhance their services. It is recommended for future research to integrate DEA with other Multi Criteria Decision Making (MCDM) methods such as TOPSIS and VIKOR to fully rank the DMUs.

A proposed method for third-party reverse logistics partner selection and order allocation in the cellphone industry

There are growing social and government pressures that encourage end of life (EOL) electronic products companies to focus on reverse logistics (RL) and recovery options. The complexity of RL elements for EOL electronic products is a challenge in this field. Recently, original equipment manufacturers (OEMs) have focused on RL activities. To this aim, third-party reverse logistics partners (3PRLPs) play an important role. This paper simultaneously draws attention to the selection of 3PRLPs and determine how orders are assigned to every 3PRLP using a new framework. There are two phases in this framework. The first phase includes an approach that integrates data envelopment analysis (DEA) with a differential evolution (DE) algorithm to increase the discriminatory power and distinguish the 3PRLPs based on their efficiencies. DEA is considered in this study because it is an effective method to determine the efficiency based on the simultaneous analysis of inputs and outputs. In Phase 2, those efficiency scores are utilized to allocate orders to 3PRLPs using a multi-objective model. Two solution approaches are employed for solving the proposed multi-objective model and to find Pareto-optimal solutions. The application of the models is shown in Canada’s cellphone industry. The results reveal that the efficiency of 3PRLPs may have an effect on both third-party reverse logistics partner selection and the orders allocated to them.

Iterative Multi-Attribute Procurement Auction with Decision Support for Bid Formulation

Iterative multi-attribute reverse auctions in practice create certain difficulties for both the buyer and participating bidders. While the buyer faces the problem of creating the right attribute weights, the bidders have difficulty in adjusting the attribute values in each round. In this paper, we present an iterative multi-attribute reverse auction mechanism based on integrated data envelopment analysis (DEA) and best–worst method (BWM) with an objective of reducing the intervention of the buyer in the determination of the winner and also easing up the preference elicitation process. Unlike the typical scoring auctions, the proposed mechanism does not require the buyer to estimate the characteristics of the participating sellers in order to determine the optimal scoring function. As there will be no other intervention from the buyer during the winner determination process, the proposed method makes the procurement process impartial and corruption-free. Besides solving the buyer’s problem, the proposed mechanism is also associated with an optimal bid determination method (OBDM) to assist the sellers in formulating improvised bids in iterative rounds of the auction. Simulation experiments show that the proposed OBDM benefits both the buyer and sellers. For the buyer, it provides higher expected utility and attribute values as per his preferences; for the seller, it gives a better expected profit and a higher probability of winning.

OC-0311 Integrating data envelopment analysis into radiotherapy treatment planning for head and neck cancer

OC-0311 Integrating data envelopment analysis into radiotherapy treatment planning for head and neck cancer

DEVELOPING AN INTEGRATED MODEL FOR EVALUATING R&D ORGANIZATIONS’ PERFORMANCE: COMBINATION OF DEA-ANP

Assessing the performance of the Research and development (R&D) organizations to achieve higher productivity, growth, and development is always a critical necessity. Therefore, developing a more accurate model to evaluate the performance is always required. For this purpose, this study is aimed at developing a decision-making model for evaluating R&D performance. The model comes up with determining the most proper evaluative criteria for assessing R&D organizations. Then, it integrates Data Envelopment Analysis (DEA) with Analytical Network Process (ANP) to assess R&D performance. This paper is aimed to develop an integrated model for evaluating R&D performance. The findings of the study show that the DEA-ANP model is an accurate and acceptable model for evaluating R&D organizations’ performance.

Big data and portfolio optimization: A novel approach integrating DEA with multiple data sources

The existing literature suggests that the out-of-sample performance of traditional mean-variance portfolio strategies is not robust, and their performance is even inferior to that of the equal weight strategy. To address this problem, this paper first clarifies that a complete investment process consists of two parts, namely, stock selection and investment weight formulation. Then, we design a stock selection scheme integrating Data Envelopment Analysis (DEA) with multiple data sources, including historical stock trading data, technical indicators, social media data and news data, to assess the investment value of stocks in terms of historical return, asset correlation and investor sentiment performance. In addition, we use Support Vector Machine (SVM) combined with the multi-source data on stocks to predict the stock price movements and combine the obtained stock price movements and the proposed stock selection scheme to construct the portfolio optimization model. Further, we also carry out an out-of-sample test on the proposed stock selection scheme and investment strategies, in which the constituents of CSI 300 index are selected as the test samples. The empirical results show that the proposed stock selection scheme can effectively improve the out-of-sample performance of all investment strategies. Besides, the proposed investment strategy has a better out-of-sample performance compared to the traditional global minimum variance investment strategy, tangency portfolio investment strategy, and equal weight strategy. Finally, we perform a robustness test of the above findings using an additional dataset.

