Matching Items (28)
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- All Subjects: Data Analytics
- Creators: Department of Information Systems
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
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.
ContributorsAl-Assi, Hashim (Co-author) / Chiang, Robert (Co-author) / Liu, Andrew (Co-author) / Ludwick, David (Co-author) / Simonson, Mark (Thesis director) / Hertzel, Michael (Committee member) / Barrett, The Honors College (Contributor) / Department of Information Systems (Contributor) / Department of Finance (Contributor) / Department of Economics (Contributor) / Department of Supply Chain Management (Contributor) / School of Accountancy (Contributor) / School of Mathematical and Statistical Sciences (Contributor) / Mechanical and Aerospace Engineering Program (Contributor) / WPC Graduate Programs (Contributor)
Created2015-05
Description
Coherent vortices are ubiquitous structures in natural flows that affect mixing and transport of substances and momentum/energy. Being able to detect these coherent structures is important for pollutant mitigation, ecological conservation and many other aspects. In recent years, mathematical criteria and algorithms have been developed to extract these coherent structures in turbulent flows. In this study, we will apply these tools to extract important coherent structures and analyze their statistical properties as well as their implications on kinematics and dynamics of the flow. Such information will aide representation of small-scale nonlinear processes that large-scale models of natural processes may not be able to resolve.
ContributorsCass, Brentlee Jerry (Author) / Tang, Wenbo (Thesis director) / Kostelich, Eric (Committee member) / Department of Information Systems (Contributor) / School of Mathematical and Statistical Sciences (Contributor) / Barrett, The Honors College (Contributor)
Created2018-05
Description
Exchange traded funds (ETFs) in many ways are similar to more traditional closed-end mutual funds, although thee differ in a crucial way. ETFs rely on a creation and redemption feature to achieve their functionality and this mechanism is designed to minimize the deviations that occur between the ETF’s listed price and the net asset value of the ETF’s underlying assets. However while this does cause ETF deviations to be generally lower than their mutual fund counterparts, as our paper explores this process does not eliminate these deviations completely. This article builds off an earlier paper by Engle and Sarkar (2006) that investigates these properties of premiums (discounts) of ETFs from their fair market value. And looks to see if these premia have changed in the last 10 years. Our paper then diverges from the original and takes a deeper look into the standard deviations of these premia specifically.
Our findings show that over 70% of an ETFs standard deviation of premia can be explained through a linear combination consisting of two variables: a categorical (Domestic[US], Developed, Emerging) and a discrete variable (time-difference from US). This paper also finds that more traditional metrics such as market cap, ETF price volatility, and even 3rd party market indicators such as the economic freedom index and investment freedom index are insignificant predictors of an ETFs standard deviation of premia when combined with the categorical variable. These findings differ somewhat from existing literature which indicate that these factors should have a significant impact on the predictive ability of an ETFs standard deviation of premia.
Our findings show that over 70% of an ETFs standard deviation of premia can be explained through a linear combination consisting of two variables: a categorical (Domestic[US], Developed, Emerging) and a discrete variable (time-difference from US). This paper also finds that more traditional metrics such as market cap, ETF price volatility, and even 3rd party market indicators such as the economic freedom index and investment freedom index are insignificant predictors of an ETFs standard deviation of premia when combined with the categorical variable. These findings differ somewhat from existing literature which indicate that these factors should have a significant impact on the predictive ability of an ETFs standard deviation of premia.
ContributorsZhang, Jingbo (Co-author, Co-author) / Henning, Thomas (Co-author) / Simonson, Mark (Thesis director) / Licon, L. Wendell (Committee member) / Department of Finance (Contributor) / Department of Information Systems (Contributor) / School of Mathematical and Statistical Sciences (Contributor) / Barrett, The Honors College (Contributor)
Created2019-05
Description
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.
ContributorsLoo, Andrew (Co-author) / Brennan, Michael (Co-author) / Sheiner, Alexander (Co-author) / Hertzel, Michael (Thesis director) / Simonson, Mark (Committee member) / Barrett, The Honors College (Contributor) / Department of Information Systems (Contributor) / Department of Finance (Contributor) / Department of Supply Chain Management (Contributor) / WPC Graduate Programs (Contributor) / School of Accountancy (Contributor)
Created2014-05
Description
The main goal of this study was to understand the awareness of small business owners regarding occupational fraud, meaning fraud committed from within an organization. A survey/questionnaire was used to gather insight into the knowledge and perceptions of small business owners, while also obtaining information about the history of fraud and the internal controls within their business. Twenty-four owners of businesses with less than 100 employees participated in the study. The results suggest that small business owners overestimate their knowledge regarding internal controls and occupational fraud, while also underestimating the risk of fraud within their own business. In fact, 92% of participants were not at all familiar with the popular Internal Control \u2014 Integrated Framework published by the Committee of Sponsoring Organizations of the Treadway Commission. The results also show that small business owners tend to overestimate the protection provided by their currently implemented controls in regard to their risk of fraud. Overall, through continued knowledge of internal controls and occupational fraud, business owners can better protect their businesses from the risk of occupational fraud by increasing their awareness of fraud.
