Scale In Data Envelopment Analysis Research Articles

Study on human resource allocation efficiency based on DEA analysis

As a new resource, human resource has attracted wide attention in many social fields. The competition of human resource is the competition of productivity between different enterprises. With the reform and opening up, national economy of China continues to develop rapidly. The sound macro-economy and rapidly development of capital market lead the banking industry to have a good development prospect. Whether the human resource allocation of commercial Banks is effective or not has a great impact on the bank earning. This study aims to further clarify the human resource allocation efficiency of banks to improve the efficiency and realize the improvement of bank profits. In this study, computer software was used to calculate the scale efficiency, pure technical efficiency, the change of scale interval and data envelopment analysis (DEA) efficiency of some selected representative banks according to the DEA model. The analysis showed that the DEA scale of many banks could reach the valid value 1, and the human resource allocation efficiency was optimized. The pure technical efficiency of some banks was valid value 1, but the scale efficiency was low and ineffective, which needed to be enlarged appropriately. And the DEA efficiency of some banks was invalid, which was caused by the valid value 1 of the scale efficiency and relatively low pure technical efficiency, and such banks need to make reasonable allocation of human resources.

Determinants of cargo and eco-efficiencies of global container shipping companies

PurposeThis study aims to evaluate cargo and eco-efficiency of global container shipping companies (CSCs) and explore the determinants of the CSCs' efficiencies. While the former is derived from the CSCs' operational perspective, the latter highlights environmental issue related to carbon emission reduction.Design/methodology/approachIn the first stage, a two-stage double bootstrap approach of data envelopment analysis (DEA) is applied to derive bias-corrected cargo and eco-efficiency of the top ten global CSCs under the variable returns to scale assumption. In the second stage, ordinary least squares and truncated regression are applied to examine determinants of the CSCs' efficiencies.FindingsThe DEA results reveal that the cargo efficiency of the CSCs is higher than their eco-efficiency by about 2.6% under variable returns to scale in DEA. However, the bias-corrected results show that the difference is 2.9%. The overall average efficiencies suggest that the CSCs can improve their cargo (eco) efficiency by 6.9% (10.8%). In the second stage, the regression results show that the numbers of ship, return on assets and asset turnover ratio are significantly related to both cargo and eco-efficiencies, whereas the total fleet capacity positively affects cargo efficiency.Research limitations/implicationsThe results of this study can help the inefficient CSCs make strategic decisions to improve their performance. For example, their business experience and capacity may be contributing to their efficiencies. However, this study only focuses on the container market among the three main markets, namely, dry bulk, wet bulk and container.Originality/valueThis study highlights an environmental issue in the shipping industry. While CSCs are operating their cargo efficiently in general, they should also put green initiatives into their business operations for the long-term sustainability.

Severe Flaws of Returns to Scale in Data Envelopment Analysis

In data envelopment analysis (DEA), returns to scale (RTS) are a widely accepted instrument for a decision-making unit (DMU) to reveal its activity scaling potentials. In the case of increasing returns to scale (IRS), a DMU learns that upsizing activities improves its productivity. For decreasing returns to scale (DRS), the instrument likewise should depict a downsizing force, again for improving productivity. Unfortunately, here the classical RTS concept shows misbehavior. Under certain circumstances, it is the wrong indicator for scaling activities and even hides respective productivity improvement potentials. In this paper, we study this phenomenon and illustrate it via little numerical examples and a real-world application consisting of 37 Brazilian banks.

A parameter-free approach for estimating the quality and quantity of the right and left returns to scale in Data Envelopment Analysis

One of the concepts that has gained important attention from economic and management-science research is that of the concept of returns to scale (RTS). There are several research papers on the behavior of the right and left RTS, as two specific directions of RTS, based on Data Envelopment Analysis (DEA) models. However, the main weakness of most of these methods is that they are based on the defined parameters, which itself leads to a high sensitivity of the models to variations in the magnitudes of the parameters and, thus, unreliable results. In this paper, we specially address this issue and propose a simple procedure for detecting the right and left RTS classification with an important feature that is independent of any predetermined parameters. In addition to the type of RTS, we also suggest a method to determine the value of the right and left RTS corresponding to each of the efficient DMUs. Finally, the superiority of the proposed parameter-free procedure over a parameter-based methods is elaborated by a real case in the banking system.

