Barrett, The Honors College Thesis/Creative Project Collection
Barrett, The Honors College at Arizona State University proudly showcases the work of undergraduate honors students by sharing this collection exclusively with the ASU community.
Barrett accepts high performing, academically engaged undergraduate students and works with them in collaboration with all of the other academic units at Arizona State University. All Barrett students complete a thesis or creative project which is an opportunity to explore an intellectual interest and produce an original piece of scholarly research. The thesis or creative project is supervised and defended in front of a faculty committee. Students are able to engage with professors who are nationally recognized in their fields and committed to working with honors students. Completing a Barrett thesis or creative project is an opportunity for undergraduate honors students to contribute to the ASU academic community in a meaningful way.
Displaying 1 - 5 of 5
Description
This thesis investigates the use of MS Power BI in the case company’s heterogeneous computing environment. The empirical evidence was collected through the authors’ own observations and exposure to the modeling of dashboards, other supported external findings from interviews, published articles, academic journals, and speaking with leading experts at the WA ‘Dynamic Talks Seattle/Redmond: Big Data Analytics’ conference. Power BI modeling is effective for advancing the development of statistical thinking and data retrieving skills, finding trends and patterns in data representations, and making predictions. Computer-based data modeling gave meaning to math results, and supported examining implications of these results with simple charts to improve perception. Querying and other add-ins that would be seen as affordances when using other BI softwares, with some complexity removed in Power BI, make modeling data an easier undertaking for report builders. Using computer-based qualitative data analysis software, this paper details opportunities and challenges of data modeling with dashboards. Simple linear regression is used for case study use only.
ContributorsKusen, Alexandra Jeshua (Co-author) / Briones, Jared (Co-author) / Fugleberg, Aaron (Co-author) / Lin, Amy (Co-author) / Simonson, Mark (Thesis director) / Hertzel, Michael (Committee member) / Department of Finance (Contributor) / Barrett, The Honors College (Contributor)
Created2020-05
Description
Elon Musk is known for making controversial tweets, which often lead to lawsuits. Our thesis focuses on analyzing the effect that these individual tweets have on stock prices. Our hypothesis focuses on the idea that when Elon Musk makes a controversial tweet, the volatility of Tesla stock will increase, while the price of Tesla stock will on average decrease. The thirteen tweets that we are examining are the tweets that we deemed to be most important, which are measured by the amount of press coverage that they have received. We also evaluated the effect that two different lawsuits that stemmed from Musk’s reckless tweets had on Tesla stock. After evaluating the effect that Elon Musk’s tweets had on the stock volume and price, we will then determine whether or not Elon Musk and other CEO’s alike should be able to tweet in a similar manner. In order to analyze stock movement, volume, and significance we imported statistical data from Yahoo Finance and Nasdaq into Excel. From there, We added charts to model the volatility and the direction of price data. Additionally, we created separate indexes to compare stock moves and test for abnormal returns. From these returns we were able to calculate the alpha and beta for Tesla, its peers and competitors. To analyze Musk’s tweets, we collected close to 7,000 tweets and ordered them chronologically in Excel. With the combination of the stock and tweet data, we were in an excellent spot to analyze the data and come to a conclusion.
ContributorsDe Roo, Gilles (Co-author) / Lueck, Elliott (Co-author) / Budolfson, Arthur (Thesis director) / Hertzel, Michael (Committee member) / Department of Finance (Contributor) / Barrett, The Honors College (Contributor)
Created2020-05
Description
By 2030, annual global automobile production is projected to reach over 110 million vehicles with an increasing quantity having autonomous capabilities. Based on this trend, Company X is poised to drive profits by leveraging advancing technology from their subsidiary to gain significant market share within the AV industry. This will solidify Company X’s position as a key player and leader within the AV industry, which is expected to grow to $7 trillion by 2050, and Company X can achieve this by providing a technology suite including a systems on a chip to auto manufacturers and creating partnerships in the technology and automotive industry.
ContributorsAvery, Hailey (Co-author) / Green, Ryan (Co-author) / Hall, Robert (Co-author) / Hummel, Haley (Co-author) / Simonson, Mark (Thesis director) / Hertzel, Michael (Committee member) / Department of Finance (Contributor) / Department of Information Systems (Contributor) / Barrett, The Honors College (Contributor)
Created2020-05
Description
This thesis discusses the case for Company X to improve its vast supply chain by implementing an artificial intelligence solution in the management of its spare parts inventory for manufacturing-related machinery. Currently, the company utilizes an inventory management system, based on previously set minimum and maximum thresholds, that doesn’t use predictive analytics to stock required spares inventory. This results in unnecessary costs and redundancies within the supply chain resulting in the stockout of spare parts required to repair machinery. Our research aimed to quantify the cost of these stockouts, and ultimately propose a solution to mitigate them. Through discussion with Company X, our findings led us to recommend the use of Artificial Intelligence (A.I.) within the inventory management system to better predict when stockouts would occur. As a result of data availability, our analysis began on a smaller scale, considering only a single manufacturing site at Company X. Later, our findings were extrapolated across all manufacturing sites. The analysis includes the cost of stockouts, the capital that would be saved with A.I. implementation, costs to implement this new A.I. software, and the final net present value (NPV) that Company X could expect in 10 years and 25 years. The NPV calculations explored two scenarios, an external partnership and the purchase of a small private company, that lead to our final recommendations regarding the implementation of an A.I. software solution in Company X’s spares inventory management system. Following the analysis, a qualitative discussion of the potential risks and market opportunities associated with the explored implementation scenarios further guided the determination of our final recommendations.
ContributorsHolohan, Joseph Michael Houston (Co-author) / Shahriari, Rosie (Co-author) / Aun, Jose (Co-author) / Heineke, Christopher (Co-author) / Gurrola, Macario (Co-author) / Simonson, Mark (Thesis director) / Hertzel, Michael (Committee member) / Department of Finance (Contributor) / Barrett, The Honors College (Contributor)
Created2020-05
Description
"Company X," a technology company, is known for being one of the world’s largest semiconductor chip manufacturers; however, they are also one of the largest authors of software. In 2019, "Company X" entered a new paradigm where, according to the CEO, while "Company X"’s core strategy has not changed, "Company X" is embracing the transition to a data-centric company from a PC-centric company. The scope that the project examines is--in this transition to a data-centric company and based on the company's current expertise and competitive advantages--should "Company X" be branching into an additional division or leverage existing intellectual property (IP)? The goal of the project is to understand how "Company X" can leverage its expertise in hardware and software service packages to maximize the value of the company.
ContributorsArellano, Andrea (Co-author) / Roos, Bailey (Co-author) / Broas, Joshua (Co-author) / Kotti, Abhigyan (Co-author) / Simonson, Mark (Thesis director) / Hertzel, Michael (Committee member) / Dean, W.P. Carey School of Business (Contributor) / Department of Finance (Contributor) / Barrett, The Honors College (Contributor)
Created2020-05