Decomposing school and school-type efficiency

This paper puts forward a Data Envelopment Analysis (DEA) approach to decomposing a pupil's under-attainment at school. Under-attainment is attributed to the pupil, the school and the type of funding regime under which the school operates. A pupil-level analysis is used firstly on a within school and secondly on a between school basis, grouping schools by type such as state-funded, independent and so on. Overall measures of each pupil's efficiency are thus disentangled into pupil, school and school-type efficiencies. This approach provides schools with a set of efficiency measures, each one conveying different information. © 2001 Elsevier Science B.V.

A semi-oriented radial measure for measuring the efficiency of decision making units with negative data, using DEA

Data Envelopment Analysis (DEA) is a nonparametric method for measuring the efficiency of a set of decision making units such as firms or public sector agencies, first introduced into the operational research and management science literature by Charnes, Cooper, and Rhodes (CCR) [Charnes, A., Cooper, W.W., Rhodes, E., 1978. Measuring the efficiency of decision making units. European Journal of Operational Research 2, 429–444]. The original DEA models were applicable only to technologies characterized by positive inputs/outputs. In subsequent literature there have been various approaches to enable DEA to deal with negative data. In this paper, we propose a semi-oriented radial measure, which permits the presence of variables which can take both negative and positive values. The model is applied to data on a notional effluent processing system to compare the results with those yielded by two alternative methods for dealing with negative data in DEA: The modified slacks-based model suggested by Sharp et al. [Sharp, J.A., Liu, W.B., Meng, W., 2006. A modified slacks-based measure model for data envelopment analysis with ‘natural’ negative outputs and inputs. Journal of Operational Research Society 57 (11) 1–6] and the range directional model developed by Portela et al. [Portela, M.C.A.S., Thanassoulis, E., Simpson, G., 2004. A directional distance approach to deal with negative data in DEA: An application to bank branches. Journal of Operational Research Society 55 (10) 1111–1121]. A further example explores the advantages of using the new model.

Evaluating productive efficiency:comparative study of commercial banks in Gulf countries

Financial institutes are an integral part of any modern economy. In the 1970s and 1980s, Gulf Cooperation Council (GCC) countries made significant progress in financial deepening and in building a modern financial infrastructure. This study aims to evaluate the performance (efficiency) of financial institutes (banking sector) in GCC countries. Since, the selected variables include negative data for some banks and positive for others, and the available evaluation methods are not helpful in this case, so we developed a Semi Oriented Radial Model to perform this evaluation. Furthermore, since the SORM evaluation result provides a limited information for any decision maker (bankers, investors, etc...), we proposed a second stage analysis using classification and regression (C&R) method to get further results combining SORM results with other environmental data (Financial, economical and political) to set rules for the efficient banks, hence, the results will be useful for bankers in order to improve their bank performance and to the investors, maximize their returns. Mainly there are two approaches to evaluate the performance of Decision Making Units (DMUs), under each of them there are different methods with different assumptions. Parametric approach is based on the econometric regression theory and nonparametric approach is based on a mathematical linear programming theory. Under the nonparametric approaches, there are two methods: Data Envelopment Analysis (DEA) and Free Disposal Hull (FDH). While there are three methods under the parametric approach: Stochastic Frontier Analysis (SFA); Thick Frontier Analysis (TFA) and Distribution-Free Analysis (DFA). The result shows that DEA and SFA are the most applicable methods in banking sector, but DEA is seem to be most popular between researchers. However DEA as SFA still facing many challenges, one of these challenges is how to deal with negative data, since it requires the assumption that all the input and output values are non-negative, while in many applications negative outputs could appear e.g. losses in contrast with profit. Although there are few developed Models under DEA to deal with negative data but we believe that each of them has it is own limitations, therefore we developed a Semi-Oriented-Radial-Model (SORM) that could handle the negativity issue in DEA. The application result using SORM shows that the overall performance of GCC banking is relatively high (85.6%). Although, the efficiency score is fluctuated over the study period (1998-2007) due to the second Gulf War and to the international financial crisis, but still higher than the efficiency score of their counterpart in other countries. Banks operating in Saudi Arabia seem to be the highest efficient banks followed by UAE, Omani and Bahraini banks, while banks operating in Qatar and Kuwait seem to be the lowest efficient banks; this is because these two countries are the most affected country in the second Gulf War. Also, the result shows that there is no statistical relationship between the operating style (Islamic or Conventional) and bank efficiency. Even though there is no statistical differences due to the operational style, but Islamic bank seem to be more efficient than the Conventional bank, since on average their efficiency score is 86.33% compare to 85.38% for Conventional banks. Furthermore, the Islamic banks seem to be more affected by the political crisis (second Gulf War), whereas Conventional banks seem to be more affected by the financial crisis.

