Manufacturing Process Engineer

  • by
Location
Candidate No.
1917261

About this Candidate

DAVRON CV: Candidate 1917261

INDUSTRIAL / MANUFACTURING ENGINEER | MEDICAL DEVICE PROCESS IMPROVEMENT & AUTOMATION

Core Expertise

  • Implementing welding automation delivering ~50% efficiency gains and improved product yield
  • Conducting DOE, Gage R&R, OQ/PQ to validate automated processes and ensure reliability
  • Performing time studies and capacity modeling to optimize headcount and equipment
  • Leading DMAIC and Lean Six Sigma initiatives to reduce defects and streamline workflows
  • Updating MPIs, IQs, and BOMs to improve compliance and reduce operator errors
  • Deploying Power BI and ERP analytics to monitor KPIs and inform production decisions

Project Background

  • Led automated welding system deployment from DOE through PQ, increasing throughput and yield
  • Redesigned assembly lines with new IQs and MPIs to raise production efficiency
  • Built capacity models and line balances based on time studies to eliminate bottlenecks
  • Implemented Kanban and 5S at shop floor to improve material flow and organization
  • Coordinated production plans with quality, procurement, and logistics to maintain standards

Key Skills

  • Power BI | ERP (M2M) | MES systems | MS Project | JIRA
  • Lean Six Sigma | DMAIC | DOE | Gage R&R | OQ/PQ/IQ
  • Time studies | Capacity modeling | Line balancing | Production planning
  • Minitab | MATLAB | AnyLogic | SolidWorks | AutoCAD
  • Process documentation | MPIs | BOM updates | Quality compliance

Education

  • M.S., Engineering — University of Wisconsin–Platteville (Engineering Management emphasis, in progress)
  • B.S., Industrial Engineering — University of Wisconsin–Platteville

Awards & Recognition

  • Six Sigma White Belt
  • Engineering Management Certificate

Why Interview this Candidate?

A results-driven industrial engineer with 2+ years improving medical device manufacturing through automation, time studies, and Lean Six Sigma. They led a welding automation project that boosted yield ~50% and cut labor costs 35% via capacity modeling and line balancing. Proficient with ERP/MES analytics and Power BI, they translate data into production decisions and align cross-functional teams for scalable process improvements.

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