Measurement of productivity index with dynamic DEA

Fare, Grosskopf, Norris and Zhang developed a non-parametric productivity index, Malmquist index, using data envelopment analysis (DEA). The Malmquist index is a measure of productivity progress (regress) and it can be decomposed to different components such as 'efficiency catch-up' and 'technology change'. However, Malmquist index and its components are based on two period of time which can capture only a part of the impact of investment in long-lived assets. The effects of lags in the investment process on the capital stock have been ignored in the current model of Malmquist index. This paper extends the recent dynamic DEA model introduced by Emrouznejad and Thanassoulis and Emrouznejad for dynamic Malmquist index. This paper shows that the dynamic productivity results for Organisation for Economic Cooperation and Development countries should reflect reality better than those based on conventional model.

Zero weights and non-zero slacks:different solution to the same problem

This paper re-assesses three independently developed approaches that are aimed at solving the problem of zero-weights or non-zero slacks in Data Envelopment Analysis (DEA). The methods are weights restricted, non-radial and extended facet DEA models. Weights restricted DEA models are dual to envelopment DEA models with restrictions on the dual variables (DEA weights) aimed at avoiding zero values for those weights; non-radial DEA models are envelopment models which avoid non-zero slacks in the input-output constraints. Finally, extended facet DEA models recognize that only projections on facets of full dimension correspond to well defined rates of substitution/transformation between all inputs/outputs which in turn correspond to non-zero weights in the multiplier version of the DEA model. We demonstrate how these methods are equivalent, not only in their aim but also in the solutions they yield. In addition, we show that the aforementioned methods modify the production frontier by extending existing facets or creating unobserved facets. Further we propose a new approach that uses weight restrictions to extend existing facets. This approach has some advantages in computational terms, because extended facet models normally make use of mixed integer programming models, which are computationally demanding.

Negative data in DEA:a directional distance approach applied to bank branches

This paper is drawn from the use of data envelopment analysis (DEA) in helping a Portuguese bank to manage the performance of its branches. The bank wanted to set targets for the branches on such variables as growth in number of clients, growth in funds deposited and so on. Such variables can take positive and negative values but apart from some exceptions, traditional DEA models have hitherto been restricted to non-negative data. We report on the development of a model to handle unrestricted data in a DEA framework and illustrate the use of this model on data from the bank concerned.

Investigation of research in DEA literature

The present bibliography includes most of the references published in the field of Data Envelopment Analysis (DEA) up to the year 2007. Some publications in 2007 are also listed, but not included in the statistics. To the best of the authors’ knowledge, this listing appears to be the most complete source of references on DEA and its applications in measuring the efficiency and productivity of decision making units (DMUs). The authors hope that this new updated bibliography will assist researchers and other scholars as they develop new frontiers in DEA. The most utilized/relevant journals, a keyword analysis, and selected statistics are also presented.

The Generalized Malmquist index and capacity utilization change: an application to the Italian manufacturing, 1989-1994

Several indices of plant capacity utilization based on the concept of best practice frontier have been proposed in the literature (Fare et al. 1992; De Borger and Kerstens, 1998). This paper suggests an alternative measure of capacity utilization change based on Generalized Malmquist index, proposed by Grifell-Tatje' and Lovell in 1998. The advantage of this specification is that it allows the measurement of productivity growth ignoring the nature of scale economies. Afterwards, this index is used to measure capacity change of a panel of Italian firms over the period 1989-94 using Data Envelopment Analysis and then its abilities of explaining the short-run movements of output are assessed.

