Matching Items (39)

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Local Freight Optimization

Description

In order to discover if Company X's current system of local trucking is the most efficient and cost-effective way to move freight between sites in the Western U.S., we will

In order to discover if Company X's current system of local trucking is the most efficient and cost-effective way to move freight between sites in the Western U.S., we will compare the current system to varying alternatives to see if there are potential avenues for Company X to create or implement an improved cost saving freight movement system.

Contributors

Agent

Created

Date Created
  • 2015-05

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Foundry Opportunities

Description

Company X is one of the world's largest semiconductor companies in the world, having a current market capitalization of 177.44 Billion USD, an enterprise value of 173.6 Billion USD, and

Company X is one of the world's largest semiconductor companies in the world, having a current market capitalization of 177.44 Billion USD, an enterprise value of 173.6 Billion USD, and generated 52.7 billion USD in revenue in fiscal year 2013. Recently, Company X has been looking to expand its Foundry business. The Foundry business in the semiconductor business is the actual process of making the chips. This process can be approached in several different ways by companies who need their chips built. A company, like TSMC, can be considered a pure-play company and only makes chips for other companies. A fabless company, like Apple, creates its own chip design and then allows another company to build them. It also uses other chip designs for its products, but outsources the building to another company. Lastly, the integrated device manufacturing companies like Samsung or Company X both design and build the chip. The foundry industry is a rather novel market for Company X because it owns less than 1 percent of the market. However, the industry itself is rather large, generating a total of 40 billion dollars in revenue annually, with expectations to have increasing year over year growth into the foreseeable future. The industry is fairly concentrated with TSMC being the top competitor, owning roughly 50 percent of the market with Samsung and Global Foundries lagging behind as notable competitors. It is a young industry and there is potential opportunity for companies that want to get into the business. For Company X, it is not only another market to get into, but also an added business segment to supplant their business segments that are forecasted to do poorly in the near future. This thesis will analyze the financial opportunity for Company X in the foundry space. Our final product is a series of P&L's which illustrate our findings. The results of our analysis were presented and defended in front of a panel of Company X managers and executives.

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Agent

Created

Date Created
  • 2015-05

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Collaborative Thesis: Supplier Tool Selection

Description

The goal of this thesis was to provide in depth research into the semiconductor wet-etch market and create a supplier analysis tool that would allow Company X to identify the

The goal of this thesis was to provide in depth research into the semiconductor wet-etch market and create a supplier analysis tool that would allow Company X to identify the best supplier partnerships. Several models were used to analyze the wet etch market including Porter's Five Forces and SWOT analyses. These models were used to rate suppliers based on financial indicators, management history, market share, research and developments spend, and investment diversity. This research allowed for the removal of one of the four companies in question due to a discovered conflict of interest. Once the initial research was complete a dynamic excel model was created that would allow Company X to continually compare costs and factors of the supplier's products. Many cost factors were analyzed such as initial capital investment, power and chemical usage, warranty costs, and spares parts usage. Other factors that required comparison across suppliers included wafer throughput, number of layers the tool could process, the number of chambers the tool has, and the amount of space the tool requires. The demand needed for the tool was estimated by Company X in order to determine how each supplier's tool set would handle the required usage. The final feature that was added to the model was the ability to run a sensitivity analysis on each tool set. This allows Company X to quickly and accurately forecast how certain changes to costs or tool capacities would affect total cost of ownership. This could be heavily utilized during Company X's negotiations with suppliers. The initial research as well the model lead to the final recommendation of Supplier A as they had the most cost effective tool given the required demand. However, this recommendation is subject to change as demand fluctuates or if changes can be made during negotiations.

