The COVID-19 pandemic has and will continue to radically shift the workplace. An increasing percentage of the workforce desires flexible working options and, as such, firms are likely to require less office space going forward. Additionally, the economic downturn caused by the pandemic provides an opportunity for companies to secure favorable rent rates on new lease agreements. This project aims to evaluate and measure Company X’s potential cost savings from terminating current leases and downsizing office space in five selected cities. Along with city-specific real estate market research and forecasts, we employ a four-stage model of Company X’s real estate negotiation process to analyze whether existing lease agreements in these cities should be renewed or terminated.

The COVID-19 pandemic has and will continue to radically shift the workplace. An increasing percentage of the workforce desires flexible working options and, as such, firms are likely to require less office space going forward. Additionally, the economic downturn caused by the pandemic provides an opportunity for companies to secure favorable rent rates on new lease agreements. This project aims to evaluate and measure Company X’s potential cost savings from terminating current leases and downsizing office space in five selected cities. Along with city-specific real estate market research and forecasts, we employ a four-stage model of Company X’s real estate negotiation process to analyze whether existing lease agreements in these cities should be renewed or terminated.

"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.

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.

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.

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.

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.

This paper classifies private equity groups (PEGs) seeking to engage in public to private transactions (PTPs) and determines (primarily through an examination of the implied merger arbitrage spread), whether certain reputational factors associated with the private equity industry affect a firm's ability to acquire a publicly-traded company. We use a sample of 1,027 US-based take private transactions announced between January 5, 2009 and August 2, 2018, where 333 transactions consist of private-equity led take-privates, to investigate how merger arbitrage spreads, offer premiums, and deal closure are impacted based on PEG- and PTP-specific input variables. We find that the merger arbitrage spread of PEG-backed deals are 2-3% wider than strategic deals, hostile deals have a greater merger arbitrage spread, larger bid premiums widen spreads and markets accurately identify deals that will close through a narrower spread. PEG deals offer lower premiums, as well as friendly deals and larger deals. Offer premiums are 8.2% larger among deals that eventually consummate. In a logistic regression, we identified that PEG deals are less likely to close than strategic deals, however friendly deals are much more likely to close and Mega Funds are more likely to consummate deals among their PEG peers. These findings support previous research on PTP deals. The insignificance of PEG-classified variables on arbitrage spreads and premiums suggest that investors do not differentiate PEG-backed deals by PEG due to most PEGs equal ability to raise competitive financing. However, Mega Funds are more likely to close deals, and thus, we identify that merger arbitrage spreads should be narrower among this PEG classification.

Company X has developed RealSenseTM technology, a depth sensing camera that provides machines the ability to capture three-dimensional spaces along with motion within these spaces. The goal of RealSense was to give machines human-like senses, such as knowing how far away objects are and perceiving the surrounding environment. The key issue for Company X is how to commercialize RealSense's depth recognition capabilities. This thesis addresses the problem by examining which markets to address and how to monetize this technology. The first part of the analysis identified potential markets for RealSense. This was achieved by evaluating current markets that could benefit from the camera's gesture recognition, 3D scanning, and depth sensing abilities. After identifying seven industries where RealSense could add value, a model of the available, addressable, and obtainable market sizes was developed for each segment. Key competitors and market dynamics were used to estimate the portion of the market that Company X could capture. These models provided a forecast of the discounted gross profits that could be earned over the next five years. These forecasted gross profits, combined with an examination of the competitive landscape and synergistic opportunities, resulted in the selection of the three segments thought to be most profitable to Company X. These segments are smart home, consumer drones, and automotive. The final part of the analysis investigated entrance strategies. Company X's competitive advantages in each space were found by examining the competition, both for the RealSense camera in general and other technologies specific to each industry. Finally, ideas about ways to monetize RealSense were developed by exploring various revenue models and channels.

The purpose of our research was to develop recommendations and/or strategies for Company A's data center group in the context of the server CPU chip industry. We used data collected from the International Data Corporation (IDC) that was provided by our team coaches, and data that is accessible on the internet. As the server CPU industry expands and transitions to cloud computing, Company A's Data Center Group will need to expand their server CPU chip product mix to meet new demands of the cloud industry and to maintain high market share. Company A boasts leading performance with their x86 server chips and 95% market segment share. The cloud industry is dominated by seven companies Company A calls "The Super 7." These seven companies include: Amazon, Google, Microsoft, Facebook, Alibaba, Tencent, and Baidu. In the long run, the growing market share of the Super 7 could give them substantial buying power over Company A, which could lead to discounts and margin compression for Company A's main growth engine. Additionally, in the long-run, the substantial growth of the Super 7 could fuel the development of their own design teams and work towards making their own server chips internally, which would be detrimental to Company A's data center revenue. We first researched the server industry and key terminology relevant to our project. We narrowed our scope by focusing most on the cloud computing aspect of the server industry. We then researched what Company A has already been doing in the context of cloud computing and what they are currently doing to address the problem. Next, using our market analysis, we identified key areas we think Company A's data center group should focus on. Using the information available to us, we developed our strategies and recommendations that we think will help Company A's Data Center Group position themselves well in an extremely fast growing cloud computing industry.