Business Commons^™

Identifying At-Risk Clients For Xyz Packaging, Co., Eduardo Carlos Cantu Medellin, Mihir Parikh, Christopher Graves, Brendon Jones

SMU Data Science Review

We present a multi-algorithmic modeling approach for the identification of at-risk customers for XYZ Packaging Inc. We define at-risk customers as those having declining seasonally adjusted gross income forecasts which are a strong indicator of impending customer churn. Customer retention is an area of interest regardless of industry but is especially vital in commodity-based low margin industries. We employ traditional Autoregressive Integrated Moving Average (ARIMA) and Anomaly Detection algorithms for discriminating changes in customer revenue patterns. Ultimately, we identify a meaningful proportion of clients whose forward-looking quarterly demand can be predicted within an actionable degree of accuracy.

Generalized Relay Network Design And Collaborative Dispatching In Truckload Transportation, Amin Ziaeifar

Operations Research and Engineering Management Theses and Dissertations

The truckload industry faces a serious problem of high driver shortage and turnover rate which is typically around 100\%. Among the major causes of this problem are extended on-the-road times where drivers handle several truckload pickup and deliveries successively; non-regular schedules and get-home rates; and low utilization of drivers dedicated time. These are by-and-large consequences of the driver-to-load dispatching method, which is based on point-to-point dispatching or direct shipment from origin-to-destination, commonly employed in the industry. In this dissertation, we consider an alternative dispatching method that necessitates careful design of an underlying network. In this scheme, a truckload on its …

Big-Data Talent Analytics In The Public Sector: A Promotion And Firing Model Of Employees At Federal Agencies, Rabih Neouchi

Operations Research and Engineering Management Theses and Dissertations

Talent analytics is a relatively new area of focus to researchers working in analytics and data science. Talent Analytics has the potential to help companies make many informed critical decisions around talent acquisition, promotion and retention. This work investigates data science to predict “shiny star” employees in the U.S. public sector, defined as top-notch performers over the years of a given time span. Its scope falls within talent analytics, also called people analytics, a relatively new research area.

We clean a data set made available by the U.S. Office of Personnel Management (OPM) and present two models to predict the …

Optimize The Effectiveness Of Recruiting Campaigns, Ryan A. Talk, Lakshmi Bobbillapati, Marshall Coyle

SMU Data Science Review

Abstract. Recruiting marketing plays an important role in the talent acquisition strategy today. To find the best candidates, companies make substantial investments through numerous recruiting agencies, job boards, and internal systems such as Indeed, LinkedIn, Monster, Talent Communities. In this paper we obtained a company’s LinkedIn Job Posting data to try to predict the number of visits they will receive for each job posting based on the time of the year it is posted. We compare AR(1), AR(2), AR(52), MA(1), and ARMA(1, 1) time series methods to a baseline of a persistence model. We found that out of these 5 …

Wireless Channel Characterization Based On Crowdsourced Data And Geographical Features, Rita Enami

Electrical Engineering Theses and Dissertations

To design and plan wireless communication systems, an accurate propagation estimate is required of a deployment region. Propagation prediction models consist of two types of fading: large-scale and small-scale fading. With large-scale fading, the path loss information is crucial for cell planning, coverage estimation, and optimization. With small-scale fading, the statistical fluctuation on the local variations of the average signal level can have a dramatic effect on protocol decisions and resulting performance. To obtain accurate estimates of both types of fading, typically field measurements are needed that use drive testing, which is expensive in terms of time and cost. Recently, …

Dare To Venture: Data Science Perspective On Crowdfunding, Ruhaab Markas, Yisha Wang

SMU Data Science Review

Crowdfunding is an emerging segment of the financial sectors. Entrepreneurs are now able to seek funds from the online community through the use of online crowdfunding platforms. Entrepreneurs seek to understand attributes that play into a successful crowdfunding project (commonly known as campaign). In this paper we seek so understand the field of crowdfunding and various factors that contribute to the success of a campaign. We aim to use traditional modeling techniques to predict successful campaigns for Kickstarter. We find emerging field of crowdfunding has many nuances due to various funding methods of online platforms. The importance of having relevant …

