Conference Session

Industrial Engineering Division Technical Session 1

Collection

2016 ASEE Annual Conference & Exposition

Authors

Lesley Strawderman, Mississippi State University; Shuchisnigdha Deb, Mississippi State University

Tagged Divisions

Industrial Engineering

graduation. 14.63% 48.78% 34.15% 2.44% 0.00%Notes: Bolded font indicates most common response; Q14, Q19, and Q24 are flipped scale questions. SA = StronglyAgree, A = Agree, N = Neutral, D = Disagree, SD = Strongly DisagreeReferences1. Corporation for National & Community Service (2013). College student volunteer rates. Retrieved from: http://www.volunteeringinamerica.gov/rankings/States/College-Student-Volunteer-Rates/20132. Jacoby, B. (2015). Service-learning essentials. San Francisco, CA: Jossey-Bass.3. Heffernan, K. (2001). Fundamentals of service-learning course construction. Providence, RI: Campus Compact.4. Kuh, G. (2008). High-impact educational

Conference Session

Industrial Engineering Division Technical Session 3

Collection

2016 ASEE Annual Conference & Exposition

Authors

David Elizandro, Tennessee Technological University

Tagged Divisions

Industrial Engineering

affect changesto Program Area Metrics. From Figures 4and 5, resource deficiencies are the basisfor project development.IV-b. Project(s) ManagementProject Management ensures projectactivities conform to contractspecifications and that data necessary toassess project effectiveness are available.Project specifications include: Figure 9: Program Area Root Cause Analysis Objectives: Qualitative reference to a Program Area. Metrics: Quantitative measures of Project Objectives. Methods: Project activities to achieve Project Objectives. Outcomes: Expected changes in Project Metrics.Objectives address baseline improvementsin Program Area Infrastructure or a JobsInitiative. To reflect the distinction, aProject is designated as a Build

Conference Session

Industrial Engineering Division Technical Session 1

Collection

2016 ASEE Annual Conference & Exposition

Authors

Joseph Wilck, United States Air Force Academy; Paul J. Kauffmann P.E., East Carolina University; Paul C. Lynch, Penn State University - Erie

Tagged Divisions

Industrial Engineering

toquality control from a prerequisite structure (i.e., a terminating course in a student’s curriculumplan that is routinely taken by sophomores, juniors, and seniors).Acknowledgement:The authors would like to acknowledge the helpful comments and suggestions of the reviewers.Disclaimer: The views expressed in this paper are those of the authors and do not necessarilyreflect the official policy or position of the U.S. Air Force, the U.S. Department of Defense, orthe U.S. Government.Bibliography 1. Klingbeil, N. W., Mercer, R. E., Rattan, K. S., Raymer, M. L., & Reynolds, D. B. (2006, April). The WSU model for engineering mathematics education: Student performance, perception and retention in year one. In Proceedings 2006 ASEE

Conference Session

Industrial Engineering Division Technical Session 3

Collection

2016 ASEE Annual Conference & Exposition

Authors

Angelica Burbano, Universidad Icesi

Tagged Divisions

Industrial Engineering

the Industrial Engineering (IE) program cover the knowledge, skills,and abilities required for Icesi’s students to achieve the program’s PEOs within a few years aftergraduation. These outcomes are based on ABET definitions for student outcomes. The studentoutcomes for the IE program are: a) an ability to apply knowledge of mathematics, science, and engineering b) an ability to design and conduct experiments, as well as to analyze and interpret data c) an ability to design a system, component, or processes to meet desired needs within realistic constraints such as economic, environmental, social, political, ethical, health and safety, manufacturability and sustainability d) an ability to function on multidisciplinary teams

Conference Session

Industrial Engineering Division Technical Session 1

Collection

2016 ASEE Annual Conference & Exposition

Authors

Philip Appiah-Kubi, University of Dayton

Tagged Divisions

Industrial Engineering

= 0.05 level, hence their inclusion inthe model.For the second exam, the coefficient of the independent variable and the constant are againstatistically significantly different from zero (p < 0.05) at α = 0.05 level, leading to the predictivemodel: 1 P(Y )  ( 2.485 2.485X ) (4) 1 eFrom exam 2, the probability that an effective cheat sheet aided in a student scoring an above-average score was approximately 92.31%. Furthermore, the Exp(B)s in table 1 and table 2indicate that

Conference Session

Industrial Engineering Division Technical Session 1

Collection

2016 ASEE Annual Conference & Exposition

Authors

Terri M. Lynch-Caris, Kettering University; Letitia M. Pohl, University of Arkansas

