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Description
The current Enterprise Requirements and Acquisition Model (ERAM), a discrete event simulation of the major tasks and decisions within the DoD acquisition system, identifies several what-if intervention strategies to improve program completion time. However, processes that contribute to the program acquisition completion time were not explicitly identified in the simulation study. This research seeks to determine the acquisition processes that contribute significantly to total simulated program time in the acquisition system for all programs reaching Milestone C. Specifically, this research examines the effect of increased scope management, technology maturity, and decreased variation and mean process times in post-Design Readiness Review contractor activities by performing additional simulation analyses. Potential policies are formulated from the results to further improve program acquisition completion time.
ContributorsWorger, Danielle Marie (Author) / Wu, Teresa (Thesis director) / Shunk, Dan (Committee member) / Wirthlin, J. Robert (Committee member) / Industrial, Systems (Contributor) / Barrett, The Honors College (Contributor)
Created2013-05
Description
Workplace productivity is a result of many factors, and among them is the setup of the office and its resultant noise level. The conversations and interruptions that come along with converting an office to an open plan can foster innovation and creativity, or they can be distracting and harm the performance of employees. Through simulation, the impact of different types of office noise was studied along with other changing conditions such as number of people in the office. When productivity per person, defined in terms of mood and focus, was measured, it was found that the effect of noise was positive in some scenarios and negative in others. In simulations where employees were performing very similar tasks, noise (and its correlates, such as number of employees), was beneficial. On the other hand, when employees were engaged in a variety of different types of tasks, noise had a negative overall effect. This indicates that workplaces that group their employees by common job functions may be more productive than workplaces where the problems and products that employees are working on are varied throughout the workspace.
ContributorsHall, Mikaela Starrantino (Author) / Pavlic, Theodore P. (Thesis director) / Cooke, Nancy (Committee member) / Industrial, Systems (Contributor) / Barrett, The Honors College (Contributor)
Created2017-05
Description
Abstract Chess has been a common research topic for expert-novice studies and thus for learning science as a whole because of its limited framework and longevity as a game. One factor is that chess studies are good at measuring how expert chess players use their memory and skills to approach a new chessboard con�guration. Studies have shown that chess skill is based on memory, speci�cally, "chunks" of chess piece positions that have been previously encountered by players. However, debate exists concerning how these chunks are constructed in players' memory. These chunks could be constructed by proximity of pieces on the chessboard as well as their precise location or constructed through attack-defense relations. The primary objective of this study is to support which one is more in line with chess players' actual chess abilities based off their memory, proximity or attack/defense. This study replicates and extends an experiment conducted by McGregor and Howe (2002), which explored the argument that pieces are primed more by attack and defense relations than by proximity. Like their study, the present study examined novice and expert chess players' response times for correct and error responses by showing slides of game configurations. In addition to these metrics, the present study also incorporated an eye-tracker to measure visual attention and EEG to measure affective and cognitive states. They were added to allow the comparison of subtle and unconscious behaviors of both novices and expert chess players. Overall, most McGregor and Howe's (2002) results were replicated supporting their theory on chess expertise. This included statistically significance for skill in the error rates with the mean error rates on the piece recognition tests were 70.1% for novices and 87.9% for experts, as well as significance for the two-way interaction for relatedness and proximity with error rates of 22.4% for unrelated/far, 18.8% for related/far, 15.8% for unrelated
ear, and 29.3% for related
ear. Unfortunately, there were no statistically significance for any of the response time effects, which McGregor and Howe found for the interaction between skill and proximity. Despite eye-tracking and EEG data not either support nor confirm McGregor and Howe's theory on how chess players memorize chessboard configurations, these metrics did help build a secondary theory on how novices typically rely on proximity to approach chess and new visual problems in general. This was exemplified by the statistically significant results for short-term excitement for the two-way interaction of skill and proximity, where the largest short-term excitement score was between novices on near proximity slides. This may indicate that novices, because they may lean toward using proximity to try to recall these pieces, experience a short burst of excitement when the pieces are close to each other because they are more likely to recall these configurations.
ear, and 29.3% for related
ear. Unfortunately, there were no statistically significance for any of the response time effects, which McGregor and Howe found for the interaction between skill and proximity. Despite eye-tracking and EEG data not either support nor confirm McGregor and Howe's theory on how chess players memorize chessboard configurations, these metrics did help build a secondary theory on how novices typically rely on proximity to approach chess and new visual problems in general. This was exemplified by the statistically significant results for short-term excitement for the two-way interaction of skill and proximity, where the largest short-term excitement score was between novices on near proximity slides. This may indicate that novices, because they may lean toward using proximity to try to recall these pieces, experience a short burst of excitement when the pieces are close to each other because they are more likely to recall these configurations.
