Data Envelopment Analysis-discriminant Analysis Research Articles

An Innovative Patient Clustering Method Using Data Envelopment Analysis–Discriminant Analysis and Artificial Neural Networks: A Case Study in Healthcare Systems

A major lesson healthcare managers learned from the COVID-19 outbreak is the need for more effective patient classification and medical resource allocation for future pandemics. In their view, hospitalization mortality could be greatly reduced if more effective systems for patient classification were in place before the outbreak to evaluate and assign treatment facilities. This study presents a scalable patient clustering approach using a Self-Organizing Map (SOM) of the Artificial Neural Network (ANN) to cluster patients for appropriate treatment allocation. The patients’ membership is forecasted using Data Envelopment Analysis–Discriminant Analysis (DEA-DA). The objectives of this research are to develop a flexible framework that healthcare systems can adopt to cluster patients based on specific testing criteria from medical records and to assign them to suitable medical centers with appropriate treatment resources. This method aims to enhance healthcare system efficiency by ensuring patients with severe illnesses receive care at well-equipped centers, while those with milder symptoms are directed to other suitable facilities. The approach is scalable and adaptable to any type of widespread illness and aims to increase recovery rates and decrease mortality rates, as confirmed by the case study results.

Understanding the efficiency evolution for the Chinese provincial power industry: A new approach for combining data envelopment analysis-discriminant analysis with an efficiency shift across periods

To examine a change in unified efficiency of provincial power industry, this study proposes an approach which combines Data Envelopment Analysis-Discriminant Analysis (DEA-DA), DEA environmental assessment and a rank sum test. The proposed approach is designed to overcome the following difficulties: (a) how to classify various decision making units (DMUs) into different groups, (b) how to identify the existence of group heterogeneity across DMUs, (c) how to measure unified efficiencies of power industry in different regions of China, (d) how to separate among various unified efficiency measures, and (e) how to unify these measures into a single measure which expresses total efficiency. To document the practicality, this study applies the proposed approach to examine unified efficiency measures of Chinese provincial power industry from 2009 to 2015. We obtain three empirical findings. First, the unified efficiency measurement identifies an existence of heterogeneity between two groups of provinces in China. Second, profound differences were confirmed in unified efficiency at a provincial level. Special attention should be given to the provinces with poor performance under both natural and managerial disposability. Finally, under both DEA and DEA-DA frameworks, large differences were confirmed between natural and managerial disposability. These two disposability concepts may assist in developing well-designed environmental and energy policy.

Assessment of oil refinery performance: Application of data envelopment analysis-discriminant analysis

Refineries are vital not only in the oil and gas industry but also in many other industries. Refinery products are essential for many industrial sectors and end-users. However, the market for refinery products has witnessed a significant transformation due to changes made in products to meet market demand and environmental regulations. This study examines refineries’ operational efficiency and conducts an efficiency-based rank assessment using an unbalanced panel dataset comprised of oil and gas refineries in four global regions (U.S. and Canada; Europe; Asia-Pacific; Africa and the Middle East) covering 2008 to 2017. This study applies a combination of data envelopment analysis and data envelopment analysis–discriminant analysis to examine the efficiency-based rank for oil and gas refineries. A Kruskal–Wallis rank sum test is conducted to examine whether the average efficiency-based ranks measures change over time and whether they differ among the four regions. Moreover, a Wilcoxon rank sum test is utilized to investigate whether the adjusted efficiency averages differ between any two of the regions under study over the 10 years between 2008 and 2017. The results indicate that the U.S. and Canada display superior performance among the four regions, likely because that region contains major companies and complex refineries that use highly advanced technology.

