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Description
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.
ContributorsDougherty, Mitch (Co-author) / Marshall, Jeffrey (Co-author) / Zieler, Jason (Co-author) / Gilmore, Eric (Co-author) / Hertzel, Michael (Thesis director) / Simonson, Mark (Committee member) / Yarn, James (Committee member) / Walter Cronkite School of Journalism and Mass Communication (Contributor) / Department of Finance (Contributor) / Barrett, The Honors College (Contributor)
Created2014-05
Description
Company X is one of the world's largest manufacturer of semiconductors. The company relies on various suppliers in the U.S. and around the globe for its manufacturing process. The financial health of these suppliers is vital to the continuation of Company X's business without any material interruption. Therefore, it is in Company X's interest to monitor its supplier's financial performance. Company X has a supplier financial health model currently in use. Having been developed prior to watershed events like the Great Recession, the current model may not reflect the significant changes in the economic environment due to these events. Company X wants to know if there is a more accurate model for evaluating supplier health that better indicates business risk. The scope of this project will be limited to a sample of 24 suppliers representative of Company X's supplier base that are public companies. While Company X's suppliers consist of both private and public companies, the used of exclusively public companies ensures that we will have sufficient and appropriate data for the necessary analysis. The goal of this project is to discover if there is a more accurate model for evaluating the financial health of publicly traded suppliers that better indicates business risk. Analyzing this problem will require a comprehensive understanding of various financial health models available and their components. The team will study best practice and academia. This comprehension will allow us to customize a model by incorporating metrics that allows greater accuracy in evaluating supplier financial health in accordance with Company X's values.
ContributorsLi, Tong (Co-author) / Gonzalez, Alexandra (Co-author) / Park, Zoon Beom (Co-author) / Vogelsang, Meridith (Co-author) / Simonson, Mark (Thesis director) / Hertzel, Mike (Committee member) / Department of Finance (Contributor) / Department of Information Systems (Contributor) / School of Accountancy (Contributor) / WPC Graduate Programs (Contributor) / Barrett, The Honors College (Contributor)
Created2016-05
Description
In the aftermath of the 2008 financial crisis, banking regulators have been taking a more active role in pursing greater financial stability. One area of focus has been on Wall Street banks' leverage lending practices which include leveraged lending activities to fund leveraged buyouts. In March 2013, the Federal Reserve and the Office of the Comptroller of the Currency issued guidance urging banks to avoid financing leveraged buyouts in most industries that would put total debt on a company of more than six times its earnings before interest, taxes, depreciation and amortization, or Ebitda. Our research, using data on all leveraged buyouts (with EBITDA >$20 million) issued after the guidance, sets out to explain the elements banks consider when exceeding leverage limitations. Initially, we hypothesized that since deals over 6x leverage had higher amounts of debt, they were riskier deals, which would carry over to other risk measures such as yield to maturity on debt and company credit ratings. To analyze this, we obtained a large data set with all LBO deals in the past three years and ran difference-in-means tests on a number of variables such as deal size, credit rating and yield to maturity to determine if deals over 6x leverage had significantly different risk characteristics than deals under 6x leverage. Contrary to our hypothesis, we found that deals over 6x leverage had significantly less risk, mainly demonstrated by lower average YTMs, than deals under 6x. One possible explanation of this might be that banks, wanting to ensure they are not fined, will only go through with a deal over 6x leverage if other risk metrics such as yield to maturity are well below average.
ContributorsKing, Adam (Co-author) / Lukemire, Sean (Co-author) / McAleer, Stephen (Co-author) / Simonson, Mark (Thesis director) / Bonadurer, Werner (Committee member) / Department of Finance (Contributor) / Department of Economics (Contributor) / Barrett, The Honors College (Contributor)
Created2016-05
Description
Abstract
The aim of the research performed was to increase research potential in the field of cell stimulation by developing a method to adhere human neural progenitor cells (hNPC’s) to a sterilized stretchable microelectrode array (SMEA). The two primary objectives of our research were to develop methods of sterilizing the polydimethylsiloxane (PDMS) substrate being used for the SMEA, and to derive a functional procedure for adhering hNPC’s to the PDMS. The proven method of sterilization was to plasma treat the sample and then soak it in 70% ethanol for one hour. The most successful method for cell adhesion was plasma treating the PDMS, followed by treating the surface of the PDMS with 0.01 mg/mL poly-l-lysine (PLL) and 3 µg/cm2 laminin. The development of these methods was an iterative process; as the methods were tested, any problems found with the method were corrected for the next round of testing until a final method was confirmed. Moving forward, the findings will allow for cell behavior to be researched in a unique fashion to better understand the response of adherent cells to physical stimulation by measuring changes in their electrical activity.
