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Optimization of Incoming Inspection

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

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.

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2017-05

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A Simulation Model of the Effect of Workplace Structure on Productivity

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

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.

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2017-05

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An Empirical Approach to Developing Leadership Skills: Engaging High School Students in Professional Education

Description

The Performance Based Studies Research Group (PBSRG) has developed industry-tested leadership and management techniques that have been proven to increase organizational performance. The Leadership Society of Arizona (LSA) has worked closely with PBSRG to develop an educational framework that introduces

The Performance Based Studies Research Group (PBSRG) has developed industry-tested leadership and management techniques that have been proven to increase organizational performance. The Leadership Society of Arizona (LSA) has worked closely with PBSRG to develop an educational framework that introduces these leadership concepts to college students. LSA is now endeavoring to make this curriculum more accessible for K-12 students and educators. As part of a thesis creative project, the author has developed a strategy to connect with and enable local high schools, teachers, and students to engage with the professional industry and higher education. This strategy will allow LSA to connect with up to 150 high school students over the summer of 2016. By making this education easily accessible, the author has accomplished a milestone in the larger effort encompassed by LSA. The course chosen to present to high school students is an abridged variation of the Barrett Honors College course "Deductive Logic: Leadership and Management Techniques". The class framework is designed to instantiate a self-sustaining program for future summer school courses. The summer school course will allow high school students to learn, understand, and apply college level concepts into their education, work, and personal lives. The development of the framework for the program encompasses networking/partnering efforts, marketing package creation, and the delivery of the summer school course over the months of June and July in 2016.

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2016-05

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Data and Predictive Analytics for Energy Use

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

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).

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2016-12