Costs and efficiency of higher education institutions in England:a DEA analysis

As student numbers in higher education in the UK have expanded during recent years, it has become increasingly important to understand its cost structure. This study applies Data Envelopment Analysis (DEA) to higher education institutions in England to assess their cost structure, efficiency and productivity. The paper complements an earlier study that used parametric methods to analyse the same panel data. Interestingly, DEA provides estimates of subject-specific unit costs that are in the same ballpark as those provided by the parametric methods. The paper then extends the previous analysis and finds that further student number increases of the order of 20–27% are feasible through exploiting operating and scale efficiency gains and also adjusting student mix. Finally the paper uses a Malmquist index approach to assess productivity change in the UK higher education. The results reveal that for a majority of institutions productivity has actually decreased during the study period.

Measuring productive efficiency using Nerlovian profit efficiency indicator and metafrontier analysis

The aim of this paper is to illustrate the measurement of productive efficiency using Nerlovian indicator and metafrontier with data envelopment analysis techniques. Further, we illustrate how profit efficiency of firms operating in different regions can be aggregated into one overarching frontier. Sugarcane production in three regions in Kenya has been used to illustrate these concepts. Results show that the sources of inefficiency in all regions are both technical and allocative, but allocative efficiency contributes more to the overall Nerlovian (in)efficiency indicator. © 2011 Springer-Verlag.

Uses of frontier efficiency methodologies and multi-criteria decision making for performance measurement in the energy sector