Measurement of hospital performance

The rationale for carrying out this research was to address the clear lack of knowledge surrounding the measurement of public hospital performance in Ireland. The objectives of this research were to develop a comprehensive model for measuring hospital performance and using this model to measure the performance of public acute hospitals in Ireland in 2007. Having assessed the advantages and disadvantages of various measurement models the Data Envelopment Analysis (DEA) model was chosen for this research. DEA was initiated by Charnes, Cooper and Rhodes in 1978 and further developed by Fare et al. (1983) and Banker et al. (1984). The method used to choose relevant inputs and outputs to be included in the model followed that adopted by Casu et al. (2005) which included the use of focus groups. The main conclusions of the research are threefold. Firstly, it is clear that each stakeholder group has differing opinions on what constitutes good performance. It is therefore imperative that any performance measurement model would be designed within parameters that are clearly understood by any intended audience. Secondly, there is a lack of publicly available qualitative information in Ireland that inhibits detailed analysis of hospital performance. Thirdly, based on available qualitative and quantitative data the results indicated a high level of efficiency among the public acute hospitals in Ireland in their staffing and non pay costs, averaging 98.5%. As DEA scores are sensitive to the number of input and output variables as well as the size of the sample it should be borne in mind that a high level of efficiency could be as a result of using DEA with too many variables compared to the number of hospitals. No hospital was deemed to be scale efficient in any of the models even though the average scale efficiency for all of the hospitals was relatively high at 90.3%. Arising from this research the main recommendations would be that information on medical outcomes, survival rates and patient satisfaction should be made publicly available in Ireland; that despite a high average efficiency level that many individual hospitals need to focus on improving their technical and scale efficiencies, and that performance measurement models should be developed that would include more qualitative data.

A new DEA model for technology selection in the presence of ordinal data

This paper suggests a data envelopment analysis (DEA) model for selecting the most efficient alternative in advanced manufacturing technology in the presence of both cardinal and ordinal data. The paper explains the problem of using an iterative method for finding the most efficient alternative and proposes a new DEA model without the need of solving a series of LPs. A numerical example illustrates the model, and an application in technology selection with multi-inputs/multi-outputs shows the usefulness of the proposed approach. © 2012 Springer-Verlag London Limited.

Flexible measures in production process:a DEA-based approach

Data envelopment analysis (DEA) has been proven as an excellent data-oriented efficiency analysis method for comparing decision making units (DMUs) with multiple inputs and multiple outputs. In conventional DEA, it is assumed that the status of each measure is clearly known as either input or output. However, in some situations, a performance measure can play input role for some DMUs and output role for others. Cook and Zhu [Eur. J. Oper. Res. 180 (2007) 692–699] referred to these variables as flexible measures. The paper proposes an alternative model in which each flexible measure is treated as either input or output variable to maximize the technical efficiency of the DMU under evaluation. The main focus of this paper is on the impact that the flexible measures has on the definition of the PPS and the assessment of technical efficiency. An example in UK higher education intuitions shows applicability of the proposed approach.

Input/output deterioration in production processes

In many real applications of Data Envelopment Analysis (DEA), the decision makers have to deteriorate some inputs and some outputs. This could be because of limitation of funds available. This paper proposes a new DEA-based approach to determine highest possible reduction in the concern input variables and lowest possible deterioration in the concern output variables without reducing the efficiency in any DMU. A numerical example is used to illustrate the problem. An application in banking sector with limitation of IT investment shows the usefulness of the proposed method. © 2010 Elsevier Ltd. All rights reserved.

Estimating the scope for savings in referrals and drug prescription costs in the general Practice units of a UK primary care trust

This paper explores the potential for cost savings in the general Practice units of a Primary Care Trust (PCT) in the UK. We have used Data Envelopment Analysis (DEA) to identify benchmark Practices, which offer the lowest aggregate referral and drugs costs controlling for the number, age, gender, and deprivation level of the patients registered with each Practice. For the remaining, non-benchmark Practices, estimates of the potential for savings on referral and drug costs were obtained. Such savings could be delivered through a combination of the following actions: (i) reducing the levels of referrals and prescriptions without affecting their mix (£15.74 m savings were identified, representing 6.4% of total expenditure); (ii) switching between inpatient and outpatient referrals and/or drug treatment to exploit differences in their unit costs (£10.61 m savings were identified, representing 4.3% of total expenditure); (iii) seeking a different profile of referral and drug unit costs (£11.81 m savings were identified, representing 4.8% of total expenditure). © 2012 Elsevier B.V. All rights reserved.