Contributors

Agent

Created

Date Created
  • 2016-12

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The Valuation Effects of a Drug Recall

Description

The pharmaceutical industry is heavily regulated. This regulation results in a high number of recalls in this industry compared to other industries. The pharmaceutical industry is subject to high regulation

The pharmaceutical industry is heavily regulated. This regulation results in a high number of recalls in this industry compared to other industries. The pharmaceutical industry is subject to high regulation because of the harmful effects pharmaceuticals can have on consumers. In this paper I examine the valuation effects that a drug recall has on both the recalling firm and the recalling firm's rivals. I perform an event study analysis on the data. I show that there exists a statistically significant negative effect for a drug recall on the recalling firm's market value immediately surrounding the announcement. Additionally, there is a statistically significant positive effect for a drug recall on the recalling firm's rivals after the announcement.

Contributors

Agent

Created

Date Created
  • 2015-12

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Intel Collaborative Thesis, Substrate Group 2012

Description

Our thesis project aims to evaluate a major semiconductor company's (The Company) substrate supplier strategy in order to find the ideal number of suppliers that minimizes fixed cost and supplier

Our thesis project aims to evaluate a major semiconductor company's (The Company) substrate supplier strategy in order to find the ideal number of suppliers that minimizes fixed cost and supplier power. With The Company spending roughly $2.2 billion annually on substrates, supplier strategy has a significant impact on their costs. As a general rule in micro processing, the circuitry of the processor becomes twice as dense every two years. The substrate, being the pathway through which the process or with the motherboard, must become more advanced as well, although the technology does not grow at nearly the same speed. Leading the way in their industry, The Company is at the forefront of technology and produces the world's most advanced processing units. The suppliers The Company purchases from must be innovators in their own respective fields in order to be capable of handling such "bleeding-edge" technology; this requires a supplier to make a commitment to continuously work towards meeting The Company's constantly changing technological requirements. The ultimate goal of this project is to determine the ideal number of substrate suppliers that balances the effects of production costs and buying power to give the company the best overall purchase price.

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Agent

Created

Date Created
  • 2012-05

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MICROSOFT POWER BI CASE STUDY: AN EFFICIENT BUSINESS INTELLIGENCE SOLUTION

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

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.

Contributors

Agent

Created

Date Created
  • 2020-05

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Collaborative Thesis Project- Autonomous Vehicles

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

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.

Contributors

Agent

Created

Date Created
  • 2020-05

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Collaborative Thesis - Disrupting the Supply Chain: Application of A.I. and Machine Learning in Spares Inventory Management

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

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.

Contributors

Agent

Created

Date Created
  • 2020-05

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The effect of Elon Musk's tweets on Tesla stock price

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

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.

Contributors

Agent

Created

Date Created
  • 2020-05

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COMMERCIAL AIRLINE FUEL COSTS: HEDGING STRATEGIES AND PERFORMANCE

Description

This thesis examines the fuel hedging strategies and their performance in the airline industry. Hedging allows an airline to establish a semi-fixed cost for fuel prices in the future. Unexpected

This thesis examines the fuel hedging strategies and their performance in the airline industry. Hedging allows an airline to establish a semi-fixed cost for fuel prices in the future. Unexpected increases in fuel costs can easily move an airline into bankruptcy while a decrease in fuel prices can create massive profits. With fuel prices that can vary 70% in several months, many airlines hedge fuel costs in order to cap a massive expense for the company. It is extremely difficult for airlines, or anyone, to predict what fuel prices will do next week, yet alone next quarter. This thesis notes there is no advisable portion of fuel that should be hedged for any airline; it is instead a complex set of variables that must be analyzed for each individual firm on an ongoing basis. Hedging is notably advised if a firm can accept the added costs of hedging premiums, the wages of employees to actively manage a hedging portfolio and the additional accounting regulations that must be followed. It can be performed using a variety of hedging instruments and utilizing various commodities. Over time, hedging will have a net effect of zero, therefore adding zero value to the firm. In reality, it is assumed that hedging fuel costs will help stabilize fuel prices and therefore stabilize cash flows and profits. The ideal implication is that the market will respond to increased stability in profits with a higher value of the firms publicly traded stock.

Contributors

Agent

Created

Date Created
  • 2016-05