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A better assessment of the neural network architecturesE performance will help in predicting foreign exchange correlations. The selection of proper architectural design leads to the success in neural network forecasting.

This case study develops an innovative management and scheduling system for corrective maintenance of machines in a manufacturing facility. The study also involves a comparative evaluation of the proposed and the existing systems under a spectrum of operating conditions. A comprehensive simulation is used to evaluate system performances under a variety of settings which include reliability, service level, and cost consequences. The analysis is based on a full factorial experimental design. In summary, the developed self-regulating management system which involves dynamic work allocation and pre-emption is shown to yield higher machine availability and higher mechanic utilisation even with fewer mechanics. The study also finds that the new system is more streamlined, agile, and robust although it is subject to more-constrained machine reliability and mechanic service time environments. Further, a major reduction of current manpower can still achieve at least 95% machine availability, illustrating the cost effectiveness and efficacy of the developed system. This rule-based corrective maintenance system can be operated in uncertain environments on a real-time basis without additional reformatting costs and provides a competitive measure to deal with managerial issues such as low retention rate for skilled mechanics, highly uneven training levels and pay scales. The financial consequences and gains in strategic advantage with respect to the facility's operational structure are promising after implementation. Moreover, the system developed in this case study represents a meaningful starting point for a more vigorous theoretical research on the bucket brigade system to different functions in industrial and operations management. [ABSTRACT FROM AUTHOR]

Over the recent two decades, e-commerce has been evolving and becoming an indispensable part of businesses. Most businesses especially those in the retail and service sectors now conduct some forms of their operations online. These businesses are able to gain easy access to and collect vast amount of data in e-commerce for customer analytics. The information obtained from customer analytics are used for direct marketing and customer relationship management. To improve service level and financial performance of the online operations, a typical management team has to select and subsequently use proper approaches to analyze the data. This chapter presents and reviews a selected number of data analytic methods commonly used in e-commerce research. The reviewed methods are divided into two domains, i.e., traditional statistical methods and machine learning algorithms. Specifically, factor analysis, logistic regression, cluster analysis, and structural equation modeling are the reviewed traditional statistical methods whereas artificial neural network, support vector machine, evolutionary algorithms, and decision tree induction are the reviewed machine learning algorithms. Each section of the chapter focuses on one data analytic method and presents a brief technical overview and the corresponding empirical modeling issues. Following the technical exposition, a number of relevant studies published in the literature are selected to exemplify applications of the method in recent e-commerce research. This chapter is expected to provide researchers and practitioners an insightful survey of different methodologies and help them identify potential areas for future studies and applications in e-commerce data analytics. [ABSTRACT FROM AUTHOR]

In the last decade, neural networks have emerged from an esoteric instrument in academic research to a rather common tool assisting auditors, investors, portfolio managers and investment advisors in making critical financial decisions. It is apparent that a better understanding of the network's performance and limitations would help both researchers and practitioners in analysing real-world problems. Unlike many existing studies which focus on a single type of network architecture, this study evaluates and compares the performance of models based on two competing neural network architectures, the multi-layered feedforward neural network (MLFN) and general regression neural network (GRNN). Our empirical evaluation measures the network models' strength on the prediction of currency exchange correlation with respect to a variety of statistical tests including RMSE, MAE, U statistic, Theil's decomposition test, Henriksson–Merton market timing test and Fair–Shiller informational content test. Results of experiments suggest that the selection of proper architectural design may contribute directly to the success in neural network forecasting. In addition, market timing tests indicate that both MLFN and GRNN models have economically significant values in predicting the exchange rate correlation. On the other hand, informational content tests discover that the neural network models based on different architectures capture useful information not found in each other and the information sets captured by the two network designs are independent of one another. An auxiliary experiment is developed and confirms the possible synergetic effect from combining forecasts made by the two different network architectures and from incorporating information from an implied correlation model into the neural network forecasts. Implied correlation and random walk models are also included in our empirical experiment for benchmark comparison. Copyright © 2005 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]