Komal Aqeel Safdar, Ali Emrouznejad and Prasanta Kumar Dey
The aim of this research study is to develop a queue assessment model to evaluate the inflow of walk-in outpatients in a busy public hospital of an emerging economy, in the…
Abstract
Purpose
The aim of this research study is to develop a queue assessment model to evaluate the inflow of walk-in outpatients in a busy public hospital of an emerging economy, in the absence of appointment systems, and construct a dynamic framework dedicated towards the practical implementation of the proposed model, for continuous monitoring of the queue system.
Design/methodology/approach
The current study utilizes data envelopment analysis (DEA) to develop a combined queuing–DEA model as applied to evaluate the wait times of patients, within different stages of the outpatients' department at the Combined Military Hospital (CMH) in Lahore, Pakistan, over a period of seven weeks (23rd April to 28th May 2014). The number of doctors/personnel and consultation time were considered as outputs, where consultation time was the non-discretionary output. The two inputs were wait time and length of queue. Additionally, VBA programming in Excel has been utilized to develop the dynamic framework for continuous queue monitoring.
Findings
The inadequate availability of personnel was observed as the critical issue for long wait times, along with overcrowding and variable arrival pattern of walk-in patients. The DEA model displayed the “required” number of personnel, corresponding to different wait times, indicating queue build-up.
Originality/value
The current study develops a queue evaluation model for a busy outpatients' department in a public hospital, where “all” patients are walk-in and no appointment systems. This model provides vital information in the form of “required” number of personnel which allows the administrators to control the queue pre-emptively minimizing wait times, with optimal yet dynamic staff allocation. Additionally, the dynamic framework specifically targets practical implementation in resource-poor public hospitals of emerging economies for continuous queue monitoring.
Details
Keywords
Bertha Viviana Ruales Guzmán, Gloria Isabel Rodríguez Lozano and Oscar Fernando Castellanos Domínguez
This research had two main objectives: To measure the productivity of companies in the Colombian dairy industry and to identify efficient decision-making units (DMUs) that can be…
Abstract
Purpose
This research had two main objectives: To measure the productivity of companies in the Colombian dairy industry and to identify efficient decision-making units (DMUs) that can be used as work sample in future case studies.
Design/methodology/approach
In the measurement of productivity, financial variables were considered for a sample of 19 DMUs. Efficient companies were identified through the data envelopment analysis (DEAs) methodology with the VRS model oriented to inputs and outputs. The input variables analyzed were “current asset,” “property, plant and equipment,” “non-current liability” and “equity,” while the output variables were “revenue” and “profit.”
Findings
Findings revealed that seven DMUs are efficient in the input and output orientation and that companies of different sizes and with or without quality certifications are efficient in the sample analyzed. Additionally, the benchmark efficient DMUs were identified for each of the non-efficient DMUs.
Research limitations/implications
The implications for the research include the contribution to the theory, on the one hand, with the analysis of the current state of the literature on the use of DEA in the food sector, and on the other, with the use of DEA to measure the productivity of Colombian dairy industry companies and with the identification of a sample of efficient units that can be analyzed in future case studies.
Originality/value
This article is novel and pioneering because it measures for the first time the productivity of DMUs of the Colombian dairy industry, in addition to including the current state of the literature on the application of the DEA methodology in the food sector. These findings contribute to the consolidation of the theory and also provide inputs for researchers, practitioners, managers, and policy makers.
Details
Keywords
Joses M. Kirigia, Ali Emrouznejad, Rui Gama Vaz, Henry Bastiene and Jude Padayachy
The purpose of this paper is to measure the technical and scale efficiency of health centres; to evaluate changes in productivity; and to highlight possible policy implications of…
Abstract
Purpose
The purpose of this paper is to measure the technical and scale efficiency of health centres; to evaluate changes in productivity; and to highlight possible policy implications of the results for policy makers.
Design/methodology/approach
Data envelopment analysis (DEA) is employed to assess the technical and scale efficiency, and productivity change over a four‐year period among 17 public health centres.
Findings
During the period of study, the results suggest that the public health centres in Seychelles have exhibited mean overall or technical efficiency of above 93 per cent. It was also found that the overall productivity increased by 2.4 per cent over 2001‐2004.
Research limitations/implications
Further research can be undertaken to gather data on the prices of the various inputs to facilitate an estimation of the allocative efficiency of clinics. If such an exercise were to be undertaken, researchers may also consider collecting data on quantities and prices of paramedical, administrative and support staff to ensure that the analysis is more comprehensive than the study reported in this paper. Institutionalization of efficiency monitoring would help to enhance further the already good health sector stewardship and governance.
Originality/value
This paper provides new empirical evidence on a four‐year trend in the efficiency and productivity of health centres in Seychelles.
