Assessing the queuing process using data envelopment analysis:an application in health centres

Queuing is one of the very important criteria for assessing the performance and efficiency of any service industry, including healthcare. Data Envelopment Analysis (DEA) is one of the most widely-used techniques for performance measurement in healthcare. However, no queue management application has been reported in the health-related DEA literature. Most of the studies regarding patient flow systems had the objective of improving an already existing Appointment System. The current study presents a novel application of DEA for assessing the queuing process at an Outpatients’ department of a large public hospital in a developing country where appointment systems do not exist. The main aim of the current study is to demonstrate the usefulness of DEA modelling in the evaluation of a queue system. The patient flow pathway considered for this study consists of two stages; consultation with a doctor and pharmacy. The DEA results indicated that waiting times and other related queuing variables included need considerable minimisation at both stages.

Guiding schools to improved performance using data envelopment analysis:an illustration with data from a local education authority

The educational process is characterised by multiple outcomes such as the achievement of academic results of various standards and non-academic achievements. This paper shows how data envelopment analysis (DEA) can be used to guide secondary schools to improved performance through role-model identification and target setting in a way which recognises the multi-outcome nature of the education process and reflects the relative desirability of improving individual outcomes. The approach presented in the paper draws from a DEA-based assessment of the schools of a local education authority carried out by the authors. Data from that assessment are used to illustrate the approach presented in the paper. (Key words: Data envelopment analysis, education, target setting.)

Applications of data envelopment analysis in education

Non-parametric methods for efficiency evaluation were designed to analyse industries comprising multi-input multi-output producers and lacking data on market prices. Education is a typical example. In this chapter, we review applications of DEA in secondary and tertiary education, focusing on the opportunities that this offers for benchmarking at institutional level. At secondary level, we investigate also the disaggregation of efficiency measures into pupil-level and school-level effects. For higher education, while many analyses concern overall institutional efficiency, we examine also studies that take a more disaggregated approach, centred either around the performance of specific functional areas or that of individual employees.

Better queue management in a busy public hospital of a developing country without appointment system::an application using data envelopment analysis

Queuing is a key efficiency criterion in any service industry, including Healthcare. Almost all queue management studies are dedicated to improving an existing Appointment System. In developing countries such as Pakistan, there are no Appointment Systems for outpatients, resulting in excessive wait times. Additionally, excessive overloading, limited resources and cumbersome procedures lead to over-whelming queues. Despite numerous Healthcare applications, Data Envelopment Analysis (DEA) has not been applied for queue assessment. The current study aims to extend DEA modelling and demonstrate its usefulness by evaluating the queue system of a busy public hospital in a developing country, Pakistan, where all outpatients are walk-in; along with construction of a dynamic framework dedicated towards the implementation of the model. The inadequate allocation of doctors/personnel was observed as the most critical issue for long queues. Hence, the Queuing-DEA model has been developed such that it determines the ‘required’ number of doctors/personnel. The results indicated that given extensive wait times or length of queue, or both, led to high target values for doctors/personnel. Hence, this crucial information allows the administrators to ensure optimal staff utilization and controlling the queue pre-emptively, minimizing wait times. The dynamic framework constructed, specifically targets practical implementation of the Queuing-DEA model in resource-poor public hospitals of developing countries such as Pakistan; to continuously monitor rapidly changing queue situation and display latest required personnel. Consequently, the wait times of subsequent patients can be minimized, along with dynamic staff scheduling in the absence of appointments. This dynamic framework has been designed in Excel, requiring minimal training and work for users and automatic update features, with complex technical aspects running in the background. The proposed model and the dynamic framework has the potential to be applied in similar public hospitals, even in other developing countries, where appointment systems for outpatients are non-existent.