Evaluating the operational performance of airside and landside at Chinese airports with novel inputs

ABSTRACTThis paper evaluates the operational performance of airside and landside at Chinese airports with two novel inputs. Furthermore, the airport landside operation is decomposed into passenger-terminal operations and cargo-warehouse operations. One novel input is the capability of the runway system which is introduced into airside performance evaluation. The other novel input is cargo warehousing which is introduced into landside performance evaluation. To address multiple optimal solutions when estimating Returns to Scale in Data Envelopment Analysis, we adopted the Zhu and Shen method. This empirical study shows that neither the number of runways nor total runway length is a genuine index of runway system capability in the evaluation of airside performance. Only four airports achieved full efficiency in all eight measures while eight airports did not achieve any full efficiency. In addition, one airport did not perform well in the benchmark analysis.

A full-scale investigation of the directional returns to scale in data envelopment analysis

Returns to scale is considered as one of the important concepts in data envelopment analysis (DEA) which can be useful for deciding to increase or decrease the size of a particular decision making unit. Traditional returns to scale on the efficient surface of the production possibility set with variable returns to scale (VRS) technology is introduced as a ratio of proportional changes of output components to proportional changes of input components. However, a problem which may arise in the real world is the impossibility or undesirability of proportional change in the input or output components. One of the attempts which is made to solve the aforementioned problem is the work of Yang et al., 2014. They have introduced the “directional returns to scale” in the DEA framework and have proposed some procedures to estimate and measure it. In this paper, the introduced directional returns to scale is investigated from a new perspective based on the defining hyperplanes of the production possibility set with VRS technology. We propose some algebraic equations and linear programming models which in addition to measuring the directional returns to scale, they enable us to analyse it. Moreover, we introduce the concepts of the best input and output direction vectors for expansion of input components or compression of output components, respectively, and propose two linear programming models in order to obtain these directions. The presented equations and models are demonstrated using a case study and numerical examples.

Stability of the Classifications of Returns to Scale in Data Envelopment Analysis: A Case Study of the Set of Public Postal Operators

A significant theme in data envelopment analysis (DEA) is the stability of returns to scale (RTS) classification of specific decision making unit (DMU) which is under observed production possibility set. In this study the observed DMUs are public postal operators (PPOs) in European Union member states and Serbia as a candidate country. We demonstrated a sensitivity analysis of the inefficient PPOs by DEA-based approach. The development of this analytical process is performed based on real world data set. The estimations and implications are derived from the empirical study by using the CCR RTS method and the most productive scale size concept (MPSS). First, we estimated the RTS classification of all observed PPOs. After that, we determined stability intervals for preserving the RTS classification for each CCR inefficient PPO under evaluation. Finally, scale efficient inputs and output targets for these PPOs are designated.

A cost and technical efficiency analysis of two alternative models for implementing the basic package of health services in Afghanistan

Since 2003, the Afghan Ministry of Public Health (MoPH) and international partners have directed a contracting-out model through which non-governmental organisations (NGOs) deliver the Basic Package of Health Services (BPHS) in 31 of the 34 Afghan provinces. The MoPH also managed health service delivery in three provinces under an alternative initiative entitled Strengthening Mechanisms (SM). In 2011, under the authority of the MoPH and Delegation of the European Union to Afghanistan, EPOS Health Management conducted a cost and technical efficiency study of the contracting-out and SM mechanisms in six provinces to examine economic trade-offs in the provision of the BPHS. The study provides analyses of all resource inputs and primary outputs of the BPHS in the six provinces during 2008 and 2009. The authors examined technical efficiency using Data Envelopment Analysis (DEA) at the BPHS facility level. Cost analysis results indicate that the weighted average cost per BPHS outpatient visit totalled $3.41 in the SM provinces and $5.39 in the NGO-led provinces in 2009. Furthermore, the data envelopment analyses (DEAs) indicate that facilities in the three NGO-led provinces scored 0.168 points higher on the DEA scale (0–1) than SM facilities. The authors conclude that an approximate 60% increase in costs yielded a 16.8% increase in technical efficiency in the delivery of the BPHS during 2009 in the six provinces.