Integration of data envelopment analysis and Al-Refaie and Al-Tahat model in Taguchi method for the optimisation of additives in expansive soil treatment

ABSTRACT The effectiveness of the incorporation of data envelopment analysis (DEA) and Al-Refaie and Al-Tahat (AA) model to solve the intrinsic multiple response problem in Taguchi optimisation method was assessed in this study. Charnes, Cooper and Rhodes model, which is a variant of the DEA method, was employed in the optimisation of additives for expansive soil treatment. The additives, which were sawdust ash (SDA), quarry dust (QD) and Portland cement (CM), were assigned various levels in the designed Taguchi mixed level L18 (6^1 × 3^2) orthogonal array experiment. SDA was assigned six levels while CM and QD were assigned three levels each. Multiple responses, which included the unconfined compressive strength, California bearing ratio and differential free swell, were determined with the designed experiment and later optimised concurrently with the DEA and AA model. Optimum combination of the additives (20% SDA + 20%QD + 5% CM) obtained with the models and validated by performing confirmatory tests on the soil admixed with it proved that DEA and AA model can be integrated in Taguchi method to optimise multiple responses simultaneously. Finally, scanning electron microscope of the expansive soil treated with the optimum combination of additives revealed the formation of cementation compound in the mixture.

Integrating Data Envelopment Analysis into radiotherapy treatment planning for head and neck cancer patients

Radiotherapy treatment (RT) irradiates a patient's tumour volume while minimising damage to healthy tissue and surrounding critical organs at risk (OAR). In the conventional RT planning process, the RT planner has to iteratively adjust either the planning objectives (tumour or OAR dose levels) or the weights of the planning objectives until an acceptable plan is obtained that satisfies the minimum requirements. At the end of this iterative process, it remains unknown whether this plan is the best that can be obtained for the patient. The oncologist reviews each plan and decides to either treat using this plan or request further plan development, which may or may not lead to an actual improvement of the reviewed plan.We describe how Data Envelopment Analysis (DEA) is used as a real-time decision support tool to assess quality of RT plans for head and neck cancer patients by applying a knowledge-based comparison of each new plan to a library of previous clinically approved plans. This library allows benchmarking, which gives planners and oncologists a better idea of the relative quality of their plan and its improvement potential, resulting in improved use of resources and better quality treatments for patients. Our DEA-based approach provides a novel way of capturing multiple measures of plan quality as well as anatomical differences between patients in the benchmarking process. We present the developed DEA model and results for a set of benchmark instances. Initial results of integrating DEA-based quality feedback into the RT planning process are presented showing that operations research can contribute significantly to planning quality in this setting.

Information technology and performance: Integrating data envelopment analysis and configurational approach

While several studies claim that information technology (IT) improves business performance, others claim that the impact of IT on performance remains unclear. Based on data envelopment analysis (DEA), this paper empirically examines the relationship between IT factors, intermediate performance metrics, and business outcomes. It also advances a new conceptual perspective to investigate the relationship between IT investment and performance. We propose a theoretical framework based on network DEA models, considering multiple periods, multiple inputs and outputs to study and understand the influence of IT on performance. Using a sample of 86 firms from Asia, Europe, and the US, we measure information technology performance with network DEA models, advance an explanation of the relationship between IT and performance and compare this relationship by regions and industries. By integrating DEA and a configurational analysis, we also develop a set of configurations of IT performance to understand the differences by regions and industries. Our results show that: IT performance shows little regional difference, but significant industrial diversity. We found four configurations to capture industrial differences in IT performance, and found that the efficiency of IT operations rather than IT investments, was the main reason leading to an increase in business performance.