Analytical approximations to predict performance measures of manufacturing systems with job failures and parallel processing

Parallel processing is prevalent in many manufacturing and service systems. Many manufactured products are built and assembled from several components fabricated in parallel lines. An example of this manufacturing system configuration is observed at a manufacturing facility equipped to assemble and test web servers. Characteristics of a typical web server assembly line are: multiple products, job circulation, and paralleling processing. The primary objective of this research was to develop analytical approximations to predict performance measures of manufacturing systems with job failures and parallel processing. The analytical formulations extend previous queueing models used in assembly manufacturing systems in that they can handle serial and different configurations of paralleling processing with multiple product classes, and job circulation due to random part failures. In addition, appropriate correction terms via regression analysis were added to the approximations in order to minimize the gap in the error between the analytical approximation and the simulation models. Markovian and general type manufacturing systems, with multiple product classes, job circulation due to failures, and fork and join systems to model parallel processing were studied. In the Markovian and general case, the approximations without correction terms performed quite well for one and two product problem instances. However, it was observed that the flow time error increased as the number of products and net traffic intensity increased. Therefore, correction terms for single and fork-join stations were developed via regression analysis to deal with more than two products. The numerical comparisons showed that the approximations perform remarkably well when the corrections factors were used in the approximations. In general, the average flow time error was reduced from 38.19% to 5.59% in the Markovian case, and from 26.39% to 7.23% in the general case. All the equations stated in the analytical formulations were implemented as a set of Matlab scripts. By using this set, operations managers of web server assembly lines, manufacturing or other service systems with similar characteristics can estimate different system performance measures, and make judicious decisions - especially setting delivery due dates, capacity planning, and bottleneck mitigation, among others.

The impact of institutional complexity and top management characteristics on executive compensation and firm performance

While most studies take a dyadic view when examining the environmental difference between the home country of a multinational enterprise (MNE) and a particular foreign country, they ignore that an MNE is managing a network of subsidiaries embedded in diverse environments. Additionally, neither the impacts of global environments on top executives nor the effects of top executives’ capabilities to handle institutional complexity are fully explored. Thus, using a three-essay format, this dissertation tried to fill these gaps by addressing the effects of institutional complexity and top management characteristics on top executive compensation and firm performance. ^ Essay 1 investigated the impact of an MNE’s institutional complexity, or the diversity of national institutions facing an MNE’s network of subsidiaries, on the top management team (TMT) compensation. This essay proposed that greater political and cultural complexity leads to not only greater TMT total compensation but also to a greater portion of TMT compensation linked with long-term performance. The arguments are supported in this essay by using an unbalanced panel dataset including 296 U.S. firms with 1,340 observations. ^ Essay 2 explored TMT social capital and its moderating role on value creation and appropriation by the chief executive officer (CEO). Using a sample with 548 U.S. firms and 2,010 observations, it found that greater TMT social capital does facilitate the effects of CEO intellectual capital and social capital on firm growth. Finally, essay 3 examined the performance implications for the fit between managerial information-processing capabilities and institutional complexity. It proposed that institutional complexity is associated with the needs of information-processing. On the other hand, smaller TMT turnover and larger TMT size reflect larger managerial information-processing capabilities. Consequently, superior performance is achieved by the match among institutional complexity, TMT turnover, and TMT size. All hypotheses in essay 3 are supported in a sample of 301 U.S. firms and 1,404 observations. ^ To conclude, this dissertation advances and extends our knowledge on the roles of institutional environments and top executives on firm performance and top executive compensation.^

A novel signal processing method for intraoperative neurophysiological monitoring in spinal surgeries

Intraoperative neurophysiologic monitoring is an integral part of spinal surgeries and involves the recording of somatosensory evoked potentials (SSEP). However, clinical application of IONM still requires anywhere between 200 to 2000 trials to obtain an SSEP signal, which is excessive and introduces a significant delay during surgery to detect a possible neurological damage. The aim of this study is to develop a means to obtain the SSEP using a much less, twelve number of recordings. The preliminary step involved was to distinguish the SSEP with the ongoing brain activity. We first establish that the brain activity is indeed quasi-stationary whereas an SSEP is expected to be identical every time a trial is recorded. An algorithm was developed using Chebychev time windowing for preconditioning of SSEP trials to retain the morphological characteristics of somatosensory evoked potentials (SSEP). This preconditioning was followed by the application of a principal component analysis (PCA)-based algorithm utilizing quasi-stationarity of EEG on 12 preconditioned trials. A unique Walsh transform operation was then used to identify the position of the SSEP event. An alarm is raised when there is a 10% time in latency deviation and/or 50% peak-to-peak amplitude deviation, as per the clinical requirements. The algorithm shows consistency in the results in monitoring SSEP in up to 6-hour surgical procedures even under this significantly reduced number of trials. In this study, the analysis was performed on the data recorded in 29 patients undergoing surgery during which the posterior tibial nerve was stimulated and SSEP response was recorded from scalp. This method is shown empirically to be more clinically viable than present day approaches. In all 29 cases, the algorithm takes 4sec to extract an SSEP signal, as compared to conventional methods, which take several minutes. The monitoring process using the algorithm was successful and proved conclusive under the clinical constraints throughout the different surgical procedures with an accuracy of 91.5%. Higher accuracy and faster execution time, observed in the present study, in determining the SSEP signals provide a much improved and effective neurophysiological monitoring process.

