As the IoT (Internet of Things) market continues to grow, Company X needs to find a way to penetrate the market and establish larger market share. The problem with Company X's current strategy and cost structure lies in the fact that the fastest growing portion of the IoT market is microcontrollers (MCUs). As Company X currently holds its focus in manufacturing microprocessors (MPUs), the current manufacturing strategy is not optimal for entering competitively into the MCU space. Within the MCU space, the companies that are competing the best do not utilize such high level manufacturing processes because these low cost products do not demand them. Given that the MCU market is largely untested by Company X and its products would need to be manufactured at increasingly lower costs, it runs the risk of over producing and holding obsolete inventory that is either scrapped or sold at or below cost. In order to eliminate that risk, we will explore alternative manufacturing strategies for Company X's MCU products specifically, which will allow for a more optimal cost structure and ultimately a more profitable Internet of Things Group (IoTG). The IoT MCU ecosystem does not require the high powered technology Company X is currently manufacturing and therefore, Company X loses large margins due to its unnecessary leading technology. Since cash is king, pursuing a fully external model for MCU design and manufacturing processes will generate the highest NPV for Company X. It also will increase Company X's market share, which is extremely important given that every tech company in the world is trying to get its hands into the IoT market. It is possible that in ten to thirty years down the road, Company X can manufacture enough units to keep its products in-house, but this is not feasible in the foreseeable future. For now, Company X should focus on the cost market of MCUs by driving its prices down while maintaining low costs due to the variables of COGS and R&D given in our fully external strategy.

This thesis looks into the current method a particular company uses to value its inventory carrying costs (ICC). By identifying costs incurred during all stages of production, along with incorporating industry standards and academic research while avoiding the shortcomings of the company's current method, this thesis was able to derive a more comprehensive and manageable tool for measuring ICC. Our findings led to concrete recommendations, which will provide real value to company managers by improving the accuracy of project finance calculations, supply chain optimization modeling, and numerous other decisions relying on accurate inventory data inputs.

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.

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.

This paper examines the qualitative and quantitative effects of the 2008 financial crisis on the current landscape of the investment banking industry. We begin by reviewing what occurred during the financial crisis, including which banks took TARP money, which banks became bank holding companies, and significant mergers and acquisitions. We then examine the new regulations that were created in reaction to the crisis, including the Dodd-Frank Act. In particular, we focus on the Volcker Rule, which is a section of the act that prohibits proprietary trading and other risky activities at banks. Then we shift into a quantitative analysis of the changes that banks made from the years 2005-2016. To do this, we chose four banks to be representative of the industry: Goldman Sachs, Morgan Stanley, J.P. Morgan, and Bank of America. We then analyze four metrics for each bank: revenue mix, value at risk, tangible common equity ratio, and debt to equity ratio. These provide methods for analyzing how banks have shifted their revenue centers to accommodate new regulations, as well as how these shifts have affected banks' risk levels and leverage. Our data show that all four banks that we observed shifted their revenue centers to flatter revenue areas, such as investment management, wealth management, and consumer banking operations. This was paired with fairly flat investment banking revenues across the board when controlling for overall market changes in the investment banking sector. Additionally, trading-focused banks significantly shifted their operations away from proprietary trading and higher risk activities. These changes resulted in lower value at risk measures for Goldman Sachs and Morgan Stanley with very minor increases for J.P. Morgan and Bank of America, although these two banks had low levels of absolute value at risk when compared to Goldman Sachs and Morgan Stanley. All banks' tangible common equity ratios increased and debt to equity ratios decreased, indicating a safer investment for shareholders and lower leverage. We conclude by offering a forecast of our expectations for the future, particularly in light of a Trump presidency. We expect less regulation going forward and the potential reversal of the Volcker Rule. We believe that these changes would result in more revenue coming from trading and riskier strategies, increasing value at risk, decreasing tangible common equity ratios, and increasing debt to equity ratios. While we do expect less regulation and higher risk, we do not expect these banks to reach pre-crisis levels due to the significant amount of regulations that would be particularly difficult for the Trump administration to reverse.

The basis of this project was to analyze the potential cost savings derived from the implementation of an ultrasonic flaw detector for gas pipes in factories. The group began by researching the market of the Industrial Internet of Things. IIoT is a very attractive market for investment, as connected technologies are become both more advanced and more affordable. Factory automation also saves costs of human capital, maintenance, and bad product cost as well as safety. After doing this preliminary research, the group continued by identifying potential solutions to current shortcomings of the manufacturing status quo. After narrowing down the options, the ultrasonic flaw detector appeared to have the highest potential for success in Company X's factories. The group began doing research on what physical components would go into this solution. They found pricing for all of the various parts of such a device as well as estimated labor, maintenance, and implementation costs. After estimating these costs, the team began the construction of a detailed financial model to generate the hypothetical net present value of such a tool. After presenting two times to a panel of Company X employees, the group decided to focus only on cost savings for Company X, and not the potential revenues of selling the whole solution. They ran a sensitivity analysis on all of the factors that contributed to the NPV of the project, and discovered that the estimated percentage of scrapped product resulting from gas leaks and the percentage of gas lost to leaks contributed the most to the NPV.

