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Industrial Engineering (2nd)

These projects are at the endpoint of a two-semester sequence. They are functionally complete.

Cycle Time Reduction Through Kanbans

Sponsor: Signify

Student Team: Reese Willhite, Alejandro Aguirre, Francisca Robbe, Ricardo Ramirez

Faculty Advisor: Dr. Pat Thomas

Signify is the new company name of Philips Lighting, having spun off from Philips to become a separate company in 2016. Signify is the world leader in connected LED products, systems, and services. Founded as Philips in Eindhoven, The Netherlands, they have led the lighting industry with innovations that serve professional and consumer markets for more than 127 years. With operations in more than 70 countries and 32,000 people worldwide, Signify generated sales of approximately $8 billion in 2017.

Signify’s facility located in San Marcos, Texas focuses on the assembly of outdoor lighting products. After recognizing that product lead times were significantly longer than their competitors, Signify initiated a Factory of the Future program. This project, under the umbrella of the Factory of the Future program, will utilize Lean Manufacturing tools, techniques, and principles such as value-stream analysis, visual control, and kanbans to decrease overall in-process inventory, reduce part pick-times, and minimize product cycle time for Signify’s customers.

Poster


Airline Flight On-Time Performance Modeling Using Machine Leaning

Sponsor: Sabre Corporation

Student Team: Nydia Huynh, Carlos Alvarez, Anthony Lako

Faculty Advisor: Dr. Pat Thomas

Sabre Corporation is a global technology leader in the travel industry. Based out of Southlake, Texas, Sabre began operations in 1960 as a joint initiative between IBM and American Airlines to create a computerized airline reservation system. Being one of the world’s largest software companies, Sabre’s technology is utilized by more than a billion people across the world to plan, book, and get to their desired destination as efficiently as possible.

Sabre strives to improve all aspects of airline transportation. One key metric is an airline’s on-time performance rate. Several machine learning and regression models, coded in Python, will be used and compared to analyze the extent to which various input variables most impact on-time performance as well to predict on-time performance.

Poster