A Study on Support Method of Consulting Service Using Text Mining

This study aims to build a support method for consulting service companies allowing them to respond to client demands regardless of the expertise of the consultants. With an emphasis on the revitalization of small and medium-sized enterprises, the importance of support systems for consulting services for small and medium-sized enterprises, which support solving problems that are difficult to deal with by an enterprise, is increasing. Consulting companies can respond to a wide range of management consultations; however, because the contents of a consultation are widely and highly specialized, a service proposal and the problem detection depend on the experience and intuition of the consultant, and thus a stable service may occasionally not be provided. Therefore, a support system for providing stable services independent of the ability of consultants is desired. In this research, as the first step in constructing a support system, an analysis of customer information describing the content of a consultation with the client companies is conducted to predict the occurrence of future problems. Text data such as the consultant’s visitation history, consultation content by e-mail, and call center content are used in the analysis because the contents explain not only the current problems but also possibly contain future problems. This paper describes a method for analyzing the text data by employing text mining. In the proposed method, by combining a correspondence analysis with a DEA (Data Envelopment Analysis) discriminant analysis, words that are strongly related to problem detection are extracted from a large number of words obtained from text data, and variables of the DEA discriminant analysis are reduced and analyzed. The proposed method focuses on a cancellation of contract problems. The cancellation problem does not include uncertainty; it is clearly known whether the contract of the consulting service is being updated or cancelled. In this study, computer experiments were conducted to verify the effectiveness of the proposed method through a comparison with an existing method. The results of the verification experiment are as follows. First, there is a possibility of discovering new factors that cannot be determined from the intuition and experience of the consultant regarding the target problem. Second, through a comparison with the existing method, the effectiveness of the proposed method was confirmed.

Investigating socio-economic ranking of cities in Turkey using data envelopment analysis (DEA) and linear discriminant analysis (LDA)

Decision makers have to analyze both social and economic situation of the cities because of the budget constraint of the governments while considering investment to the cities. In this respect, data envelopment analysis (DEA) has been widely used to determine the efficiency of the cities and to make clear the needs of the cities calling decision making units. In this study, socio-economic ranking of the cities of Turkey is presented by using DEA and linear discriminant analysis and the cities are compared to each other according to the socio-economic development efficiency scores. Then, the highlighted inferences about the cities are presented according to the NUTs-1 classification.

A developed model of data envelopment analysis-discriminant analysis for predicting group membership of suppliers in green supply chain

This paper presents a developed model of data envelopment analysis-discriminant analysis (DEA-DA) for predicting group membership of green suppliers. This model accurately predicts group membership of the green suppliers in the presence of undesirable outputs. For demonstrating applicability of our proposed model, using a real-world dataset for evaluating green suppliers of the Hyundai Steel Company, the DEA-DA model developed by Boudaghi (2017) is analysed and compared with the new model. The results show that our developed model has more precise prediction of green suppliers' group membership with respect to considering undesirable outputs.

A developed model of data envelopment analysis-discriminant analysis for predicting group membership of suppliers in green supply chain

A developed model of data envelopment analysis-discriminant analysis for predicting group membership of suppliers in green supply chain

A Broad Case-Based Distance Approach for Screening with Different Target Points

The case-based distance (CBD) methods for screening are helpful for assisting decision makers in filtering out alternatives that are unlikely to be chosen. However, most of these methods based on selected cases and distance measurements from a chosen target point can only solve screening problems with positive criteria weights. This study proposes a two-phase approach based on mixed integer programming to integrate the concept of Data Envelopment Analysis-Discriminant Analysis (DEA-DA) and the extended case-based distance (ECBD) method for screening problems involving uncertain signs of criteria weights and different target points. The results show that the proposed approach can reduce the misclassification rate and address multiple solution problems. In addition, because the proposed approach can solve problems involving negative weights directly, the influences of different target points can be reduced. Therefore, it is helpful for decision makers to conduct scenario analysis based on different chosen target points.

The energy efficiency of China's regional construction industry based on the three-stage DEA model and the DEA-DA model