Värdeflödesanalys och Data Envelopment Analysis : En fallstudie för att värdera effektiviteten hos ett logistikföretag

Companies face new challenges almost every day. In order to stay competitive, it is important that companies strive for continuous development and improvement. By describing companies through their processes it is possible to get a clear overview of the entire operation, which can contribute, to a well-established overall understanding of the company. This is a case study based on Stort AB which is a small logistics company specialized in international transportation and logistics solutions. The purpose of this study is to perform value stream mapping in order to create a more efficient production process and propose possible improvements in order to reduce processing time. After performing value stream mapping, data envelopment analysis is used to calculate how lean Stort AB is today and how lean the company can become by implementing the proposed improvements. The results show that the production process can improve efficiency by minimizing waste produced by a bad workplace layout and over-processing. The authors suggested solution is to introduce standardized processes and invest in technical instruments in order to automate the process to reduce process time. According to data envelopment analysis the business is 41 percent lean at present and may soon become 55 percent lean and finally reach an optimum 100 percent lean mode if the process is automated.

Eficiência das unidades de saúde familiar na diabetes – uma abordagem por Data Envelopment Analysis

Dissertação de Mestrado, Gestão de Unidades de Saúde, Faculdade de Economia, Universidade do Algarve, 2016

Uma metodologia para a análise do desempenho sustentável dos postos de revenda de combustível na cidade de Natal/RN, com o uso de data envelopment analysis-DEA

The increasing use of fossil fuels in line with cities demographic explosion carries out to huge environmental impact in society. For mitigate these social impacts, regulatory requirements have positively influenced the environmental consciousness of society, as well as, the strategic behavior of businesses. Along with this environmental awareness, the regulatory organs have conquered and formulated new laws to control potentially polluting activities, mostly in the gas stations sector. Seeking for increasing market competitiveness, this sector needs to quickly respond to internal and external pressures, adapting to the new standards required in a strategic way to get the Green Badge . Gas stations have incorporated new strategies to attract and retain new customers whom present increasingly social demand. In the social dimension, these projects help the local economy by generating jobs and income distribution. In this survey, the present research aims to align the social, economic and environmental dimensions to set the sustainable performance indicators at Gas Stations sector in the city of Natal/RN. The Sustainable Balanced Scorecard (SBSC) framework was create with a set of indicators for mapping the production process of gas stations. This mapping aimed at identifying operational inefficiencies through multidimensional indicators. To carry out this research, was developed a system for evaluating the sustainability performance with application of Data Envelopment Analysis (DEA) through a quantitative method approach to detect system s efficiency level. In order to understand the systemic complexity, sub organizational processes were analyzed by the technique Network Data Envelopment Analysis (NDEA) figuring their micro activities to identify and diagnose the real causes of overall inefficiency. The sample size comprised 33 Gas stations and the conceptual model included 15 indicators distributed in the three dimensions of sustainability: social, environmental and economic. These three dimensions were measured by means of classical models DEA-CCR input oriented. To unify performance score of individual dimensions, was designed a unique grouping index based upon two means: arithmetic and weighted. After this, another analysis was performed to measure the four perspectives of SBSC: learning and growth, internal processes, customers, and financial, unifying, by averaging the performance scores. NDEA results showed that no company was assessed with excellence in sustainability performance. Some NDEA higher efficiency Gas Stations proved to be inefficient under certain perspectives of SBSC. In the sequence, a comparative sustainable performance and assessment analyzes among the gas station was done, enabling entrepreneurs evaluate their performance in the market competitors. Diagnoses were also obtained to support the decision making of entrepreneurs in improving the management of organizational resources and promote guidelines the regulators. Finally, the average index of sustainable performance was 69.42%, representing the efforts of the environmental suitability of the Gas station. This results point out a significant awareness of this segment, but it still needs further action to enhance sustainability in the long term

A novel ranking procedure for forecasting approaches using Data Envelopment Analysis

To compare the accuracy of different forecasting approaches an error measure is required. Many error measures have been proposed in the literature, however in practice there are some situations where different measures yield different decisions on forecasting approach selection and there is no agreement on which approach should be used. Generally forecasting measures represent ratios or percentages providing an overall image of how well fitted the forecasting technique is to the observations. This paper proposes a multiplicative Data Envelopment Analysis (DEA) model in order to rank several forecasting techniques. We demonstrate the proposed model by applying it to the set of yearly time series of the M3 competition. The usefulness of the proposed approach has been tested using the M3-competition where five error measures have been applied in and aggregated to a single DEA score.