Details
Keywords
M.R. Mulwa, A. Emrouznejad and F.M. Murithi
The data used in this study is for the period 1980‐2000. Almost midway through this period (in 1992), the Kenyan government liberalized the sugar industry and the role of the…
Abstract
Purpose
The data used in this study is for the period 1980‐2000. Almost midway through this period (in 1992), the Kenyan government liberalized the sugar industry and the role of the market increased, while the government's role with respect to control of prices, imports and other aspects in the sector declined. This exposed the local sugar manufacturers to external competition from other sugar producers, especially from the COMESA region. This study aims to find whether there were any changes in efficiency of production between the two periods (pre and post‐liberalization).
Design/methodology/approach
The study utilized two methodologies to efficiency estimation: data envelopment analysis (DEA) and the stochastic frontier. DEA uses mathematical programming techniques and does not impose any functional form on the data. However, it attributes all deviation from the mean function to inefficiencies. The stochastic frontier utilizes econometric techniques.
Findings
The test for structural differences in the two periods does not show any statistically significant differences between the two periods. However, both methodologies show a decline in efficiency levels from 1992, with the lowest period experienced in 1998. From then on, efficiency levels began to increase.
Originality/value
To the best of the authors' knowledge, this is the first paper to use both methodologies in the sugar industry in Kenya. It is shown that in industries where the noise (error) term is minimal (such as manufacturing), the DEA and stochastic frontier give similar results.
Details
Keywords
Abdel Latef M. Anouze and Imad Bou-Hamad
This paper aims to assess the application of seven statistical and data mining techniques to second-stage data envelopment analysis (DEA) for bank performance.
Abstract
Purpose
This paper aims to assess the application of seven statistical and data mining techniques to second-stage data envelopment analysis (DEA) for bank performance.
Design/methodology/approach
Different statistical and data mining techniques are used to second-stage DEA for bank performance as a part of an attempt to produce a powerful model for bank performance with effective predictive ability. The projected data mining tools are classification and regression trees (CART), conditional inference trees (CIT), random forest based on CART and CIT, bagging, artificial neural networks and their statistical counterpart, logistic regression.
Findings
The results showed that random forests and bagging outperform other methods in terms of predictive power.
Originality/value
This is the first study to assess the impact of environmental factors on banking performance in Middle East and North Africa countries.
Details
Keywords
Estimating the production function is one of the most interest topics in economics, managements, and operations research. Often the number of decision-making units (DMUs) is not…
Abstract
Estimating the production function is one of the most interest topics in economics, managements, and operations research. Often the number of decision-making units (DMUs) is not sufficiently large in comparison with the numbers of inputs and outputs. In this case, the available methodologies suffer to distinguish between DMUs and to provide a fair estimation of the production function. In the literature, studies usually suggest that researchers should either decrease the number of input-output variables or increase the number of DMUs. We demonstrate the reasons for such suggestions and provide a geometric visualization to address this issue. A simple but powerful model is introduced which is able to estimate a production function when the number of DMUs are small. A real-life numerical example of 32 DMUs with 45 variables is also used to demonstrate the advantages of the introduced model. From such an approach, researchers can benchmark organizations even if the number of DMUs is less than the number of input-output variables.
Details
Keywords
C.P. Barros, Mike G. Tsionas, Peter Wanke and Md. Abul Kalam Azad
The purpose of this paper is to analyze the bank efficiency in three developing countries, namely Angola, Brazil and Mozambique, aiming to infer differences given that they belong…
Abstract
Purpose
The purpose of this paper is to analyze the bank efficiency in three developing countries, namely Angola, Brazil and Mozambique, aiming to infer differences given that they belong to the same cultural tradition. The underlying idea is to control for the cultural background, thus allowing the discussion on how different socio-economic and historical variables maybe impacting different levels of banking efficiency and returns to scale results within the ambit of these three countries.
Design/methodology/approach
Due to the presence of latent inefficiency, the authors have to modify the technique to accommodate simulation by importance sampling; therefore, in effect, the authors use a local maximum simulated likelihood approach.
Findings
The results reveal that Brazil has the highest level of output-oriented efficiency, followed by Angola and then Mozambique. The same ranking is observed in returns to scale, except that vis-à-vis technical change, Brazil and Angola rank first. Finally, inefficiency derived from technical change is highest in Mozambique, followed by Angola and then Brazil. Therefore, these results reveal that the countries with the highest degree of development are higher in efficiency.
Originality/value
Previous studies have identified factors such as legal tradition, accounting conventions, regulatory structures, property rights, culture and religion as possible explanations for cross-border variations in financial development and economic growth. This is the first time banking efficiency is assessed in light of a common cultural background by selecting a group of countries that share the same language and colonial past. Since results are controlled for the same background, it is possible to affirm that the findings are purely related to scale size and economic/political background issues of each country.