Guest editorial Ali Emrouznejad is a Senior Lecturer at the Aston Business School in Birmingham, UK. His areas of research interest include performance measurement and management, efficiency and productivity analysis as well as data mining. He has published widely in various international journals. He is an Associate Editor of IMA Journal of Management Mathematics and Guest Editor to several special issues of journals including Journal of Operational Research Society, Annals of Operations Research, Journal of Medical Systems, and International Journal of Energy Management Sector. He is in the editorial board of several international journals and co-founder of Performance Improvement Management Software. William Ho is a Senior Lecturer at the Aston University Business School. Before joining Aston in 2005, he had worked as a Research Associate in the Department of Industrial and Systems Engineering at the Hong Kong Polytechnic University. His research interests include supply chain management, production and operations management, and operations research. He has published extensively in various international journals like Computers & Operations Research, Engineering Applications of Artificial Intelligence, European Journal of Operational Research, Expert Systems with Applications, International Journal of Production Economics, International Journal of Production Research, Supply Chain Management: An International Journal, and so on. His first authored book was published in 2006. He is an Editorial Board member of the International Journal of Advanced Manufacturing Technology and an Associate Editor of the OR Insight Journal. Currently, he is a Scholar of the Advanced Institute of Management Research. Uses of frontier efficiency methodologies and multi-criteria decision making for performance measurement in the energy sector This special issue aims to focus on holistic, applied research on performance measurement in energy sector management and for publication of relevant applied research to bridge the gap between industry and academia. After a rigorous refereeing process, seven papers were included in this special issue. The volume opens with five data envelopment analysis (DEA)-based papers. Wu et al. apply the DEA-based Malmquist index to evaluate the changes in relative efficiency and the total factor productivity of coal-fired electricity generation of 30 Chinese administrative regions from 1999 to 2007. Factors considered in the model include fuel consumption, labor, capital, sulphur dioxide emissions, and electricity generated. The authors reveal that the east provinces were relatively and technically more efficient, whereas the west provinces had the highest growth rate in the period studied. Ioannis E. Tsolas applies the DEA approach to assess the performance of Greek fossil fuel-fired power stations taking undesirable outputs into consideration, such as carbon dioxide and sulphur dioxide emissions. In addition, the bootstrapping approach is deployed to address the uncertainty surrounding DEA point estimates, and provide bias-corrected estimations and confidence intervals for the point estimates. The author revealed from the sample that the non-lignite-fired stations are on an average more efficient than the lignite-fired stations. Maethee Mekaroonreung and Andrew L. Johnson compare the relative performance of three DEA-based measures, which estimate production frontiers and evaluate the relative efficiency of 113 US petroleum refineries while considering undesirable outputs. Three inputs (capital, energy consumption, and crude oil consumption), two desirable outputs (gasoline and distillate generation), and an undesirable output (toxic release) are considered in the DEA models. The authors discover that refineries in the Rocky Mountain region performed the best, and about 60 percent of oil refineries in the sample could improve their efficiencies further. H. Omrani, A. Azadeh, S. F. Ghaderi, and S. Abdollahzadeh presented an integrated approach, combining DEA, corrected ordinary least squares (COLS), and principal component analysis (PCA) methods, to calculate the relative efficiency scores of 26 Iranian electricity distribution units from 2003 to 2006. Specifically, both DEA and COLS are used to check three internal consistency conditions, whereas PCA is used to verify and validate the final ranking results of either DEA (consistency) or DEA-COLS (non-consistency). Three inputs (network length, transformer capacity, and number of employees) and two outputs (number of customers and total electricity sales) are considered in the model. Virendra Ajodhia applied three DEA-based models to evaluate the relative performance of 20 electricity distribution firms from the UK and the Netherlands. The first model is a traditional DEA model for analyzing cost-only efficiency. The second model includes (inverse) quality by modelling total customer minutes lost as an input data. The third model is based on the idea of using total social costs, including the firm’s private costs and the interruption costs incurred by consumers, as an input. Both energy-delivered and number of consumers are treated as the outputs in the models. After five DEA papers, Stelios Grafakos, Alexandros Flamos, Vlasis Oikonomou, and D. Zevgolis presented a multiple criteria analysis weighting approach to evaluate the energy and climate policy. The proposed approach is akin to the analytic hierarchy process, which consists of pairwise comparisons, consistency verification, and criteria prioritization. In the approach, stakeholders and experts in the energy policy field are incorporated in the evaluation process by providing an interactive mean with verbal, numerical, and visual representation of their preferences. A total of 14 evaluation criteria were considered and classified into four objectives, such as climate change mitigation, energy effectiveness, socioeconomic, and competitiveness and technology. Finally, Borge Hess applied the stochastic frontier analysis approach to analyze the impact of various business strategies, including acquisition, holding structures, and joint ventures, on a firm’s efficiency within a sample of 47 natural gas transmission pipelines in the USA from 1996 to 2005. The author finds that there were no significant changes in the firm’s efficiency by an acquisition, and there is a weak evidence for efficiency improvements caused by the new shareholder. Besides, the author discovers that parent companies appear not to influence a subsidiary’s efficiency positively. In addition, the analysis shows a negative impact of a joint venture on technical efficiency of the pipeline company. To conclude, we are grateful to all the authors for their contribution, and all the reviewers for their constructive comments, which made this special issue possible. We hope that this issue would contribute significantly to performance improvement of the energy sector.

Economic efficiency of smallholder maize producers in Western Kenya:a DEA meta-frontier analysis

Maize is the main staple food for most Kenyan households, and it predominates where smallholder, as well as large-scale, farming takes place. In the sugarcane growing areas of Western Kenya, there is pressure on farmers on whether to grow food crops, or grow sugarcane, which is the main cash crop. Further, with small and diminishing land sizes, the question of productivity and efficiency, both for cash and food crops is of great importance. This paper, therefore, uses a two-step estimation technique (DEA meta-frontier and Tobit Regression) to highlight the inefficiencies in maize cultivation, and their causes in Western Kenya.

Efficiency analysis of Islamic banks:a case of Gulf Cooperation Council (GCC)

Although considerable effort has been invested in the measurement of banking efficiency using Data Envelopment Analysis, hardly any empirical research has focused on comparison of banks in Gulf States Countries This paper employs data on Gulf States banking sector for the period 2000-2002 to develop efficiency scores and rankings for both Islamic and conventional banks. We then investigate the productivity change using Malmquist Index and decompose the productivity into technical change and efficiency change. Further, hypothesis testing and statistical precision in the context of nonparametric efficiency and productivity measurement have been used. Specially, cross-country analysis of efficiency and comparisons of efficiencies between Islamic banks and conventional banks have been investigated using Mann-Whitney test.