China's construction industry has constantly been confronted with the problems, such as high resource consumption, serious pollution and low energy efficiency. Thus, improving the energy efficiency of the construction industry and reducing its energy consumption can not only promote the sustainable development of the socio-economy and eco-economy, but also enhance the overall development level of the construction industry. In the context, the objectives are to put forward a set of systematic methodologies for measuring the energy efficiency of the regional construction industry and analyzing its change trends. First, the energy efficiency index system of the construction industry and its influencing factors are constructed through the literature review. Second, two research methods (the three-stage Data Envelopment Analysis (DEA) model and the Data Envelopment Analysis-Discriminant Analysis (DEA-DA) model) are applied to analyze the energy efficiency in 30 provinces of China and the change trends from 2003 to 2011. The results indicate that after eliminating the influence of the environment factors and random errors, the energy efficiency values of the construction industry in most of the provinces were improved. The mean of China's energy efficiency of the construction industry in each year was approximately 0.92. Except Shandong with the lowest values, the mean of the other provinces was over 0.8, which reflected that the energy management and utilization levels in the construction industry were relative mature. However, the energy efficiency in most of provinces fluctuated constantly during these nine years, with the peak in 2004 and a downward trend in the overall efficiency after 2004. From the regional aspect, the energy efficiency of the construction industry in the eastern, central and western regions decreased successively; as the development level of the local economy had less significant effects on the energy efficiency, the gaps among the three regions were not obvious.

Obviating some of the theoretical barriers of data envelopment analysis-discriminant analysis: an application in predicting cluster membership of customers

Data envelopment analysis-discriminant analysis (DEA-DA) has been used for predicting cluster membership of decision-making units (DMUs). One of the possible applications of DEA-DA is in the marketing research area. This paper uses cluster analysis to cluster customers into two clusters: Gold and Lead. Then, to predict cluster membership of new customers, DEA-DA is applied. In DEA-DA, an arbitrary parameter imposing a small gap between two clusters (η) is incorporated. It is shown that different η leads to different prediction accuracy levels since an unsuitable value for η leads to an incorrect classification of DMUs. We show that even the data set with no overlap between two clusters can be misclassified. This paper proposes a new DEA-DA model to tackle this issue. The aim of this paper is to illustrate some computational difficulties in previous DEA-DA approaches and then to propose a new DEA-DA model to overcome the difficulties. A case study demonstrates the efficacy of the proposed model.

Obviating some of the theoretical barriers of data envelopment analysis-discriminant analysis: an application in predicting cluster membership of customers

Obviating some of the theoretical barriers of data envelopment analysis-discriminant analysis: an application in predicting cluster membership of customers

Developing a model for determining optimal η in DEA-discriminant analysis for predicting suppliers’ group membership in supply chain

This study aims at developing a model for determining optimal η in data envelopment analysis –discriminant analysis (DEA-DA) in order to predict the suppliers’ group membership in supply chain. Also, this paper improves the DEA-DA model developed by Sueyoshi (Eur. J. Oper. Res. 115(3), 564–582; [11]). In this model, η parameter is used in order to avoid a trivial solution and to create more discrimination. Since the main shortcoming of the DEA-DA is determining η value based on the researcher’s subjective decision, the present paper develops a model to determine optimal η. Consequently, this paper leads to the estimate of an optimal set of weights for producing a hyperplane for determining the discriminant linear function and, hence promotes the prediction precision of the group membership of a set of observations. The efficacy of proposed model is presented using a numerical example as well as a case study.

Using cluster analysis and DEA-discriminant analysis to predict group membership of new customers

To understand the value of customers and target the most profitable customers, this paper uses cluster analysis to classify customers into four groups as platinum, gold, iron, and lead. Then, to predict group membership of new customers, data envelopment analysis-discriminant analysis (DEA-DA) model is applied. This paper operationalises the concept proposed by Zeithaml et al. (2001), ('The customer pyramid: creating and serving profitable customers', California Management Review, Vol. 43, No. 4, pp.118-142) on forming the customer pyramid. A case study demonstrates the application of the proposed approach.

A combined use of DEA (Data Envelopment Analysis) with Strong Complementary Slackness Condition and DEA–DA (Discriminant Analysis)

This study discusses a combined use of DEA (Data Environment Analysis) with SCSC (Strong Complementary Slackness Condition) and DEA–DA (Discriminant Analysis). Many studies use DEA to evaluate the performance of various organizations in private and public sectors. A conventional use of DEA is not perfect because it still contains zero in many multipliers. This implies that DEA does not fully utilize information on all inputs and outputs. As a result, DEA produces many efficient organizations. To overcome the methodological difficulty, this study proposes a new use of DEA/SCSC and DEA–DA to reduce the number of efficient organizations.