Returns to scale vs. damages to scale in data envelopment analysis: An impact of U.S. clean air act on coal-fired power plants

This study proposes a use of Data Envelopment Analysis (DEA) to measure Returns to Scale (RTS) of US coal-fired power plants. The power plants produce not only desirable outputs (e.g., electricity) but also undesirable outputs (e.g., CO2 and NOx) as a result of their plant operations. Therefore, the proposed use of DEA also measures a new concept, or “DTS: Damages to Scale” (corresponding to RTS for undesirable outputs). Both scale measures discussed in this study are a quick-and-easy approach for assessing RTS and DTS, but not an exact method, because it does not consider a direct linkage between the two measures. This study applies the proposed approach to examine the legal validity of U.S. Clean Air Act (CAA). We find that the CAA has been legally effective and influential on the operation of coal-fired power plants in the United States because their plant operations belong to increasing RTS on a desirable output and increasing DTS on three undesirable outputs. The increasing DTS indicates a need for managerial improvement and/or engineering innovation such as advanced clean coal technology. This empirical result implies that U.S. federal and state governments need to expand the legal scope of CAA to the emission control of CO2 because the current CAA excludes the regulation on CO2 emission that is now considered as a main source of the global warming and climate change.

Enhancement of value portfolio performance using data envelopment analysis

PurposeThe purpose of this paper is to examine the applicability of data envelopment analysis (DEA) as a basis of value portfolio selection criterion.Design/methodology/approachThe portfolios are composed of the comprehensive sample of Finnish non‐financial stocks based on their DEA scale efficiency scores. The performance of portfolios is evaluated on the basis of average return and several risk‐adjusted performance metrics. Moreover, the impact of holding period length on the results is examined by varying the portfolio reformation frequency from one to five years at annual frequency.FindingsThe results show that the DEA scale efficiency scores add value to portfolio selection. Though outperformance of the DEA value portfolios in contrast to both comparable glamour portfolio and the stock market average is most evident for shorter (i.e. annual and biannual) holding periods, the absolute performance of the DEA value portfolio can be enhanced by using longer reformation intervals.Research limitations/implicationsThe sample of stocks is not large in spite of its comprehensiveness from the local stock market aspect. Future studies can apply DEA approach to other stock markets to examine whether the results are parallel to this study.Practical implicationsThe DEA is particularly useful as a multicriteria methodology in cases in which the number of stocks in the sample is large.Originality/valueThis paper is the first attempt to form value portfolios using DEA models. The proposed methodology provides an interesting alternative to detect undervalued stocks by capturing several dimensions of relative value simultaneously. It provides also useful implications in portfolio management.

An additive model approach for estimating returns to scale in imprecise data envelopment analysis

In this paper, additive model is used to provide an alternative approach for estimating returns to scale in data envelopment analysis. The proposed model is developed in both stochastic and fuzzy data envelopment analysis. Deterministic (crisp) equivalents are obtained which correspond to the stochastic and fuzzy models. Numerical examples are, also, used to illustrate the proposed approaches.

A dual simplex-based method for determination of the right and left returns to scale in DEA

Abstract This study reviews the concept of the “right” and the “left” returns to scale (RTS) in data envelopment analysis (DEA), and a dual simplex-based method, for determining these two notions in RTS, is proposed, which has computational advantages as compared to the customary method.

Weak congestion in output additive data envelopment analysis

Our earlier work [Wei QL, Yan H. Congestion and returns to scale in data envelopment analysis. European Journal of Operational Research 2004;153:641–60] discussed necessary and sufficient conditions for the existence of congestion together with aspects of returns to scale under an output-oriented DEA framework. In line with this work, the current paper investigates the issue of “weak congestion”, wherein congestion occurs when the reduction of selected inputs causes some, rather than all, outputs to increase, without a worsening of others. We define output efficiency for decision making units under a series of typical DEA output additive models. Based on this definition, we offer necessary and sufficient conditions for the existence of weak congestion. Numerical examples are provided for purposes of illustration.

