One of the primary concerns in most financial institutions to have an appropriate method for ranking customers. Bank customers are the primary sources of creating income and the success of banking industry depends on how to select good customers for allocation of loans. This paper uses Decision Tree, K-nearest neighbor (KNN), Support Vector Machine (SVM), Naive Bayes, and Logistic Regression for data categorization to estimate credit ranking of bank customers in one of major banks in Middle East. The results indicate that Logistic Regression was considered as the best method for ranking customers with the precision of 76.17% while Decision Tree was considered as the weakest technique with the precision of 73.30%.

Risk management plays an important role on the success of the any project. There are several techniques, which are available to help different phases of risk management process. This paper presents a survey to identify the necessary tools, which are mostly implemented and related to the success of any project management, and more specifically with effective project risk management. The survey is based on a questionnaire designed to a sample of project managers from the construction industries. The response data was studied to determine which tools are more used in the organizations, which could yield a better project management performance.

Selection of an appropriate contractor plays an important role for the success of the accomplish-ment of any construction project. This paper presents a multi-criteria decision making method for contractor selection. The proposed study uses six criteria, namely; Experience, Financial stability, Quality performance, Manpower resources, Equipment resources and Current workload for evaluating different contractors. Using analytical hierarchy process, the study ranks these criteria and finds the relative importance of them. Next, The technique for Order of Preference by Simi-larity to ideal Solution (TOPSIS) is used to rank the alternative contractors according to these criteria.

One of the primary tools for asset evaluation on stock market is to use price-to-earnings (P/E) ratio. The method is simple and has become popular among many investors for buy/sell decisions. In this paper, we present a comprehensive review on recent advances on the use of P/E ratio for measuring other firms’ characteristics. The survey has reviewed several studies on the relationship between P/E ratio and stock performance, estimation of transaction data, insider transaction, future growth, firm size, interest ratio, book-to-market equity, etc.

Reliability and quality assurance have become major considerations in the design and manufacture of today’s parts and products. Survivability of green compact using powder metallurgy technology is considered as one of the major quality attributes in manufacturing systems today. During powder metallurgy (PM) production, the compaction conditions and behavior of the metal powder dictate the stress and density distribution in the green compact prior to sintering. These parameters greatly influence the mechanical properties and overall strength of the final component. In order to improve these properties, higher compaction pressures are usually employed, which make unloading and ejection of green compacts more challenging, especially for the powder-compacted parts with relatively complicated shapes. This study looked at a mathematical survivability model concerning green compact characteristics in PM technology and the stress-strength failure model in reliability engineering. This model depicts the relationship between mechanical loads (stress) during ejection, experimentally determined green strength and survivability of green compact. The resulting survivability is the probability that a green compact survives during and after ejection. This survivability model can be used as an efficient tool for selecting the appropriate parameters for the process planning stage in PM technology. A case study is presented here in order to demonstrate the application of the proposed survivability model.

This paper investigates the dynamics of the NASDAQ topology before, during, and after 2008 financial crisis. First, multiresolution analysis by virtue of wavelet transform is employed to denoise each NASDAQ sector return series. Second, the correlation matrix of sectors is built and analyzed in each time period to view comovements of sectors. Third, hierarchical clustering trees are constructed in each time period to find out how the structure of the NASDAQ market evolves through time. Our results suggest that interrelationships between sectors become stronger in times of crisis and especially in post-crisis period. In addition, some markets tend to form the same cluster in all time periods; for instance the Industrial and Bank sectors and the Telecommunication and Computer sectors. However, the general topology of the NASDAQ market has been considerably changed over periods. In sum, the complex structure of the NASDAQ market is dynamic and is more integrated after 2008 financial crisis. This result indicates that there are less diversification opportunities in the post-crisis period in comparison with pre-crisis period. These empirical findings are important for the development of subsequent portfolio strategies.

Recently, there have been significant development on e-business and nearly all business partners try to offer their products and services via internet. One primary question for the implementation of e-business is to measure the effect of this new facility on supply chain in project based organizations. Although e-business may not directly influence on organizational performance, it definitely influences on unifying customers and suppliers, which yields to a better performance of organizations. This study performs a study to measure the indirect effect of e-business on project based organizational performance. The proposed study of this paper designs a questionnaire and distributes it among 140 professional experts in different industries in province of Ontario, Canada. The survey examines different hypotheses for a possible correlation between e-business and integrated suppliers, e-business and customers, integrated customers and suppliers with organizational performance. The results of this survey indicate a positive relationship between all these components either directly or indirectly.

In order to prepare students for the workforce, academic programs incorporate a variety of tools that students are likely to use in their future careers. One of these tools employed by business and technology programs is the integration of live software applications such as SAP through the SAP University Alliance (SAP UA) program. Since the SAP UA program has been around for only about 10 years and the available literature on the topic is limited, research is needed to determine the strengths and weaknesses of the SAP UA program. A collaborative study of SAP UA faculty perceptions of their SAP UAs was conducted in the fall of 2011. Of the faculty invited to participate in the study, 31% completed the online survey. The results indicate that most faculty experienced difficulty implementing SAP into their programs and report that a need exists for more standardized curriculum and training, while a large percentage indicated that they are receiving the support they need from their schools and SAP.

In this study, the backpropagation neural network (BPNN) is tested for the ability to forecast the daily volatility of two stock market indices from the Middle East and North Africa (MENA) region using volume; namely Morocco and Saudi Arabia. Volatility series were estimated using the Exponential Auto-Regressive Conditional Heteroskedasticity (EGARCH) model. The simulation results show that trading volume helps improving the forecasting accuracy of BPNN in Morocco but not in Saudi Arabia. As a result, volume represents valuable information flow to be used in the modeling and prediction of volatility in Morocco. In addition, it is found that BPNN overpredicts volatility during high volatile periods. This finding is important in financial applications such as asset allocation and derivatives pricing.

The bullwhip effect happens whenever the demand order fluctuations in the supply chain (SC) escalate as they are transferred up the SC. In fact, a small change in point-of-sale demand may be interpreted by SC participants as a much bigger variability in demand. This looks like a cracking a whip, where a small flick of the wrist may yield a large motion at the end of the whip. Misstate data from one side of a SC to the other part may yield substantial sloppiness. This includes increase in inventories, shortage of cash flow, weak customer satisfaction, etc. Enterprises may efficiently reduce the bullwhip effect by completely learning its root causes. This paper presents an overview on the concept and recent development of the bullwhip effect.