ContributorsSeto, Christian Paul (Author) / Atkinson, Robert (Thesis director) / Runger, George (Committee member) / Industrial, Systems (Contributor, Contributor) / Barrett, The Honors College (Contributor)
Created2017-05
Description
The first step in process improvement is to scope the problem, next is measure the current process, but if data is not readily available and cannot be manually collected, then a measurement system must be implemented. General Dynamics Mission Systems (GDMS) is a lean company that is always seeking to improve. One of their current bottlenecks is the incoming inspection department. This department is responsible for finding defects on parts purchased and is critical to the high reliability product produced by GDMS. To stay competitive and hold their market share, a decision was made to optimize incoming inspection. This proved difficult because no data is being collected. Early steps in many process improvement methodologies, such as Define, Measure, Analyze, Improve and Control (DMAIC), include data collection; however, no measurement system was in place, resulting in no available data for improvement. The solution to this problem was to design and implement a Management Information System (MIS) that will track a variety of data. This will provide the company with data that will be used for analysis and improvement. The first stage of the MIS was developed in Microsoft Excel with Visual Basic for Applications because of the low cost and overall effectiveness of the software. Excel allows update to be made quickly, and allows GDMS to collect data immediately. Stage two would be moving the MIS to a more practicable software, such as Access or MySQL. This thesis is only focuses on stage one of the MIS, and GDMS will proceed with stage two.
ContributorsDiaz, Angel (Author) / McCarville, Daniel R. (Thesis director) / Pavlic, Theodore (Committee member) / Industrial, Systems (Contributor) / Barrett, The Honors College (Contributor)
Created2017-05
Description
In this study, the implementation of educational technology and its effect on learning and user experience is measured. A demographic survey, pretest/posttest, and educational experience survey was used to collect data on the control and experimental groups. The experimental group was subjected to different learning material than the control group with the use of the Elements 4D mobile application by Daqri to learn basic chemical elements and compounds. The control group learning material provided all the exact information as the application, but in the 2D form of a printed packet. It was expected the experimental group would outperform the control group and have a more enjoyable experience and higher performance. After data analysis, it was concluded that the control group outperformed the experimental group on performance and both groups has similar experiences in contradiction to the hypothesis. Once the factors that contribute to the limitations of different study duration, learning the application beforehand, and only-memorization questions are addressed, the study can be conducted again. Application improvements may also alter the future results of the study and hopefully lead to full implementation into a curriculum.
ContributorsApplegate, Garrett Charles (Author) / Atkinson, Robert (Thesis director) / Chavez-Echeagaray, Maria Elena (Committee member) / Industrial, Systems (Contributor) / Barrett, The Honors College (Contributor)
Created2017-05
Description
The overall energy consumption around the United States has not been reduced even with the advancement of technology over the past decades. Deficiencies exist between design and actual energy performances. Energy Infrastructure Systems (EIS) are impacted when the amount of energy production cannot be accurately and efficiently forecasted. Inaccurate engineering assumptions can result when there is a lack of understanding on how energy systems can operate in real-world applications. Energy systems are complex, which results in unknown system behaviors, due to an unknown structural system model. Currently, there exists a lack of data mining techniques in reverse engineering, which are needed to develop efficient structural system models. In this project, a new type of reverse engineering algorithm has been applied to a year's worth of energy data collected from an ASU research building called MacroTechnology Works, to identify the structural system model. Developing and understanding structural system models is the first step in creating accurate predictive analytics for energy production. The associative network of the building's data will be highlighted to accurately depict the structural model. This structural model will enhance energy infrastructure systems' energy efficiency, reduce energy waste, and narrow the gaps between energy infrastructure design, planning, operation and management (DPOM).
ContributorsCamarena, Raquel Jimenez (Author) / Chong, Oswald (Thesis director) / Ye, Nong (Committee member) / Industrial, Systems (Contributor) / Barrett, The Honors College (Contributor)
Created2016-12