Tagged Topics

Diversity

Tagged Divisions

Industrial Engineering

8. Manufacturing, Production, and Service Systems: 8–12 questions 9. Facilities and Logistics: 8–12 questions 10. Human Factors, Ergonomics, and Safety: 8–12 questions A. Hazard identification and risk assessment B. Environmental stress assessment (e.g., noise, vibrations, heat) C. Industrial hygiene D. Design for usability (e.g., tasks, tools, displays, controls, user interfaces) E. Anthropometry F. Biomechanics G. Cumulative trauma disorders (e.g., low back injuries, carpal tunnel syndrome) H. Systems safety I. Cognitive engineering (e.g., information processing, situation awareness, human error, mental models) 11. Work

Conference Session

Industrial Engineering Division Technical Session 3

Collection

2016 ASEE Annual Conference & Exposition

Authors

David Elizandro, Tennessee Technological University; David H. Huddleston, Tennessee Technological University; Y. Jane Liu, Tennessee Technological University; Elizabeth L. Hutchins

Tagged Divisions

Industrial Engineering

% 33 40% 14 15 21 17 28 11 20% 44 35 41 39 20% 24 28 10 13 9 7 12 10 0% 0% 2009 2010 2011 2012 2013 2014 2009 2010 2011 2012 2013 2014 A B C

Conference Session

Industrial Engineering Division Technical Session 2

Collection

2016 ASEE Annual Conference & Exposition

Authors

Christina R. Scherrer, Kennesaw State University; Michael Maloni, Kennesaw State University; Elizabeth M. Boyd, Kennesaw State University; Stacy M. Campbell, Kennesaw State University

Tagged Topics

Diversity

Tagged Divisions

Industrial Engineering

2015).4. Ruamsook, K. and Craighead, C. (2014), "A supply chain talent perfect storm?", Supply Chain Management Review, Vol. 18 No. 1, pp. 12-17.5. Knemeyer, A. M. and Murphy, P. R. (2004), "Promoting the value of logistics to future business leaders: An exploratory study using a principles of marketing experience", International Journal of Physical Distribution & Logistics Management, Vol. 34 No. 10, pp. 775-792.6. Ozment, J. and Keller, S. B. (2011), "The future of logistics education", Transportation Journal, Vol. 50 No. 1, pp. 65-83.7. Arnseth, L. (2015), "The logistics workfroce talent crisis", Inside Supply Management, Vol. 28 No. 6, pp. 20-23.8. Knemeyer, A. M. and Murphy, P. R. (2004

Conference Session

Industrial Engineering Division Technical Session 1

Collection

2016 ASEE Annual Conference & Exposition

Authors

Terri M. Lynch-Caris, Kettering University; Karl D. Majeske, Oakland University

Tagged Divisions

Industrial Engineering

jobs become computer based, workers willspend greater amounts of time on a computer. It is important that the Industrial Engineeringcurriculum stays current on such demographic changes and update individual coursesaccordingly. This paper demonstrates how relatively simple and low cost studies can beintroduced into a traditional ergonomics class and benefit the students.References1. Bureau of Labor Statistics (2005). Computer and Internet use at work in 2003. Washington, DC: U.S. Department of Labor, Bureau of Labor Statistics.2. Reuters 2008 http://www.reuters.com/article/2008/06/23/us-computers-statistics-idUSL23245254200806233. Epstein, R., Colford, S., Epstein, E., Loye, B. Walsh, M. (2012). The effects of feedback on computer

Conference Session

Industrial Engineering Division Technical Session 2

Collection

2016 ASEE Annual Conference & Exposition

Authors

James C. Curry, Lamar University; Brian Craig P.E., Lamar University; Weihang Zhu, Lamar University

Tagged Divisions

Industrial Engineering

website recommends that students should complete at least sixmath-based lower division courses (such as Calculus I/II, Differential Equations, Linear Algebra,Physics I/II, Chemistry I/II, Statics, Dynamics, Engineering Economics, Circuits, or similarcourses) before joining the BSIE 2+2 online program. The website also recommends thatstudents should have reasonable good grades (mostly A and B) in these math-based lowerdivision courses. While LU has reasonable low transfer admission standards, the departmenttargets students who would have a reasonable high probability of completing a challengingdistance engineering education program. The degree plan in Table 1 is somewhat typical, butsome variation is common (e.g. POLS, HIST, etc. being taken in

Conference Session

Industrial Engineering Division Technical Session 2

Collection

2016 ASEE Annual Conference & Exposition

Authors

Paul C. Lynch, Penn State University - Erie; Cynthia Bober, Penn State University; Joseph Wilck, United States Air Force Academy

Tagged Topics

Diversity

Tagged Divisions

Industrial Engineering

“unsatisfying” class together. To alleviate this issue relating to beingbinary of “good” or “bad” courses, only one course was evaluated in each survey given tostudents. The course assigned to each student was randomly given for one of three courses thatthe students would have taken or have been currently enrolled in, named Class A, Class B, ClassC. Each survey type had approximately 35 students in the sample set. Therefore, the 107participating students were split into thirds to compare three courses. The questions and formatamong the class versions remained the same. Different courses within the curriculum werechosen to avoid a student ranking courses very high or very low in satisfaction, leading to a nullmodel that shows little significance. All

