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2018 Conference Proceeding

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2018 Conference Proceeding CONFERENCE PROCEEDINGS BY TRACK 2018 MIDWEST DSI ANNUAL CONFERENCE INDIANAPOLIS, INDIANA CONFERENCE CHAIR: PEGGY LEE DANIELS, IUPUI PROCEEDINGS COORDINATORS: HAOJIE CHEN, VALPARAISO UNIVERSITY ABBY BRIDWELL, VALPARAISO UNIVERSITY A1 TABLE OF CONTENTS PREPARED BY ABBY BRIDWELL AND HAOJIE CHEN Tracks Included: Business Analytics Finance and Accounting Innovative Education Marketing Operations and Supply Chain Management BUSINESS ANALYTICS Comparison and contrast of Statistics Software Packages including R and Python for teaching purposes Ceyhun.Ozgur, Valparaiso University Sanjeev Jha , Valparaiso University Yiming Shen, Valparaiso University ……………………………………………………Page 1-23 Forecasting Intermittent Demand Patterns with Time Series and Machine Learning Methodologies Yuwen Hong, Purdue University Jingda Zhou, Purdue University Matthew A. Lanham ,Purdue University ………………………………………………Page 24-48 A Proposed Data Analytics Workflow and Example Using the R Caret Package Simon Jones, Purdue University Zhenghao Ye, Purdue University Zhuoheng Xie, Purdue University Chris Root, Purdue University Theerakorn Prasutchai, Purdue University Michael Roggenburg, Purdue University Matthew A. Lanham, Purdue University …………………….……………………...Page 49-71 Recruitment Analytics: An Investigation of Program Awareness & Matriculation Liye Sun, Purdue University Matthew A. Lanham, Purdue University ……………………………………………….Page 72 A2 FINANCE AND ACCOUNTING A Value at Risk Argument for Dollar Cost Averaging Laurence E. Blose, Grand Valley State University Eric Hoogstra, Grand Valley State University ……………………………………..…Page 73-89 INNOVATIVE EDUCATION Developing an Innovative Supply Chain Management Major and Minor Curriculum Sanjay Kumar, Valparaiso University Ceyhun Ozgur, Valparaiso University Coleen Wilder, Valparaiso University Sanjeev Jha, Valparaiso University ………………………………………………….Page 90-112 Disruptive Innovation & Sustainable Value: The Implications of Disruptive Innovation on the Outcome of RE Businesses in Developed Economies Page 113-134 Marketing: Carrier Choice Optimization with Tier Based Rebate for a National Retailer Surya Gundavarapu, Purdue University Matthew A. Lanham, Purdue University……………………………………………..Page 135-152 "I’ve Been Chain-ged" La Saundra Pendleton Janaina Siegler……………………………………………………………………………Page 153 Role of Political Identity in Friendship Networks Surya Gundavarapu, Purdue University Matthew A. Lanham, Purdue University……………………………………………..Page 154-169 XGBoost - A Competitive Approach for Online Price Prediction Joshua D. McKenney, Purdue University Yuqi Jiang, Purdue University Junyan Shao, Purdue University Matthew A. Lanham, Purdue University………………………………………….….Page 170-185 A3 OPERATIONS AND SUPPLY CHAIN MANAGEMENT A Comparative Study of Machine Learning Frameworks for Demand Forecasting Kalyan Mupparaju, P Anurag Soni, Purdue University Prasad Gujela, Purdue University Matthew A Lanham, Purdue University……………………………………………Page 186-206 Does Advance Warning Help Mitigate the Impact of Supply Chain Disruptions? Sourish Sarkar, Pennsylvania State University—Erie Sanjay Kumar, Valparaiso University……………………………………………….Page 207-208 Effect of Forecast Accuracy on Inventory Optimization Model Surya Gundavarapu, Purdue University Prasad Gujela, Purdue University Shan Lin, Purdue University Matthew A. Lanham, Purdue University……………………………………………..Page 209-230 The Evolution of the Open Source ERP iDempiere Community Network: A Preliminary Analysis Zhengzhong Shi, University of Massachusetts at Dartmouth Hua Sun, Shandong University………………………………………………………Page 231-241 Future LPG Shipments Forecasting Based on Empty LPG Vessels Data Jou-Tzu Kao, Purdue University Rong Liao, Purdue University Hongxia Shi, Purdue University Joseph Tsai, Purdue University Shenyang Yang, Purdue University Matthew A. Lanham, Purdue University……………………………………………..Page 242-256 Online Small-Group Learning Pedagogies for the 21st Century Classrooms Sema Kalaian ,Eastern Michigan University……………………………………………..Page 257 Opportunities for Enhancing Buyer-Supplier Relationship: Inspirations from the Natural World ……………………………………………………………………………………….Page 258-286 A4 Optimal Clustering of Products for Regression-Type and Classification-Type Predictive Modeling for Assortment Planning Raghav Tamhankar, Purdue University Sanchit Khattar, Purdue University Xiangyi Che, Purdue University Siyu Zhu, Purdue University Matthew A. Lanham, Purdue University……………………………………………Page 286a-303 Reducing the Cost of International Trade Through the Use of Foreign Trade Zones Gary Smith, Penn State Erie…………………………………………………………Page 304-321 Risky Business: Predicting Cancellations in Imbalanced Multi-Classification Settings Anand Deshmukh1, Purdue University