Notes on Sensitivity and Stability of the Classifications of Returns to Scale in Data Envelopment Analysis

As its title suggests, this note constitutes a critique of the paper by Seiford and Zhu (1999), “Sensitivity and Stability of the Classifications of Returns to Scale (RTS) in Data Envelopment Analysis”. By means of counter examples, we discuss some problems related to results presented in that paper.

Notes on Sensitivity and Stability of the Classifications of Returns to Scale in Data Envelopment Analysis: A Comment

Seiford and Zhu (1999) address the issue of sensitivity of returns to scale (RTS)classifications in data envelopment analysis (DEA). As noted in Jahanshahloo,Lotfi and Zohrehbandian (2004), a number of Theorems (e.g., Theorems 11, 12,16 and 17) in Seiford and Zhu (1999) may not be true for some decision makingunits (DMUs). They proposed a remedy for these Theorems. We point out that theissue can be addressed directly by the findings in Seiford and Zhu (1999).Note that such an issue is caused by DMUs located outside the set

An enhanced procedure for estimating returns-to-scale in DEA

In this paper, by improving the Golany and Yu's method [Estimating returns to scale in DEA, European Journal of Operational Research 103 (1) (1997) 28-37], a new algorithm to estimate returns-to-scale (RTS) in data envelopment analysis (DEA) models is proposed. We show that the proposed algorithm overcomes existing disadvantages in the Golany and Yu's method, introduced for this purpose. The advantages of the new algorithm are illustrated.

Non-discretionary and discretionary factors and scale in data envelopment analysis

This paper discusses the interpretation of exogenously fixed, non-discretionary factors in data envelopment analysis (DEA) and suggests a generalised model for incorporating different types of inputs and outputs in DEA. The paper first compares two approaches to the inclusion of continuous non-discretionary factors in a one-stage model, one introduced by Banker and Morey [Operat. Res. 34 (4) (1986) 513–521] and the other by Lovell [TOP 2 (1994) 175–248] and Ruggiero [Eur. J. Operat. Res. 90 (1996) 553–565]. The approaches are compared theoretically, on the basis of axiomatic systems, and empirically, on the basis of examples and simulated data. The key difference is that some models require non-discretionary factors to be scale independent, i.e. indices, and some require them to be scale dependent, i.e. volume measures. Also, the returns to scale properties vary depending on the manner in which non-discretionary factors are treated. In practice, we may want to utilise the properties of the different models simultaneously. A generalised DEA model allowing for this possibility is presented.

Congestion and returns to scale in data envelopment analysis

Congestion indicates an economic state where inputs are overly invested. Evidence of congestion occurs whenever reducing some inputs can increase outputs. Return to scale is another related important economic implication of DEA efficiencies. In this research, we discuss together the problems of congestion, constant return to scale, increasing return to scale and decreasing return to scale by output oriented DEA models, C 2R model, BC 2 model, FG model, ST model, and a modified model. We identify the necessary and sufficient conditions for the evidence of congestion, constant, increasing and decreasing returns to scale. We show the exclusive relationships between the concepts of congestion and returns to scale. Numerical examples are provided for illustration.

On Returns to Scale under Weight Restrictions in Data Envelopment Analysis

Weextend the concept of returns to scale in Data Envelopment Analysis(DEA) to the weight restriction environments. By the additionof weight restrictions, the status of returns to scale, i.e., increasing, constant and decreasing may suffer a change. Wefirst define ``returns to scale'' under weight restrictions andpropose a method for identifying the status of returns to scale.Then, it is demonstrated that the region of the most productivescale size (MPSS) will usually be narrowed by this addition.For an inefficient decision making unit (DMU), a simple rulefor determining the status of returns to scale of its projectedDMU will be presented. An empirical study compares the resultsof the proposed method with those of the BCC model and demonstratesthe change in the MPSS for both models.

Improving the Identification of Returns to Scale in Data Envelopment Analysis

Improving the Identification of Returns to Scale in Data Envelopment Analysis