Conference Session

Industrial Engineering Division Technical Session 3

Collection

2016 ASEE Annual Conference & Exposition

Authors

Alexandra Chronopoulou, University of Illinois, Urbana-Champaign; Kelly J. Cross, University of Illinois, Urbana-Champaign; Douglas M. King, University of Illinois, Urbana-Champaign; Ehsan Salimi, University of Southern California

Tagged Topics

Diversity

Tagged Divisions

Industrial Engineering

National Assessment of Higher Order Thinking. Retrieved from http://www.criticalthinking.org/pages/a-model-for-the-national-assessment-of-higher-order- thinking/591 12. Peach, B. E., Mukherjee, A., & Hornyak, M. (2007). Assessing critical thinking: A college's journey and lessons learned. Journal of Education for Business, 82(6), 313-320. 13. Prince, M. J., & Felder, R. M. (2006). Inductive teaching and learning methods: Definitions, comparisons, and research bases. Journal of Engineering Education-Washington-, 95(2). 14. Ralston, P., & Bays, C. (2010). Refining a critical thinking rubric for engineering. 2010 Annual Conference & Exposition, Louisville, Kentucky. https

Conference Session

Industrial Engineering Division Technical Session 3

Collection

2016 ASEE Annual Conference & Exposition

Authors

Ebisa Wollega, Colorado State University - Pueblo; Vitor Ambrosio Winckler, Colorado State University - Pueblo

Tagged Divisions

Industrial Engineering

, 180.[10]. Van Meteren, R., & Van Someren, M. (2000, May). Using content-based filtering for recommendation. InProceedings of the Machine Learning in the New Information Age: MLnet/ECML2000 Workshop (pp. 47-56).[11]. Xiao, B., & Benbasat, I. (2007). E-commerce product recommendation agents: Use, characteristics, andimpact. Mis Quarterly, 31(1), 137-209.[12]. Zhao, Z. D., & Shang, M. S. (2010, January). User-based collaborative-filtering recommendation algorithmson hadoop. In Knowledge Discovery and Data Mining, 2010. WKDD'10. Third International Conference on (pp.478-481). IEEE.[13]. Zuva, T., Ojo, S. O., Ngwira, S., & Zuva, K. (2012). A survey of recommender systems techniques, challengesand evaluation metrics. International

Conference Session

Industrial Engineering Division Technical Session 3

Collection

2016 ASEE Annual Conference & Exposition

Authors

Aimee T. Ulstad, Ohio State University; Yeonsu Ryu

Tagged Divisions

Industrial Engineering

Paper ID #16109Using Mentors as Live Case Studies for Teaching Topics in Supply ChainManagementAimee T. Ulstad, Ohio State University Aimee Ulstad, P.E is an Associate Professor of Practice in the Integrated Systems Engineering Department at The Ohio State University. Prior to joining the faculty at Ohio State, Aimee was an industry professional in various field in engineering for over 30 years. Aimee received her degrees in Mechanical Engineering and Masters in Business Administration from Ohio State. She began her career as a packaging equipment engineer at Procter and Gamble, then moved to Anheuser-Busch where she

Conference Session

Industrial Engineering Division Technical Session 2

Collection

2016 ASEE Annual Conference & Exposition

Authors

Laura E Moody, Mercer University; Joan Burtner, Mercer University

Tagged Topics

Diversity

Tagged Divisions

Industrial Engineering

Paper ID #16575Opportunities, Challenges, and Locus of Control in Undergraduate Researchin Healthcare SettingsDr. Laura E Moody, Mercer University Dr. Laura Moody is an associate professor and chair of Industrial Engineering at Mercer University. Dr. Moody taught for 12 years in Mercer’s School of Engineering before leaving Mercer to spend 2 years as the manager of the North American Usability Group for Whirlpool Corporation. She returned to Mercer in 2003 and has served on the faculty of the Industrial Engineering and Industrial Management department ever since. At Mercer, she’s taught a variety of courses at the

Conference Session

Industrial Engineering Division Technical Session 2

Collection

2016 ASEE Annual Conference & Exposition

Authors

Eric Specking, University of Arkansas; Brian W. Henderson, University of Arkansas; Bryan Hill, University of Arkansas

Tagged Topics

Diversity

Tagged Divisions

Industrial Engineering

Paper ID #14640Perception: Industrial Engineering JobsMr. Eric Specking, University of Arkansas Eric Specking serves as the Director of Undergraduate Recruitment for the College of Engineering at the University of Arkansas. He directs the engineering recruitment office, most of the College of Engi- neering’s K-12 outreach programs, and the college’s summer camps. He received a B.S. in Computer Engineering and a M.S. in Industrial Engineering from the University of Arkansas.Mr. Brian W. Henderson, University of Arkansas Brian Henderson is the Director of Employer Relations for the University of Arkansas College of Engi