Best performance modeling using complementary DEA-ANN approach

The influence of SYRIZA-ANEL Greek health policies on hospital efficiency

BACKGROUND

We analyse the impact of the three following categories of Health Policies (HP) carried out by the Greek SYRIZA-ANEL governments on the efficiency of Greek public general hospitals. These governments have implemented policies intended to change the rate of contributions to publicly funded healthcare (PCnH), policies to affect the volume and quality of publicly funded health care (PVQH) and those intended to affect the costs of publicly funded healthcare (PCH). A literary review of the PCnH. PVQH and PCH policies of the Greek SYRIZA-ANEL governments was carried out and an efficiency window-DEA study was executed using data from the Ministry of Health (MoH) and the Greek Statistical Authority (ELSTAT).

METHODS

The study was designed to assess the impact of PCnH. PVQH and PCH policies by the Greek SYRIZA-ANEL governments on the efficiency of Greek general hospitals. The data was collected from HEAL-Link scientific journals. Information on HPs was extracted from the work collected. The values of inputs and outputs used for the efficiency study were obtained from ELSTAT and Greek MoH databases.

RESULTS

HPs of the Greek SYRIZA-ANEL governments extend to all three HP categories of the sample used. These policies have a dual effect on both the inputs and outputs used in efficiency. Efficiency values exhibit fluctuations with good and bad years. The SYRIZA-ANEL governments seek to ensure more equality in access to health services. Some of the policies reduce costs and have a positive impact on efficiency, while others have the opposite effect. The increase in outputs achieved as a result of health policies is counter balanced by an increase in inputs.

CONCLUSIONS

The PCnH, PVQH and PCH policies of the SYRIZA-ANEL governance seem to have a dual orientation: some policies reduce the cost of a category and contain the total cost, thus positively contributing to an increase in efficiency. Certain policies are aimed more at fulfilling the criterion of equality in the provision of health services and thus the cost inevitably increases. From the window-DEA study, three relatively "good" years emerge (2015, 2016, 2018) and two "bad years" (2017, 2019). This analysis will be useful for further research on the effect of health policies on hospital efficiency in other countries and periods.

A DEA-ANN-based analytical framework to assess and predict the efficiency of Canadian universities in a service supply chain context

Purpose

This research is about embedding service-based supply chain management (SCM) concepts in the education sector. Due to Canada's competitive education sector, the authors focus on Canadian universities.

Design/methodology/approach

The authors develop a framework for evaluating and forecasting university performance using data envelopment analysis (DEA) and artificial neural networks (ANNs) to assist education policymakers. The application of the proposed framework is illustrated based on information from 16 Canadian universities and by investigating their teaching and research performance.

Findings

The major findings are (1) applying the service SCM concept to develop a performance evaluation and prediction framework, (2) demonstrating the application of DEA-ANN for computing and predicting the efficiency of service SCM in Canadian universities, and (3) generating insights to enable universities to improve their research and teaching performances considering critical inputs and outputs.

Research limitations/implications

This paper presents a new framework for universities' performance assessment and performance prediction. DEA and ANN are integrated to aid decision-makers in evaluating the performances of universities.

Practical implications

The findings suggest that higher education policymakers should monitor attrition rates at graduate and undergraduate levels and provide financial support to facilitate research and concentrate on Ph.D. programs. Additionally, the sensitivity analysis indicates that selecting inputs and outputs is critical in determining university rankings.

Originality/value

This research proposes a new integrated DEA and ANN framework to assess and forecast future teaching and research efficiencies applying the service supply chain concept. The findings offer policymakers insights such as paying close attention to the attrition rates of undergraduate and postgraduate programs. In addition, prioritizing internal research support and concentrating on Ph.D. programs is recommended.

Developing and training artificial neural networks using bootstrap data envelopment analysis for best performance modeling of sawmills in Ontario

Purpose

The measurement capabilities of the data envelopment analysis (DEA) models are used to train the artificial neural network (ANN) models for the best performance modeling of the sawmills in Ontario. The bootstrap DEA models measure robust technical efficiency scores and have benchmarking abilities, whereas the ANN models use abstract learning from a limited set of information and provide the predictive power.

Design/methodology/approach

The complementary modeling approaches of the DEA and the ANN provide an adaptive decision support tool for each sawmill.

Findings

The trained ANN models demonstrate promising results in predicting the relative efficiency scores and the optimal combination of the inputs and the outputs for three categories (large, medium and small) of sawmills in Ontario. The average absolute error in predicting the relative efficiency scores varies from 0.01 to 0.04, and the predicted optimal combination of the inputs (roundwood and employees) and the output (lumber) demonstrate that a large percentage of the sawmills shows less than 10% error in the prediction results.

Originality/value

The purpose of this study is to develop an integrated DEA-ANN model that can help in the continuous improvement and performance evaluations of the forest industry working under uncertain business environment.

Analyzing the Lean Six Sigma enabled organizational performance to enhance operational efficiency

Purpose

The aim of this analysis is to review the Indian manufacturing organizations practicing Lean Six Sigma (LSS) tools/techniques with an objective of monitoring the performance of an organization and to develop recommendation for strategies to benchmark organizational operational efficiency.

Design/methodology/approach

This study offers insights of the LSS performance measurement aspects of the Indian manufacturing organizations based on Data envelopment analysis (DEA) approach. The five inputs and two outputs are considered on the basis of literature review and discussed with the practitioners.

Findings

In this analysis, the relative efficiency score of 18 Indian manufacturing organizations has been determined in order to assist evaluation of the impact of monetary investment on the outputs. The present analysis not only investigates the optimum level of input variables but also lays down a significant observation that an organization having higher profit and inventory turnover ratio is not necessarily an efficient organization.