ContributorsDennis, Lauren Nicole (Author) / Orpurt, Steven (Thesis director) / Munshi, Perseus (Committee member) / Barrett, The Honors College (Contributor) / Department of Information Systems (Contributor) / School of Accountancy (Contributor)
Created2014-05
Description
Predictive analytics have been used in a wide variety of settings, including healthcare,
sports, banking, and other disciplines. We use predictive analytics and modeling to
determine the impact of certain factors that increase the probability of a successful
fourth down conversion in the Power 5 conferences. The logistic regression models
predict the likelihood of going for fourth down with a 64% or more probability based on
2015-17 data obtained from ESPN’s college football API. Offense type though important
but non-measurable was incorporated as a random effect. We found that distance to go,
play type, field position, and week of the season were key leading covariates in
predictability. On average, our model performed as much as 14% better than coaches
in 2018.
sports, banking, and other disciplines. We use predictive analytics and modeling to
determine the impact of certain factors that increase the probability of a successful
fourth down conversion in the Power 5 conferences. The logistic regression models
predict the likelihood of going for fourth down with a 64% or more probability based on
2015-17 data obtained from ESPN’s college football API. Offense type though important
but non-measurable was incorporated as a random effect. We found that distance to go,
play type, field position, and week of the season were key leading covariates in
predictability. On average, our model performed as much as 14% better than coaches
in 2018.
ContributorsBlinkoff, Joshua Ian (Co-author) / Voeller, Michael (Co-author) / Wilson, Jeffrey (Thesis director) / Graham, Scottie (Committee member) / Dean, W.P. Carey School of Business (Contributor) / Department of Information Systems (Contributor) / Department of Management and Entrepreneurship (Contributor) / Barrett, The Honors College (Contributor)
Created2019-05
Description
Predictive analytics have been used in a wide variety of settings, including healthcare, sports, banking, and other disciplines. We use predictive analytics and modeling to determine the impact of certain factors that increase the probability of a successful fourth down conversion in the Power 5 conferences. The logistic regression models predict the likelihood of going for fourth down with a 64% or more probability based on 2015-17 data obtained from ESPN’s college football API. Offense type though important but non-measurable was incorporated as a random effect. We found that distance to go, play type, field position, and week of the season were key leading covariates in predictability. On average, our model performed as much as 14% better than coaches in 2018.
ContributorsVoeller, Michael Jeffrey (Co-author) / Blinkoff, Josh (Co-author) / Wilson, Jeffrey (Thesis director) / Graham, Scottie (Committee member) / Department of Information Systems (Contributor) / Department of Finance (Contributor) / Barrett, The Honors College (Contributor)
Created2019-05
Description
Within this paper I summarize the key features, and results, of research conducted to support the development, design, and implementation of an internal control system at a startup small business. These efforts were conducted for an Honors Thesis/Creative Project for Barrett, the Honors College at Arizona State University. The research revolved around deciding which financial policies, procedures, and safeguards could be useful in creating an internal control system for small businesses. In addition to academic research, I developed an “Internal Control Questionnaire” for use as a ‘jumping off point’ in conversations about a business’ existing accounting system. This questionnaire is applicable across many industries, covering the major topics which every small business/startup should consider.
The questionnaire was then used in conjunction with two interviews of small business owners. The interviews covered both the overall financial status of their business and their business’ pre-existing accounting system. The feedback received during these interviews was subsequently used to provide the business owners with eleven recommendations ranging from the implementation of new policies to verification of existing internal controls.
Finally, I summarize my findings, both academic and real-world, conveying that many small business owners do not implement formal internal control systems. I also discuss why the business owners, in this specific circumstance, did not yet implement the aforementioned eleven suggestions.
The questionnaire was then used in conjunction with two interviews of small business owners. The interviews covered both the overall financial status of their business and their business’ pre-existing accounting system. The feedback received during these interviews was subsequently used to provide the business owners with eleven recommendations ranging from the implementation of new policies to verification of existing internal controls.
Finally, I summarize my findings, both academic and real-world, conveying that many small business owners do not implement formal internal control systems. I also discuss why the business owners, in this specific circumstance, did not yet implement the aforementioned eleven suggestions.
ContributorsDuncan, Spencer James (Author) / Garverick, Michael (Thesis director) / Casas Arce, Pablo (Committee member) / School of Accountancy (Contributor) / School of International Letters and Cultures (Contributor) / Barrett, The Honors College (Contributor)
Created2019-05
Description
The objective of this project was the creation of a web app for undergraduate CIS/BDA students which allows them to search for jobs based on criteria that are not always directly available with the average job search engine. This includes technical skills, soft skills, location and industry. This creates a more focused way for these students to search for jobs using an application that also attempts to exclude positions that are looking for very experienced employees. The activities used for this project were chosen in attempt to make as many of the processes as automatable as possible.
This was achieved by first using offline explorer, an application that can download websites, to gather job postings from Dice.com that were searched by a pre-defined list of technical skills. Next came the parsing of the downloaded postings to extract and clean the data that was required and filling a database with that cleaned data. Then the companies were matched up with their corresponding industries. This was done using their NAICS (North American Industry Classification System) codes. The descriptions were then analyzed, and a group of soft skills was chosen based on the results of Word2Vec (a group of models that assists in creating word embeddings). A master table was then created by combining all of the tables in the database. The master table was then filtered down to exclude posts that required too much experience. Lastly, the web app was created using node.js as the back-end. This web app allows the user to choose their desired criteria and navigate through the postings that meet their criteria.
This was achieved by first using offline explorer, an application that can download websites, to gather job postings from Dice.com that were searched by a pre-defined list of technical skills. Next came the parsing of the downloaded postings to extract and clean the data that was required and filling a database with that cleaned data. Then the companies were matched up with their corresponding industries. This was done using their NAICS (North American Industry Classification System) codes. The descriptions were then analyzed, and a group of soft skills was chosen based on the results of Word2Vec (a group of models that assists in creating word embeddings). A master table was then created by combining all of the tables in the database. The master table was then filtered down to exclude posts that required too much experience. Lastly, the web app was created using node.js as the back-end. This web app allows the user to choose their desired criteria and navigate through the postings that meet their criteria.
ContributorsHenry, Alfred (Author) / Darcy, David (Thesis director) / Moser, Kathleen (Committee member) / Department of Information Systems (Contributor, Contributor) / Barrett, The Honors College (Contributor)
Created2019-05