Combining an enterprise resource planning system and informality:a study of efficiency and flexibility complmentarity [sic]

DUE TO COPYRIGHT RESTRICTIONS ONLY AVAILABLE FOR CONSULTATION AT ASTON UNIVERSITY LIBRARY AND INFORMATION SERVICES WITH PRIOR ARRANGEMENT One of the current research trends in Enterprise Resource Planning (ERP) involves examining the critical factors for its successful implementation. However, such research is limited to system implementation, not focusing on the flexibility of ERP to respond to changes in business. Therefore, this study explores a combination system, made up of an ERP and informality, intended to provide organisations with efficient and flexible performance simultaneously. In addition, this research analyses the benefits and challenges of using the system. The research was based on socio-technical system (STS) theory which contains two dimensions: 1) a technical dimension which evaluates the performance of the system; and 2) a social dimension which examines the impact of the system on an organisation. A mixed method approach has been followed in this research. The qualitative part aims to understand the constraints of using a single ERP system, and to define a new system corresponding to these problems. To achieve this goal, four Chinese companies operating in different industries were studied, all of which faced challenges in using an ERP system due to complexity and uncertainty in their business environments. The quantitative part contains a discrete-event simulation study that is intended to examine the impact of operational performance when a company implements the hybrid system in a real-life situation. Moreover, this research conducts a further qualitative case study, the better to understand the influence of the system in an organisation. The empirical aspect of the study reveals that an ERP with pre-determined business activities cannot react promptly to unanticipated changes in a business. Incorporating informality into an ERP can react to different situations by using different procedures that are based on the practical knowledge of frontline employees. Furthermore, the simulation study shows that the combination system can achieve a balance between efficiency and flexibility. Unlike existing research, which emphasises a continuous improvement in the IT functions of an enterprise system, this research contributes to providing a definition of a new system in theory, which has mixed performance and contains both the formal practices embedded in an ERP and informal activities based on human knowledge. It supports both cost-efficiency in executing business transactions and flexibility in coping with business uncertainty.This research also indicates risks of using the system, such as using an ERP with limited functions; a high cost for performing informally; and a low system acceptance, owing to a shift in organisational culture. With respect to practical contribution, this research suggests that companies can choose the most suitable enterprise system approach in accordance with their operational strategies. The combination system can be implemented in a company that needs to operate a medium amount of volume and variety. By contrast, the traditional ERP system is better suited in a company that operates a high-level volume market, while an informal system is more suitable for a firm with a requirement for a high level of variety.

Assessing productive efficiency of banks using integrated Fuzzy-DEA and bootstrapping:a case of Mozambican banks

Performance analysis has become a vital part of the management practices in the banking industry. There are numerous applications using DEA models to estimate efficiency in banking, and most of them assume that inputs and outputs are known with absolute precision. Here, we propose new Fuzzy-DEA α-level models to assess underlying uncertainty. Further, bootstrap truncated regressions with fixed factors are used to measure the impact of each model on the efficiency scores and to identify the most relevant contextual variables on efficiency. The proposed models have been demonstrated using an application in Mozambican banks to handle the underlying uncertainty. Findings reveal that fuzziness is predominant over randomness in interpreting the results. In addition, fuzziness can be used by decision-makers to identify missing variables to help in interpreting the results. Price of labor, price of capital, and market-share were found to be the significant factors in measuring bank efficiency. Managerial implications are addressed.

Carbon efficiency evaluation:an analytical framework using fuzzy DEA

Data Envelopment Analysis (DEA) is a powerful analytical technique for measuring the relative efficiency of alternatives based on their inputs and outputs. The alternatives can be in the form of countries who attempt to enhance their productivity and environmental efficiencies concurrently. However, when desirable outputs such as productivity increases, undesirable outputs increase as well (e.g. carbon emissions), thus making the performance evaluation questionable. In addition, traditional environmental efficiency has been typically measured by crisp input and output (desirable and undesirable). However, the input and output data, such as CO2 emissions, in real-world evaluation problems are often imprecise or ambiguous. This paper proposes a DEA-based framework where the input and output data are characterized by symmetrical and asymmetrical fuzzy numbers. The proposed method allows the environmental evaluation to be assessed at different levels of certainty. The validity of the proposed model has been tested and its usefulness is illustrated using two numerical examples. An application of energy efficiency among 23 European Union (EU) member countries is further presented to show the applicability and efficacy of the proposed approach under asymmetric fuzzy numbers.