Methodological comparison between DEA (data envelopment analysis) and DEA–DA (discriminant analysis) from the perspective of bankruptcy assessment

Abstract This study compares DEA (data envelopment analysis) with DEA–DA (discriminant analysis) in terms of bankruptcy assessment. Recently, many DEA researchers propose a use of DEA as a quick-and-easy tool to assess corporate bankruptcy. Meanwhile, other DEA researchers discuss a use of DEA–DA for bankruptcy-based financial analysis. The two groups are very different from the conventional use of DEA because we have long applied DEA to the measurement of operational performance, or productivity analysis. The two research groups open up a new application area (bankruptcy-based financial assessment) for DEA. This study discusses methodological strengths and weaknesses of DEA and DEA–DA from the perspective of corporate failure. The proposed comparative analysis has the three main criteria: (a) how to handle negative data in financial variables, (b) how to handle data imbalance between default and non-default firms, and (c) how to identify a failure process over time. This study finds that DEA is a managerial tool for the initial assessment of corporate failure and DEA is useful for busy corporate leaders and financial managers. In contrast, DEA–DA is useful for researchers and individuals who are interested in the detailed assessment of bankruptcy and its failure process in a time horizon.

DEA-Discriminant Analysis: Methodological comparison among eight discriminant analysis approaches

Discriminant Analysis (DA) is a classification method that can predict the group membership of a newly sampled observation. Recently, a new type of non-parametric DA approach is proposed to provide a set of weights of a discriminant function, consequently yielding an evaluation score for the determination of group membership. The non-parametric DA is referred to as “Data Envelopment Analysis-Discriminant Analysis (DEA-DA),” because it maintains its discriminant capabilities by incorporating the non-parametric feature of DEA into DA. In this study, a use of the mixed integer approach of DEA-DA is compared with other DA methods. It is confirmed that it performs at least as well as the other well known DA methods. The proposed approach is further reformulated in a manner that it can deal with classification of more than two groups.

A USE OF NONPARAMETRIC TESTS FOR DEA-DISCRIMINANT ANALYSIS: A METHODOLOGICAL COMPARISON

Discriminant Analysis (DA) is a statistical tool that can predict the group membership of a newly sampled observation. Sueyoshi (European Journal of Operational Research, 115 (1999) 564; 131 (2001) 324; 152 (2004) 45) and Sueyoshi and Kirihara (International Journal of Systems Science, 29 (1998) 1249) have recently proposed a new type of nonparametric DA approach that provides a set of weights of a linear discriminant function, consequently yielding an evaluation score for the determination of group membership. The nonparametric DA is referred to as "Data Envelopment Analysis-Discriminant Analysis (DEA-DA)," because it maintains its discriminant capabilities by incorporating the nonparametric feature of DEA into DA. In this study, a use of two statistical tests is proposed for DEA-DA and its discriminant capability is compared with DEA from a perspective of financial analysis.

Mixed integer programming approach of extended DEA–discriminant analysis

Several unique features of data envelopment analysis (DEA) are incorporated into discriminant analysis (DA). Such a combined use of the two approaches is referred to as “DEA–DA” (Eur. J. Operat. Res. 1999 (115) 564). Sueyoshi (Eur. J. Operat. Res. 2001 (131) 324) has recently extended DEA–DA in a manner that it can deal with a negative value in data. The new version is referred to as “Extended DEA–DA”. An important feature of extended DEA–DA is that it can estimate weights of a linear discriminant function by minimizing the total deviation of misclassified observations. As an extension of his research, this research proposes a new type of mixed integer programming formulations that provide these weight estimates by minimizing the total number of misclassified observations.

An application of DEA for comparative analysis and ranking of regions in Serbia with regards to social-economic development

In this paper, we use data envelopment analysis (DEA) to estimate how well regions in Serbia utilize their resources. Based on data for four inputs and four outputs we applied an output-oriented CCR DEA model and it appears that 17 out of 30 regions are efficient. For each inefficient unit, DEA identifies the sources and level of inefficiency for each input and output. An output-oriented set of targets is determined for 13 inefficient regions. In addition, the possibilities of combining DEA and linear discriminant analysis (LDA) in evaluating performance are explored. The efficient regions are ranked using a cross efficiency matrix and an output-oriented version of Andersen–Petersen’s DEA model and the results are analyzed and compared.