A new slacks-based measure of Malmquist-Luenberger index in the presence of undesirable outputs

In the majority of production processes, noticeable amounts of bad byproducts or bad outputs are produced. The negative effects of the bad outputs on efficiency cannot be handled by the standard Malmquist index to measure productivity change over time. Toward this end, the Malmquist-Luenberger index (MLI) has been introduced, when undesirable outputs are present. In this paper, we introduce a Data Envelopment Analysis (DEA) model as well as an algorithm, which can successfully eliminate a common infeasibility problem encountered in MLI mixed period problems. This model incorporates the best endogenous direction amongst all other possible directions to increase desirable output and decrease the undesirable outputs at the same time. A simple example used to illustrate the new algorithm and a real application of steam power plants is used to show the applicability of the proposed model.

Introduction to performance improvement management software (PIM-DEA)

This chapter provides information on the use of Performance Improvement Management Software (PIMDEA). This advanced DEA software enables users to make the best possible analysis of the data, using the latest theoretical developments in Data Envelopment Analysis (DEA). PIM-DEA software gives full capacity to assess efficiency and productivity, set targets, identify benchmarks, and much more, allowing users to truly manage the performance of organizational units. PIM-DEA is easy to use and powerful, and it has an extensive range of the most up-to-date DEA models and which can handle large sets of data.

A linear relational DEA model to evaluate two-stage processes with shared inputs

Two-stage data envelopment analysis (DEA) efficiency models identify the efficient frontier of a two-stage production process. In some two-stage processes, the inputs to the first stage are shared by the second stage, known as shared inputs. This paper proposes a new relational linear DEA model for dealing with measuring the efficiency score of two-stage processes with shared inputs under constant returns-to-scale assumption. Two case studies of banking industry and university operations are taken as two examples to illustrate the potential applications of the proposed approach.

CO2 emissions reduction of Chinese light manufacturing industries:a novel RAM-based global Malmquist-Luenberger productivity index

Climate change has become one of the most challenging issues facing the world. Chinese government has realized the importance of energy conservation and prevention of the climate changes for sustainable development of China's economy and set targets for CO2 emissions reduction in China. In China industry contributes 84.2% of the total CO2 emissions, especially manufacturing industries. Data envelopment analysis (DEA) and Malmquist productivity (MP) index are the widely used mathematical techniques to address the relative efficiency and productivity of a group of homogenous decision making units, e.g. industries or countries. However, in many real applications, especially those related to energy efficiency, there are often undesirable outputs, e.g. the pollutions, waste and CO2 emissions, which are produced inevitably with desirable outputs in the production. This paper introduces a novel Malmquist-Luenberger productivity (MLP) index based on directional distance function (DDF) to address the issue of productivity evolution of DMUs in the presence of undesirable outputs. The new RAM (Range-adjusted measure)-based global MLP index has been applied to evaluate CO2 emissions reduction in Chinese light manufacturing industries. Recommendations for policy makers have been discussed.

A framework for measuring global Malmquist–Luenberger productivity index with CO2 emissions on Chinese manufacturing industries

China has achieved significant progress in terms of economic and social developments since implementation of reform and open policy in 1978. However, the rapid speed of economic growth in China has also resulted in high energy consumption and serious environmental problems, which hindering the sustainability of China's economic growth. This paper provides a framework for measuring eco-efficiency with CO2 emissions in Chinese manufacturing industries. We introduce a global Malmquist-Luenberger productivity index (GMLPI) that can handle undesirable factors within Data Envelopment Analysis (DEA). This study suggested after regulations imposed by the Chinese government, in the last stage of the analysis, i.e. during 2011–2012, the contemporaneous frontier shifts towards the global technology frontier in the direction of more desirable outputs and less undesirable outputs, i.e. producing less CO2 emissions, but the GMLPI drops slightly. This is an indication that the Chinese government needs to implement more policy regulations in order to maintain productivity index while reducing CO2 emissions.