The principle of legal certainty as a principle of economic efficiency

Legal certainty, a feature of the rule of law, constitutes a requirement for the operational necessities of market interactions. But, the compatibility of the principle of legal certainty with ideals such as liberalism and free market economy must not lead to the hastened conclusion that therefore the principle of legal certainty would be compatible and tantamount to the principle of economic efficiency. We intend to analyse the efficiency rationale of an important general principle of EU law—the principle of legal certainty. In this paper, we shall assert that not only does the EU legal certainty principle encapsulate an efficiency rationale, but most importantly, it has been interpreted by the ECJ as such. The economic perspective of the principle of legal certainty in the European context has, so far, never been adopted. Hence, we intend to fill in this gap and propose the representation of the principle of legal certainty as a principle of economic efficiency. After having deciphered the principle of legal certainty from a law and economics perspective (1), we shall delve into the jurisprudence of the ECJ so that the judicial reasoning of the Court as this reasoning proves the relevance of the proposed representation (2). Finally, we conclude in light of the findings of this paper (3).

Developing European operational oceanography for Blue Growth, climate change adaptation and mitigation, and ecosystem-based management

Operational approaches have been more and more widely developed and used for providing marine data and information services for different socio-economic sectors of the Blue Growth and to advance knowledge about the marine environment. The objective of operational oceanographic research is to develop and improve the efficiency, timeliness, robustness and product quality of this approach. This white paper aims to address key scientific challenges and research priorities for the development of operational oceanography in Europe for the next 5-10 years. Knowledge gaps and deficiencies are identified in relation to common scientific challenges in four EuroGOOS knowledge areas: European Ocean Observations, Modelling and Forecasting Technology, Coastal Operational Oceanography and Operational Ecology. The areas "European Ocean Observations" and "Modelling and Forecasting Technology" focus on the further advancement of the basic instruments and capacities for European operational oceanography, while "Coastal Operational Oceanography" and "Operational Ecology" aim at developing new operational approaches for the corresponding knowledge areas.

COMPARATIVE ANALYSIS OF EFFICIENCY AND OPERATING CHARACTERISTICS OF AUTOMOTIVE POWERTRAIN ARCHITECTURES THROUGH CHASSIS DYNAMOMETER TESTING

The thesis "COMPARATIVE ANALYSIS OF EFFICIENCY AND OPERATING CHARACTERISTICS OF AUTOMOTIVE POWERTRAIN ARCHITECTURES THROUGH CHASSIS DYNAMOMETER TESTING" was completed through a collaborative partnership between Michigan Technological University and Argonne National Laboratory under a contractual agreement titled "Advanced Vehicle Characterization at Argonne National Laboratory". The goal of this project was to investigate, understand and document the performance and operational strategy of several modern passenger vehicles of various architectures. The vehicles were chosen to represent several popular engine and transmission architectures and were instrumented to allow for data collection to facilitate comparative analysis. In order to ensure repeatability and reliability during testing, each vehicle was tested over a series of identical drive cycles in a controlled environment utilizing a vehicle chassis dynamometer. Where possible, instrumentation was preserved between vehicles to ensure robust data collection. The efficiency and fuel economy performance of the vehicles was studied. In addition, the powertrain utilization strategies, significant energy loss sources, tailpipe emissions, combustion characteristics, and cold start behavior were also explored in detail. It was concluded that each vehicle realizes different strengths and suffers from different limitations in the course of their attempts to maximize efficiency and fuel economy. In addition, it was observed that each vehicle regardless of architecture exhibits significant energy losses and difficulties in cold start operation that can be further improved with advancing technology. It is clear that advanced engine technologies and driveline technologies are complimentary aspects of vehicle design that must be utilized together for best efficiency improvements. Finally, it was concluded that advanced technology vehicles do not come without associated cost; the complexity of the powertrains and lifecycle costs must be considered to understand the full impact of advanced vehicle technology.

Enabling effective operational decision making on a Combined Heat and Power System using the 5C architecture

The use of Cyber Physical Systems (CPS) to optimise industrial energy systems is an approach which has the potential to positively impact on manufacturing sector energy efficiency. The need to obtain data to facilitate the implementation of a CPS in an industrial energy system is however a complex task which is often implemented in a non-standardised way. The use of the 5C CPS architecture has the potential to standardise this approach. This paper describes a case study where data from a Combined Heat and Power (CHP) system located in a large manufacturing company was fused with grid electricity and gas models as well as a maintenance cost model using the 5C architecture with a view to making effective decisions on its cost efficient operation. A control change implemented based on the cognitive analysis enabled via the 5C architecture implementation has resulted in energy cost savings of over €7400 over a four-month period, with energy cost savings of over €150,000 projected once the 5C architecture is extended into the production environment.