Practical implications

The results assist to determine the best practice units, potential source of inefficiency and deliver beneficial data for the consistent enhancement of the operational efficiency. The DEA results assist managers and decision makers to derive appropriate strategies to enhance their performance with reference to the efficient organization and to regard it as their role model.

Originality/value

This analysis renders a DEA based framework of LSS practicing Indian manufacturing organizations. The framework is unique in terms of its input-outputs variable selection and measurement procedure.

Measuring performance of government-supported drug warehouses using DEA to improve quality of drug distribution

Purpose

Drug warehouses (DWs) play a crucial role in drug distribution of government-supported healthcare supply chain as it controls both the cost and responsiveness of the logistics activities. The current study proposes a methodology using data envelopment analysis (DEA) to estimate the performance along different dimensions and was applied to 30 government-supported DWs.

Design/methodology/approach

This study employs DEA to evaluate the performance and relative technical efficiency of DWs. In this research, four inputs and six outputs are identified based on intensive literature review and discussion with all stakeholders of DWs. The inputs are warehouse storage capacity, temperature-controlled storage capacity, number of skilled employees and operational cost, while the outputs are fill rate, number of generic drugs, volume of drugs, consumption points, inventory turns ratio and time efficiency.

Findings

Results show that 30% DWs operate at the most productive scale size with 100% efficiency level while 47% DWs have a significant possibility for further enhancement in productive efficiency and 23% DWs should diminish their operational size to increase their productivity level. It was also found that achieving 100% operational productivity along warehouse space capacity needs significant effort, whereas other three inputs, namely temperature-controlled capacity, number of skilled employees and operational cost, require comparatively less effort. Similarly, it was observed that the performance along the fill rate and time efficiency is satisfactory, whereas the performance along other fours output variables (i.e. number of generic drugs, volume of drugs, consumption points and inventory turns ratio) needs to be improved.

Practical implications

The findings offer insights on the inputs and outputs that significantly contribute to efficiencies so that inefficient DWs can focus on these factors.

Originality/value

Although many issues related to DEA have been widely researched and reported, but no literature has been found for analysis of DWs in general and government-supported DWs specifically to find out efficiencies for supply chain performance improvement.

An Approach Combining DEA and ANN for Hotel Performance Evaluation

For a hotel to succeed in the long run, it becomes vital to achieve higher profits along with increased performance. The performance evaluation of a hotel can signify its sustainable competitiveness within the hospitality industry. This article performs a two-stage study that combines data envelopment analysis (DEA) and artificial neural network (ANN) to evaluate hotel performance. The first stage to evaluate the efficiency for hotels is by using the DEA technique. The input variables considered are the number of rooms and the ratings corresponding to six aspects of a hotel (service, room, value, location, sleep quality, and cleanliness). Also, revenue per available room (RevPAR) and customer satisfaction (CS) are the output variables. The distinguishing factor of this article is that it involves the use of EWOM for performance evaluation. In the second stage, the performance of the hotels is judged by using the ANN technique. The ANN results showed that the performance of the hotels is quite good. Finally, discussions based on the results and scope for future studies are provided.

Collaboration quality assessment for sustainable supply chains: benchmarking

Purpose

The purpose of this paper is to propose an instrument to evaluate the collaboration quality of sustainable supply chains by considering collaboration behaviour and sustainability factors.

Design/methodology/approach

The proposed model integrates Quality Function Deployment (QFD), Fuzzy Analytical Network Process (FANP) and Data Envelopment Analysis (DEA) to assess the efficiency score of each stakeholder involved. A case of a sugar company in Indonesia was analysed as the illustration of the application of the model.

Findings

The integrated QFD-FANP-DEA method enables to incorporate collaboration behaviour and sustainability factors in a single assessment model. The results of the case study showed the benchmarking results associated with the performance variance of stakeholders and the number of samples.

Research limitations/implications

Future research could consider other DEA approaches, extend the research by simulating a different number of scenarios and evaluating the improvement/deterioration, and expand the scope of collaboration.

Practical implications

Each stakeholder will be able to obtain the collaboration assessment results, and the improvements suggested by the model.

Social implications

Smallholders and farmers will be able to increase their benefits by using the existing resources.

Originality/value

This paper provides a methodological contribution by introducing new collaboration quality assessment methods that can accommodate any variance in multi-stakeholders, involve qualitative and quantitative measurements, and benefit all stakeholders.

DEA and Monte Carlo simulation approach towards green car selection

Purpose

Automakers are engaged in manufacturing both efficient and inefficient green cars. The purpose of this paper is to categorize efficient green cars and inefficient green cars followed by improving efficiencies of identified inefficient green cars for distribution fitting.

Design/methodology/approach

The authors have used 2014 edition of secondary data published by the Automotive Research Centre of the Automobile Club of Southern California. The paper provides the methodology of applying data envelopment analysis (DEA) consisting of 50 decision-making units (DMUs) of green cars with six input indices (emission, braking, ride quality, acceleration, turning circle, and luggage capacity) and two output indices (miles per gallon and torque) integrated with Monte Carlo simulation for drawing significant statistical inferences graphically.

Findings

The findings of this study showed that there are 27 efficient and 23 inefficient DMUs along with improvement matrix. Additionally, the study highlighted the best distribution fitting of improved efficient green cars for respective indices.

Research limitations/implications

This study suffers from limitations associated with 2014 edition of secondary data used in this research.

Practical implications

This study may be useful for motorists with efficient listing of green cars, whereas automakers can be benefitted with distribution fitting of improved efficient green cars using Monte Carlo simulation for calibration.

Originality/value

The paper uses DEA to empirically examine classification of green cars and applies Monte Carlo simulation for distribution fitting to improved efficient green cars to decide appropriate range of their attributes for calibration.