Meena Kewlani, Purdue University Yash Ambegaokar, Purdue University Matthew A. Lanham, Purdue University……………………………………………..Page 322-352 Vehicle Routing, Scheduling and Decision Utility Environment Ceyhun Ozgur, Valparaiso University Claire Okkema, Valparaiso University Yiming Shen, Valparaiso University…………………………………………………Page 353-362 A5 Comparison and contrast of Statistics Software Packages including R and Python for teaching purposes Ceyhun.Ozgur, Sanjeev Jha Yiming Shen Abstract This paper shows the advantages and disadvantages of using Python and R in teaching various types of students based on the latest data. We also compare Python and R in solving business problems for actual companies. We give some examples of how to utilize both Python and R. For example, we provide examples of teaching correlation coefficient both with Python and R. We provide three teaching goals for Python and R: 1, helping students to find a job; 2, students receive higher salary; 3, low cost for learning packages both for Python and R. Keywords: Big data, Teaching R, Teaching Python, demonstrations, examples of teaching R and python, Python Python, easy to learn, is a general-purpose programming language. It has a mature and growing ecosystem of open-source tools for mathematics and data analysis for statistics and analytics, which is becoming the language of choice for scientists and researchers. Python code can be written like programing language. The main focus is to execute an entire series of instructions at once, it can also be executed line by line or block by block, making it unique and suitable for working with data interactively. Students learned a bit of programming and problem solving which were relegated to advanced courses. In addition to object-oriented programming as the dominant paradigm. It has led textbook authors to bring powerful, industrial-strength programming languages such as C++ and Java into the introductory software curriculum. As a result, instead of experiencing the rewards and excitement of solving problems with computers, beginning computer science students become overwhelmed by the combined tasks of mastering advanced concepts and the syntax of a programming language. This research will use the Python programming language as a way of making the course in mathematic more manageable for students and instructors alike for undergraduate education. • Python has simple, conventional syntax with its statements. The expressions are created with conventional notation found in algebra. Thus, one can spend less time dealing with the syntax of a programming language and more time to solve problems. 1 • It is easy for beginners to write simple programs in Python such as support for object- oriented software development. • Python is interactive. Users can enter expressions and statements to try out experimental code and receive immediate feedback. • Python is free and is in widespread use in the industry. Students can download Python to run on a variety of devices Based on more than 1000 developers, in figure 1 it can be see that 61% of the developers use Python as their primary programing language. Figure 1 shows the most popularly used software language platform by our respondents. As seen in the figure below Python and R are the most heavily used platform by our respondents. The usage of Python and R are only 17% apart from each other, and Python is the leader. Figure 1: Share of Python, R, Both, or Other platforms usage for Analytics, Data Science, Machine Learning, 90% 80% 76% 70% 60% 59% 54% 50% 40% 30% 20% 10% 18% 17% 9% 6% 5% 2% 0% 2 Table 1 Python 7955 6073 R 7955 4708 SQL 7955 4261 Java 7955 1453 Hadoop/Hive/Pig 7955 1378 SAS Base 7955 738 IBM SPSS 7955 472 Statistics Amazon 7955 425 Machine Learning Minitab 7955 150 Based on more than 1000 developers, in figure 1 it can be see that 61% of the developers use Python as their primary programing language. (Python Developers Survey 2016: Findings) Figure 2: Use of Python in the Software Development There are about 46% of developers use python programing language as their Data analysis tool instead of traditional programmers or Web developers. Use of Python in the Software Development Other 14% Web Development 22% Software Development 18% Data analysis 19% Scientific or Data Analysis 27% (Python Developers Survey 2016: Findings) 3 R R is an open source software which designed to run statistical analyses which makes the software highly appealing, as it is able to keep up with the demands of a growing number of varied business structures. One of the core benefits of teaching R to students is that it is a highly standardized programming language (Economist 662f, 2014). This means that students need not worry about highly variable language structures or deep understanding of the different languages with which one
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