Advanced Manufacturing Intern

## About JLG, an Oshkosh company JLG began in 1969, when our founder, John L. Grove set out to resolve growing safety concerns in the construction industry. Since then we have been committed to understanding the challenges and delivering innovative solutions to the access market. We partner with customers to provide quality equipment, training opportunities and trusted support within the access industry. We are a global company, and our products—including mobile elevating work platforms, telehandlers, utility vehicles and accessories—can be found all over the world. Assist the Manufacturing Engineering department in successfully achieving manufacturing goals and contribute to continuous improvement efforts. The Manufacturing Data Analytics Intern will receive assignments from the Manager of Manufacturing, or an assigned representative, and will be encouraged to contribute ideas and suggestions. ## YOUR IMPACT - Support visualization of factory data (quality, availability, performance) to provide insights and make decisions - Create Custom Ignition dashboards Provide statistical analysis of weld, paint, and assembly data to create predictive models – - Suggest Opportunities for automated feedback in paint system, smart torque tools, as well as new robotic welding systems - Participate in time studies, line balancing, and ergonomic assessments. - Perform work station/cell analyses to improve productivity and quality. - Provide input for manufacturing fixtures and tooling. - Use AutoCAD or similar tools to create and revise plant layout drawings. - Learn and use ERP manufacturing software. - All other duties or projects as assigned. ## MINIMUM QUALIFICATIONS - Student working towards a Bachelor’s degree in Data analytics, Industrial engineering, Manufacturing or mechanical engineering or computer science - Cumulative GPA of 3.0 or above. ## STANDOUT QUALIFICATIONS - Proficient in Excel and SQL - Familiar with Power BI - Familiar with lean manufacturing principles, six sigma or industrial process optimization