Details
Keywords
Dyanne Brendalyn Mirasol-Cavero and Lanndon Ocampo
University department efficiency evaluation is a performance assessment on how departments use their resources to attain their goals. The most widely used tool in measuring the…
Abstract
Purpose
University department efficiency evaluation is a performance assessment on how departments use their resources to attain their goals. The most widely used tool in measuring the efficiency of academic departments in data envelopment analysis (DEA) deals with crisp data, which may be, often, imprecise, vague, missing or predicted. Current literature offers various approaches to addressing these uncertainties by introducing fuzzy set theory within the basic DEA framework. However, current fuzzy DEA approaches fail to handle missing data, particularly in output values, which are prevalent in real-life evaluation. Thus, this study aims to augment these limitations by offering a fuzzy DEA variation.
Design/methodology/approach
This paper proposes a more flexible approach by introducing the fuzzy preference programming – DEA (FPP-DEA), where the outputs are expressed as fuzzy numbers and the inputs are conveyed in their actual crisp values. A case study in one of the top higher education institutions in the Philippines was conducted to elucidate the proposed FPP-DEA with fuzzy outputs.
Findings
Due to its high discriminating power, the proposed model is more constricted in reporting the efficiency scores such that there are lesser reported efficient departments. Although the proposed model can still calculate efficiency no matter how much missing and unavailable, and uncertain data, more comprehensive data accessibility would return an accurate and precise efficiency score.
Originality/value
This study offers a fuzzy DEA formulation via FPP, which can handle missing, unavailable and imprecise data for output values.
Details
Keywords
Baabak Ashuri, Jun Wang, Mohsen Shahandashti and Minsoo Baek
Building energy benchmarking is required for adopting an energy certification scheme, promoting energy efficiency and reducing energy consumption. It demonstrates the current…
Abstract
Purpose
Building energy benchmarking is required for adopting an energy certification scheme, promoting energy efficiency and reducing energy consumption. It demonstrates the current level of energy consumption, the value of potential energy improvement and the prospects for additional savings. This paper aims to create a new data envelopment analysis (DEA) model that overcomes the limitations of existing models for building energy benchmarking.
Design/methodology/approach
Data preparation: the findings of the literature search and subject matter experts’ inputs are used to construct the DEA model. Particularly, it is ensured that the included variables would not violate the fundamental assumption of DEA modeling, DEA convexity axiom. New DEA formulation: controllable and non-controllable variables, e.g. weather conditions, are differentiated in the new formulation. A new approach is used to identify outliers to avoid skewing the efficiency scores for the rest of the buildings under consideration. Efficiency analysis: three distinct efficiencies are computed and analyzed in benchmarking building energy: overall, pure technical, and scale efficiency.
Findings
The proposed DEA approach is successfully applied to a data set provided by a utility management and energy services company that is active in the multifamily housing industry. Building characteristics and energy consumption of 124 multifamily properties in 15 different states in the USA are found in the data set. Buildings in this data set are benchmarked using the new DEA energy benchmarking formulation. Building energy benchmarking is also conducted in a time series manner showing how a particular building performs across the period of 12 months compared with its peers.
Originality/value
The proposed research contributes to the body of knowledge in building energy benchmarking through developing a new outlier detection method to mitigate the impact of super-efficient and super-inefficient buildings on skewing the efficiency scores of the other buildings; avoiding ratio variables in the DEA formulation to adhere to the convexity assumption that existing DEA methods do not follow; and distinguishing between controllable and non-controllable variables in the DEA formulation. This research contributes to the state of practice through providing a new energy benchmarking tool for facility managers and building owners that strive to relatively rank the energy-efficiency of their properties and identify low-performing properties as investment targets to enhance energy efficiency.
Details
Keywords
Monireh Jahani Sayyad Noveiri, Sohrab Kordrostami and Mojtaba Ghiyasi
The purpose of this study is to estimate inputs (outputs) and flexible measures when outputs (inputs) are changed provided that the relative efficiency values remain without…
Abstract
Purpose
The purpose of this study is to estimate inputs (outputs) and flexible measures when outputs (inputs) are changed provided that the relative efficiency values remain without change.
Design/methodology/approach
A novel inverse data envelopment analysis (DEA) approach with flexible measures is proposed in this research to assess inputs (outputs) and flexible measures when outputs (inputs) are perturbed on condition that the relative efficiency scores remain unchanged. Furthermore, flexible inverse DEA approaches proposed in this study are used for a numerical example from the literature and an application of Iranian banking industry to clarify and validate them.
Findings
The findings show that including flexible measures into the investigation effects on the changes of performance measures estimated and leads to more reasonable achievements.
Originality/value
The traditional inverse DEA models usually investigate the changes of some determinate input-output factors for the changes of other given input-output indicators assuming that the efficiency values are preserved. However, there are situations that the changes of performance measures should be tackled while some measures, called flexible measures, can play either input or output roles. Accordingly, inverse DEA optimization models with flexible measures are rendered in this paper to address these issues.