Semiconductor Manufacturer (Semi) wants to improve the valuation of the extended warranties they purchase for their metrology tools and determine whether or not extended warranties are worth the financial investment. Historically, suppliers have commonly overvalued warranties. For example, there is a 50%-60% profit margin on warranties in the consumer electronics industry. The costs incurred from purchasing extended warranties contribute to millions of dollars each year in tool ownership for Semi. By creating an extended warranty valuation model, our goal is to reduce the total cost of metrology tool ownership. A different perspective on the valuation of extended warranties will lead to an increased bottom line for Semi. Our valuation model will assist in determining warranty purchase pricing and appropriate service levels of maintenance personnel associated with the extended warranties. The model's objective is to compare the statistical expected total cost of buying tool parts on an "as needed" basis with the quoted price of an extended warranty. It will assess the strict financial value of either buying or not buying the extended warranty. Using actual tool part consumption data, the model can quickly evaluate the value of a supplier's warranty offer. In addition, the results from the model can be used as a negotiation tool with the suppliers. However the model will have its limitations. For example, the model will not be able to evaluate whether a metrology supplier relies on extended warranty revenues to fund research and development or whether a supplier has the financial health to remain in business with the loss of extended warranty related revenues. A shift in extended warranty purchasing by Semi could have a profound impact on the number of competitive suppliers in the future, and Semi's managers should take this into account when altering their extended warranty purchasing strategy. Our model can be utilized for three different functions: negotiating with suppliers, simplifying the decision to buy or not buy an extended warranty and influencing managers' purchasing strategies. Changing the service level costs of labor can impact Semi's decision to buy or not the extended warranty due to its effect on the probability of the warranty being a good or bad deal. In addition, the model output can significantly influence a manager's purchasing strategy within the organization by breaking down the cost savings associated with the metrology tools' part failures. In order to improve the accuracy and effectiveness of the financial model, we recommend that Semi collect and assemble the model input data in a different manner. Although it is possible Semi does collect more detailed data, the input data we received needed to be more comprehensive; it should include a list of tool parts with their respective failure dates, along with which supplier is responsible for which tool. Furthermore, Semi should develop a supplier scorecard to account for financial health, which can be factored into the model. This will result in a more precise evaluation on whether or not an extended warranty is worth the financial investment.

The object of the present study is to examine methods in which the company can optimize their costs on third-party suppliers whom oversee other third-party trade labor. The third parties in scope of this study are suspected to overstaff their workforce, thus overcharging the company. We will introduce a complex spreadsheet model that will propose a proper project staffing level based on key qualitative variables and statistics. Using the model outputs, the Thesis team proposes a headcount solution for the company and problem areas to focus on, going forward. All sources of information come from company proprietary and confidential documents.

This thesis discusses methodology used to assess the financial health of Company X's suppliers. Each suppliers' industry characteristics and key risk exposures are identified using the Porter's Five Forces. Along with qualitative analysis, financial data is analyzed with the Altman Z-Scores, forecasted financial statements, and comparative ratio analysis. The focus is narrowed down throughout the process to enable further investigation on Supplier E and the semiconductor-memory industry.The procedure and results of the analysis lead to the final recommendation to Company X on how it should assess the financial health of suppliers in the semiconductor-memory industry, and possibly other industries, using our methodology.

Over the course of six months, we have worked in partnership with Arizona State University and a leading producer of semiconductor chips in the United States market (referred to as the "Company"), lending our skills in finance, statistics, model building, and external insight. We attempt to design models that help predict how much time it takes to implement a cost-saving project. These projects had previously been considered only on the merit of cost savings, but with an added dimension of time, we hope to forecast time according to a number of variables. With such a forecast, we can then apply it to an expense project prioritization model which relates time and cost savings together, compares many different projects simultaneously, and returns a series of present value calculations over different ranges of time. The goal is twofold: assist with an accurate prediction of a project's time to implementation, and provide a basis to compare different projects based on their present values, ultimately helping to reduce the Company's manufacturing costs and improve gross margins. We believe this approach, and the research found toward this goal, is most valuable for the Company. Two coaches from the Company have provided assistance and clarified our questions when necessary throughout our research. In this paper, we begin by defining the problem, setting an objective, and establishing a checklist to monitor our progress. Next, our attention shifts to the data: making observations, trimming the dataset, framing and scoping the variables to be used for the analysis portion of the paper. Before creating a hypothesis, we perform a preliminary statistical analysis of certain individual variables to enrich our variable selection process. After the hypothesis, we run multiple linear regressions with project duration as the dependent variable. After regression analysis and a test for robustness, we shift our focus to an intuitive model based on rules of thumb. We relate these models to an expense project prioritization tool developed using Microsoft Excel software. Our deliverables to the Company come in the form of (1) a rules of thumb intuitive model and (2) an expense project prioritization tool.