International Journal of Data Envelopment Analysis
Volume:3 Issue: 2, Spring 2015

The main purpose of this paper is to investigate the performance of Nigerian insurance companies using Data Envelopment Analysis (DEA). Because of the unavailability of the required data, the study is limited to ten Nigerian insurance companies for the period of five years from 2008 to 2012. The input employed were commission expenses and management expenses, while premium and investment income were used as the output. Data were sourced from the respective insurance websites and African financial websites. DEA was the main methodology used in analyzing the data of this study while ratio analysis (liquid asset to total asset, total equity to total asset and return on asset) was also used in addition to the DEA. The overall result of the Total Factor Productivity (TFP) shows that Nigerian insurance industry is less efficient and this is caused by low level of Technical efficiency (EF) change including Technological change (TECH); this is also confirmed by the result of Latent Growth Curve Modeling (LGCM) which reveals that their efficiency over the period was declining. However, some of the leading insurance companies in terms of performance (TFP) are Leadway insurance, Standard Alliance insurance and Sovereign Trust insurance, among the insurance firms. The results of the ratio on the other hand reveal that Leadway is the highest in terms of profitability, Aiico was more liquid compared to the other firms and Oasis insurance finances more of its asset with shareholder’s fund compared to other insurance firms.

In this study, given the sequence dependent setup times, we attempt using the technique of Response Surface Methodology (RSM) to set the parameters of the genetic algorithm (GA), which is used to optimize the scheduling problem of n job on 1 machine (n/1). It aims at finding the most suitable parameters for increasing the efficiency of the proposed algorithm. At first, a central composite design was created and then using the data relating to the plan, the complete second-degree model was fitted. Then, by solving the developed non-linear programming model the optimal values of the parameters determined. The performance of algorithm, considering the obtained parameters as inputs of the common Data Envelopment Analysis (DEA), was measured. This way, we can decide on the most effective kinds of problems that can be solved by GA in a similar volume. This study can be used as a model of setting parameters of evolutionary and meta-heuristic algorithms using scientific techniques to prevent disadvantages relating to trial and error methods.

Evaluate the performance of companies on the Stock Exchange using non-parametric methods is very important. DEA and DEA-R with the strategies for piecewise linear frontier production function and use of available data, assess the stock company. In this study, using a neural network algorithm DEA and DEA-R is suggested to classify the first companies in the stock exchange; Secondly, using the cover models in the nature of input in technology and constant returns to scale Non-decreasing scale performance on each floor with propagation neural network is calculated. Thirdly, neural network training and repetition, scale efficiency is determined at the end of a functional study is presented on the company's stock.

One of the effective methods for improving the efficiency of an organization is benchmarking against successful organizations. Not only benchmarking could be a technique for identifying problems but also it greatly helps managers in relations of the design of processes. Among strategic and infrastructure industries in each country, the electricity industry is one of the most important and critical industries. Besides, it is considered as a unique industry due to its capital-intensive and costly nature. Hence, increasing efficiency and productivity in this industry is very important. This study was aimed at investigating and evaluating the efficiency of the above-mentioned companies by using the nonparametric data envelopment analysis (DEA) method. Moreover, since power transmission operations are carried out by regional electricity companies during multiple processes, network models were used in this study to evaluate the efficiency of regional electricity companies in Iran. The required information for efficiency analysis was extracted from the performance of listed companies in statistical yearbook of Iran electricity industry in the year 2011. The results indicate that regional electricity companies in Isfahan, Zanjan and Kerman achieved similar efficiency in both phases of their work. Moreover, the results show that the use of network models in evaluating the efficiency of Iran's regional electricity companies enables researchers to investigate the efficiency of internal processes of companies and give a vivid picture of the performance of an organization. These models also help in finding reasons for inefficiency of companies.

This paper aims at providing a new model based on Data Envelopment Analysis (DEA) to prioritize project risks. It is clear that the large amounts of involved capitals, the long term of infrastructure projects’ implementation, and the project management problems in on-time completion of projects indicate the necessity of paying particular attention to this issue and conducting applied research in this field. One of the important issues related to risk management is to identify the most appropriate project risks for the aim of adopting an appropriate strategy to manage them. The use of the secondary goal method in cross-sectional AHP/DEA was introduced as a more efficient model to prioritize road construction projects’ risks.

Recent bankruptcy of big companies all over the world and fluctuations in Iran's stock market require that some methods be developed for the evaluation of companies' financial potential. Different models are used for the prediction of bankruptcy and the evaluation of organizational financial situation. Environmental changes and increasing competition among agencies led to companies' and organizations' limited access to expected profit. Thus, financial decision making is, nowadays, more and more important, forcing managers to apply modern control methods through sophisticated techniques. The present study aims to evaluate the performance of companies' situation. For this purpose, we use the two models of data envelopment analysis and Zemijsky and compare results derived from them. The research data were gathered from 10 accepted in stock market. Results from data envelopment analysis model indicated that only one company was in a proper financial situation while results from Zemijsky's model showed that there were two companies in good financial condition. We also managed to develop strategies for the improvement of financial situation in other companies using data envelopment analysis model.