The aim of the research performed was to increase research potential in the field of cell stimulation by developing a method to adhere human neural progenitor cells (hNPC’s) to a sterilized stretchable microelectrode array (SMEA). The two primary objectives of our research were to develop methods of sterilizing the polydimethylsiloxane (PDMS) substrate being used for the SMEA, and to derive a functional procedure for adhering hNPC’s to the PDMS. The proven method of sterilization was to plasma treat the sample and then soak it in 70% ethanol for one hour. The most successful method for cell adhesion was plasma treating the PDMS, followed by treating the surface of the PDMS with 0.01 mg/mL poly-l-lysine (PLL) and 3 µg/cm2 laminin. The development of these methods was an iterative process; as the methods were tested, any problems found with the method were corrected for the next round of testing until a final method was confirmed. Moving forward, the findings will allow for cell behavior to be researched in a unique fashion to better understand the response of adherent cells to physical stimulation by measuring changes in their electrical activity.
ContributorsBridgers, Carson (Co-author) / Peterson, Mara (Co-author) / Stabenfeldt, Sarah (Thesis director) / Graudejus, Oliver (Committee member) / Harrington Bioengineering Program (Contributor) / School of Human Evolution and Social Change (Contributor) / Barrett, The Honors College (Contributor)
Created2016-05
Description
For our collaborative thesis we explored the US electric utility market and how the Internet of Things technology movement could capture a possible advancement of the current existing grid. Our objective of this project was to successfully understand the market trends in the utility space and identify where a semiconductor manufacturing company, with a focus on IoT technology, could penetrate the market using their products. The methodology used for our research was to conduct industry interviews to formulate common trends in the utility and industrial hardware manufacturer industries. From there, we composed various strategies that The Company should explore. These strategies were backed up using qualitative reasoning and forecasted discounted cash flow and net present value analysis. We confirmed that The Company should use specific silicon microprocessors and microcontrollers that pertained to each of the four devices analytics demand. Along with a silicon strategy, our group believes that there is a strong argument for a data analytics software package by forming strategic partnerships in this space.
ContributorsLlazani, Loris (Co-author) / Ruland, Matthew (Co-author) / Medl, Jordan (Co-author) / Crowe, David (Co-author) / Simonson, Mark (Thesis director) / Hertzel, Mike (Committee member) / Department of Economics (Contributor) / Department of Finance (Contributor) / Department of Supply Chain Management (Contributor) / Department of Information Systems (Contributor) / Hugh Downs School of Human Communication (Contributor) / Barrett, The Honors College (Contributor)
Created2016-05
Description
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.
ContributorsKadi, Bengimen (Co-author) / Peterson, Tyler (Co-author) / Langmack, Haley (Co-author) / Quintana, Vince (Co-author) / Simonson, Mark (Thesis director) / Hertzel, Michael (Committee member) / Department of Supply Chain Management (Contributor) / Department of Finance (Contributor) / Department of Information Systems (Contributor) / Department of Marketing (Contributor) / School of Accountancy (Contributor) / W. P. Carey School of Business (Contributor) / Barrett, The Honors College (Contributor)
Created2016-05
Description
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.
ContributorsDunn, Nicole (Co-author) / Boudreau, Thomas (Co-author) / Kinzy, Chris (Co-author) / Radigan, Thomas (Co-author) / Simonson, Mark (Thesis director) / Hertzel, Michael (Committee member) / WPC Graduate Programs (Contributor) / Department of Psychology (Contributor) / Department of Finance (Contributor) / School of Accountancy (Contributor) / Department of Economics (Contributor) / School of Mathematical and Statistical Science (Contributor) / W. P. Carey School of Business (Contributor) / Computer Science and Engineering Program (Contributor) / Barrett, The Honors College (Contributor)
Created2016-05
Description
In the medical industry, there have been promising advances in the increase of new types of healthcare to the public. As of 2015, there was a 98% Premarket Approval rate, a 38% increase since 2010. In addition, there were 41 new novel drugs approved for clinical usage in 2014 where the average in the previous years from 2005-2013 was 25. However, the research process towards creating and delivering new healthcare to the public remains remarkably inefficient. It takes on average 15 years, over $900 million by one estimate, for a less than 12% success rate of discovering a novel drug for clinical usage. Medical devices do not fare much better. Between 2005-2009, there were over 700 recalls per year. In addition, it takes at minimum 3.25 years for a 510(k) exempt premarket approval. Plus, a time lag exists where it takes 17 years for only 14% of medical discoveries to be implemented clinically. Coupled with these inefficiencies, government funding for medical research has been decreasing since 2002 (2.5% of Gross Domestic Product) and is predicted to be 1.5% of Gross Domestic Product by 2019. Translational research, the conversion of bench-side discoveries to clinical usage for a simplistic definition, has been on the rise since the 1990s. This may be driving the increased premarket approvals and new novel drug approvals. At the very least, it is worth considering as translational research is directly related towards healthcare practices. In this paper, I propose to improve the outcomes of translational research in order to better deliver advancing healthcare to the public. I suggest Best Value Performance Information Procurement System (BV PIPS) should be adapted in the selection process of translational research projects to fund. BV PIPS has been shown to increase the efficiency and success rate of delivering projects and services. There has been over 17 years of research with $6.3 billion of projects and services delivered showing that BV PIPS has a 98% customer satisfaction, 90% minimized management effort, and utilizes 50% less manpower and effort. Using University of Michigan \u2014 Coulter Foundation Program's funding process as a baseline and standard in the current selection of translational research projects to fund, I offer changes to this process based on BV PIPS that may ameliorate it. As concepts implemented in this process are congruent with literature on successful translational research, it may suggest that this new model for selecting translational research projects to fund will reduce costs, increase efficiency, and increase success. This may then lead to more Premarket Approvals, more new novel drug approvals, quicker delivery time to the market, and lower recalls.