Sustainable development in the BRICS countries: an efficiency analysis by data envelopment

There is much concern about the social and environmental impacts caused by the economic growth of nations. Thus, to evaluate the socio-economic performance of nations, economists have increasingly addressed matters related to social welfare and the environment. It is within the scope of this context that this work discusses the performance of countries in the BRICS group regarding sustainable development. The objective of this study regards evaluating the efficiency of these countries in transforming productive resources and technological innovation into sustainable development. The proposed objective was achieved by using econometric tools as well as the data envelopment analysis method to then create economic, environmental, and social efficiency rankings for the BRICS countries, which enabled to carry out comparative analyses on the sustainable development of those countries. The results of such assessments can be of interest for more specific scientific explorations.

Estimating the efficiency of healthcare facilities providing HIV/AIDS treatment in Zambia:a data envelopment approach

Zambia and many other countries in Sub-Saharan Africa face a key challenge of sustaining high levels of coverage of AIDS treatment under prospects of dwindling global resources for HIV/AIDS treatment. Policy debate in HIV/AIDS is increasingly paying more focus to efficiency in the use of available resources. In this chapter, we apply Data Envelopment Analysis (DEA) to estimate short term technical efficiency of 34 HIV/AIDS treatment facilities in Zambia. The data consists of input variables such as human resources, medical equipment, building space, drugs, medical supplies, and other materials used in providing HIV/AIDS treatment. Two main outputs namely, numbers of ART-years (Anti-Retroviral Therapy-years) and pre-ART-years are included in the model. Results show the mean technical efficiency score to be 83%, with great variability in efficiency scores across the facilities. Scale inefficiency is also shown to be significant. About half of the facilities were on the efficiency frontier. We also construct bootstrap confidence intervals around the efficiency scores.

Ranking efficiency units in DEA using bootstrapping an applied analysis for Slovenian farm data

This article explores how data envelopment analysis (DEA), along with a smoothed bootstrap method, can be used in applied analysis to obtain more reliable efficiency rankings for farms. The main focus is the smoothed homogeneous bootstrap procedure introduced by Simar and Wilson (1998) to implement statistical inference for the original efficiency point estimates. Two main model specifications, constant and variable returns to scale, are investigated along with various choices regarding data aggregation. The coefficient of separation (CoS), a statistic that indicates the degree of statistical differentiation within the sample, is used to demonstrate the findings. The CoS suggests a substantive dependency of the results on the methodology and assumptions employed. Accordingly, some observations are made on how to conduct DEA in order to get more reliable efficiency rankings, depending on the purpose for which they are to be used. In addition, attention is drawn to the ability of the SLICE MODEL, implemented in GAMS, to enable researchers to overcome the computational burdens of conducting DEA (with bootstrapping).

Technical Efficiency in the Indian Textiles Industry: A Nonparametric Analysis of Firm-Level Data

The Indian textiles industry is now at the crossroads with the phasing out of quota regime that prevailed under the Multi-Fiber Agreement (MFA) until the end of 2004. In the face of a full integration of the textiles sector in the WTO, maintaining and enhancing productive efficiency is a precondition for competitiveness of the Indian firms in the new liberalized world market. In this paper we use data obtained from the Annual Survey of Industries for a number of years to measure the levels of technical efficiency in the Indian textiles industry at the firm level. We use both a grand frontier applicable to all firms and a group frontier specific to firms from any individual state, ownership, or organization type in order to evaluate their efficiencies. This permits us to separately identify how locational, proprietary, and organizational characteristics of a firm affect its performance.

Data flow analysis of embedded program expressions

Data flow analysis techniques can be used to help assess threats to data confidentiality and integrity in security critical program code. However, a fundamental weakness of static analysis techniques is that they overestimate the ways in which data may propagate at run time. Discounting large numbers of these false-positive data flow paths wastes an information security evaluator's time and effort. Here we show how to automatically eliminate some false-positive data flow paths by precisely modelling how classified data is blocked by certain expressions in embedded C code. We present a library of detailed data flow models of individual expression elements and an algorithm for introducing these components into conventional data flow graphs. The resulting models can be used to accurately trace byte-level or even bit-level data flow through expressions that are normally treated as atomic. This allows us to identify expressions that safely downgrade their classified inputs and thereby eliminate false-positive data flow paths from the security evaluation process. To validate the approach we have implemented and tested it in an existing data flow analysis toolkit.