Analytical Approximations to Predict Performance Measures of Manufacturing Systems with Job Failures and Parallel Processing

Parallel processing is prevalent in many manufacturing and service systems. Many manufactured products are built and assembled from several components fabricated in parallel lines. An example of this manufacturing system configuration is observed at a manufacturing facility equipped to assemble and test web servers. Characteristics of a typical web server assembly line are: multiple products, job circulation, and paralleling processing. The primary objective of this research was to develop analytical approximations to predict performance measures of manufacturing systems with job failures and parallel processing. The analytical formulations extend previous queueing models used in assembly manufacturing systems in that they can handle serial and different configurations of paralleling processing with multiple product classes, and job circulation due to random part failures. In addition, appropriate correction terms via regression analysis were added to the approximations in order to minimize the gap in the error between the analytical approximation and the simulation models. Markovian and general type manufacturing systems, with multiple product classes, job circulation due to failures, and fork and join systems to model parallel processing were studied. In the Markovian and general case, the approximations without correction terms performed quite well for one and two product problem instances. However, it was observed that the flow time error increased as the number of products and net traffic intensity increased. Therefore, correction terms for single and fork-join stations were developed via regression analysis to deal with more than two products. The numerical comparisons showed that the approximations perform remarkably well when the corrections factors were used in the approximations. In general, the average flow time error was reduced from 38.19% to 5.59% in the Markovian case, and from 26.39% to 7.23% in the general case. All the equations stated in the analytical formulations were implemented as a set of Matlab scripts. By using this set, operations managers of web server assembly lines, manufacturing or other service systems with similar characteristics can estimate different system performance measures, and make judicious decisions - especially setting delivery due dates, capacity planning, and bottleneck mitigation, among others.

Microstructure Characteristics of U.S. Futures Markets

Prior finance literature lacks a comprehensive analysis of microstructure characteristics of U.S. futures markets due to the lack of data availability. Utilizing a unique data set for five different futures contract this dissertation fills this gap in the finance literature. In three essays price discovery, resiliency and the components of bid-ask spreads in electronic futures markets are examined. In order to provide comprehensive and robust analysis, both moderately volatile pre-crisis and volatile crisis periods are included in the analysis. The first essay entitled “Price Discovery and Liquidity Characteristics for U.S. Electronic Futures and ETF Markets” explores the price discovery process in U.S. futures and ETF markets. Hasbrouck’s information share method is applied to futures and ETF instruments. The information share results show that futures markets dominate the price discovery process. The results on the factors that affect the price discovery process show that when volatility increases, the price leadership of futures markets declines. Furthermore, when the relative size of bid-ask spread in one market increases, its information share decreases. The second essay, entitled “The Resiliency of Large Trades for U.S. Electronic Futures Markets,“ examines the effects of large trades in futures markets. How quickly prices and liquidity recovers after large trades is an important characteristic of financial markets. The price effects of large trades are greater during the crisis period compared to the pre-crisis period. Furthermore, relative to the pre-crisis period, during the crisis period it takes more trades until liquidity returns to the pre-block trade levels. The third essay, entitled “Components of Quoted Bid-Ask Spreads in U.S. Electronic Futures Markets,” investigates the bid-ask spread components in futures market. The components of bid-ask spreads is one of the most important subjects of microstructure studies. Utilizing Huang and Stoll’s (1997) method the third essay of this dissertation provides the first analysis of the components of quoted bid-ask spreads in U.S. electronic futures markets. The results show that order processing cost is the largest component of bid-ask spreads, followed by inventory holding costs. During the crisis period market makers increase bid-ask spreads due to increasing inventory holding and adverse selection risks.