ContributorsDel Rosario, Joseph Paul (Author) / Kashiwagi, Dean (Thesis director) / Kashiwagi, Jacob (Committee member) / Harrington Bioengineering Program (Contributor) / Barrett, The Honors College (Contributor)
Created2016-05
Description
Business students are trained to be professional problem solver. In order to improve students' ability to solve real-life problem, more and more business schools are encouraging students to attend case competitions and do internships before graduation. In curriculum, students are required to work on business cases and projects in team. However, due to the limited exposure to real-life business scenarios, most undergraduate students feel unprepared when faced with business problems in course projects, case competitions, and internships. Therefore, the goal of this Honors Creative Project is to provide students with an interactive resource to succeed in course projects, case competitions, and even internship projects. By introducing resources that focused on analysis approach and project management, students can learn from some successful experience and become more competitive in job market. After competing at four case competitions with talents all over the nation, we accumulated precious experience in case analysis and teamwork development within a high-pressure environment. In addition, the experiences with internships, consulting and course projects have also aided the participants' development in professionalism and quantitative analytics. Reflecting on what we have learned from our experiences, we strongly believe that the insights gained from the past are not only a treasure for us individually, but also a great resource for our colleagues. We hope to transfer our knowledge to others for their own success where "best practices" can be learned.
ContributorsXiahou, Xiaonan (Co-author) / Thoi, Kenson (Co-author) / Printezis, Antonios (Thesis director) / Arrfelt, Mathias (Committee member) / Department of Supply Chain Management (Contributor) / Department of Economics (Contributor) / Department of Finance (Contributor) / Department of Information Systems (Contributor) / Barrett, The Honors College (Contributor)
Created2016-05
Description
Breast and other solid tumors exhibit high and varying degrees of intra-tumor heterogeneity resulting in targeted therapy resistance and other challenges that make the management and treatment of these diseases rather difficult. Due to the presence of admixtures of non-neoplastic cells with polyclonal cell populations, it is difficult to define cancer genomes in patient samples. By isolating tumor cells from normal cells, and enriching distinct clonal populations, clinically relevant genomic aberrations that drive disease can be identified in patients in vivo. An in-depth analysis of clonal architecture and tumor heterogeneity was performed in a stage II chemoradiation-naïve breast cancer from a sixty-five year old patient. DAPI-based DNA content measurements and DNA content-based flow sorting was used to to isolate nuclei from distinct clonal populations of diploid and aneuploid tumor cells in surgical tumor samples. We combined DNA content-based flow cytometry and ploidy analysis with high-definition array comparative genomic hybridization (aCGH) and next-generation sequencing technologies to interrogate the genomes of multiple biopsies from the breast cancer. The detailed profiles of ploidy, copy number aberrations and mutations were used to recreate and map the lineages present within the tumor. The clonal analysis revealed driver events for tumor progression (a heterozygous germline BRCA2 mutation converted to homozygosity within the tumor by a copy number event and the constitutive activation of Notch and Akt signaling pathways. The highlighted approach has broad implications in the study of tumor heterogeneity by providing a unique ultra-high resolution of polyclonal tumors that can advance effective therapies and clinical management of patients with this disease.
ContributorsLaughlin, Brady Scott (Author) / Ankeny, Casey (Thesis director) / Barrett, Michael (Committee member) / Barrett, The Honors College (Contributor) / Harrington Bioengineering Program (Contributor) / School for the Science of Health Care Delivery (Contributor)
Created2015-05