Demand Forecasting: An Open-Source Approach, Murtada Shubbar, Jared Smith

SMU Data Science Review

In this paper, we compare demand forecasting methods used by the supply chain department at Bilports to open-source forecasting methods. The design and implementation of the open-source forecasting system also attempts to use several external datasets such as consumer sentiment, housing permit starts, and weather to improve prediction quality. Additionally, the performance of the forecast is evaluated by the reduction of shipment lead times from China, the company’s primary vendor. The objective of our paper is to improve Bilports’s forecasting capabilities. The primary motivation of this paper is to increase forecasting accuracy and identify the weaknesses of the methods used …

Use Advances In Data Science And Computing Power To Invest In Stock Market, Mustafa A. Sakarwala, Anthony Tanaydin

SMU Data Science Review

As part of its overseeing of capital markets, the Securities and Exchange Commission (SEC) requires firms with publicly traded shares to issue periodic reports to shareholders. These SEC filings are part of the SEC’s Electronic Data Gathering, Analysis, and Retrieval system (EDGAR), a large online database. Financial services and banking industry have armies of analysts that are dedicated to rushing over, analyzing, and attempting to quantify qualitative data from this SEC mandated reporting. We sought to prototype a predictive model to render consistent judgments on a company's prospects, based on the written textual sections of public earnings releases extracted from …

Leveraging Reviews To Improve User Experience, Anthony Schams, Iram Bakhtiar, Cristina Stanley

SMU Data Science Review

In this paper, we will explore and present a method of finding characteristics of a restaurant using its reviews through machine learning algorithms. We begin by building models to predict the ratings of individual reviews using text and categorical features. This is to examine the efficacy of the algorithms to the task. Both XGBoost and logistic regression will be examined. With these models, our goal is then to identify key phrases in reviews that are correlated with positive and negative experience. Our analysis makes use of review data publicly made available by Yelp. Key bigrams extracted were non-specific to the …

Dallas Refugee Engagement Project, Anna Landreneau, Kovan Barzani, Uroob Haris, Lawrence Jiang, Michael Park, Thomas Schmedding

SMU Journal of Undergraduate Research

The full capabilities of well-structured project management are rarely realized outside of the scope of the respective profession. The tools and skills in which project managers specialize are furthermore often considered in high-level business contexts, but are far less remembered as crucial components to many other endeavors. This project portfolio serves as an insight into the structure and process of managing a short-term social awareness project and an exploration and application of various project management tools. It also provides a review of the success of implementing sound project management toward humanitarian work on a community level. Public Equity, the team …

Improving Vix Futures Forecasts Using Machine Learning Methods, James Hosker, Slobodan Djurdjevic, Hieu Nguyen, Robert Slater

SMU Data Science Review

The problem of forecasting market volatility is a difficult task for most fund managers. Volatility forecasts are used for risk management, alpha (risk) trading, and the reduction of trading friction. Improving the forecasts of future market volatility assists fund managers in adding or reducing risk in their portfolios as well as in increasing hedges to protect their portfolios in anticipation of a market sell-off event. Our analysis compares three existing financial models that forecast future market volatility using the Chicago Board Options Exchange Volatility Index (VIX) to six machine/deep learning supervised regression methods. This analysis determines which models provide best …

Project Insight: A Granular Approach To Enterprise Cybersecurity, Sunna Quazi, Adam Baca, Sam Darsche

SMU Data Science Review

In this paper, we disambiguate risky activity corporate users are propagating with their software in real time by creating an enterprise security visualization solution for system administrators. The current problem in this domain is the lag in cyber intelligence that inhibits preventative security measure execution. This is partially due to the overemphasis of network activity, which is a nonfinite dataset and is difficult to comprehensively ingest with analytics. We address these concerns by elaborating on the beta of a software called "Insight" created by Felix Security. The overall solution leverages endpoint data along with preexisting whitelist/blacklist designations to unambiguously communicate …