Alexis Wade Email & Phone Number

Mesa, Arizona, United States

Phoenix, Arizona, United States

- Lead Process Engineer for Core Product Line, monitoring and directing eight sites across the United States. - Responsible for year-round strategic and financial planning, reporting on weekly and monthly calls with senior leadership and regularly engaging in continuous improvement activities. - Process mapped the complete production pipeline for Core.

Chandler, Arizona, United States

- Developed a standard simulation template for new users to quickly create Discrete Event Simulations (DES). - Served as a Plant Simulation Subject Matter Expert (SME) in the Digital Transformation Office (DTO) to incorporate manufacturing simulation into the cross-organizational digital toolkit. - Simulated both development and production environments for.

Tucson, Arizona, United States

• Designed a simulation for a developing program in Process Simulator for ~26 builds to identify bottlenecks, improve flow, and detect potential threats to program success.
• Utilized 7S methodology and Microstation to complete a re-layout of an engineering lab preceding the introduction of several new programs to the area; these changes resulted in improved flow and reduced touch time.
• Developed and maintained from-scratch work instructions (WI) for an upcoming program, involving frequent and close contact with operators and the ability to interpret engineering designs.
• Built hardware for several product lines, gaining a greater understanding of the transition from design to manufacturing, how products work, and the importance of the operator’s role.
• Generated Lean continuous improvement projects and Kaizen events to address waste on the factory floor, resulting in reduced cost and increased capacity (line balancing, layouts, capacity analysis).
• Created value stream maps (VSMs) for the operations performed on a cryogenics factory floor.

Worked part-time during the semester to assist the Fulton School of Engineering (FSE) in improving retention rates among first-time freshmen. In this role, I used both statistical and machine learning methods (Logistic Regression, Random Forest, Naive Bayes, Adaboost, etc.) to create models that positively identified factors affecting a student's decision.

Tucson, Arizona Area

Tucson, Arizona Area

Tucson, Arizona Area

Master Of Science - Ms, Industrial Engineering, 3.9

Bachelor'S Degree, Industrial Engineering, 4.0

Bachelor Of Science - Bs, Industrial Engineering, 4.0

Industrial Engineering, 4.0