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Digital Twin Engineer; Manufacturing Simulation

Job in Gastonia, Gaston County, North Carolina, 28054, USA
Listing for: MANN+HUMMEL GmbH
Full Time position
Listed on 2026-06-23
Job specializations:
  • Engineering
    Manufacturing Engineer, Quality Engineering
Salary/Wage Range or Industry Benchmark: 70000 - 90000 USD Yearly USD 70000.00 90000.00 YEAR
Job Description & How to Apply Below
Position: Digital Twin Engineer (Manufacturing Simulation)

Role Summary

Modeling and Simulation Engineer

MANN+HUMMEL

Onsite - Gastonia, NC #LI-Onsite

The Manufacturing Digital Twin Engineer owns and builds the plant-level Digital Twin for an IATF-certified automotive manufacturing facility (~375 employees) producing Heavy Duty and Industrial Air and Liquid Filtration. The role develops the capabailty from the ground up using AutoCAD and Flex Sim to model end-to-end production and internal logistics, run mixed-methods simulations, and provide data-driven recommendations to improve throughout, robustness, and investment decisions - while creating reusable standards/templates for global scaling.

Main

Tasks
  • Own the plant Digital Twin in AutoCAD + Flex Sim
  • Establish governed model structure (assets, routings, constraints, assumptions, versioning)
  • keep the model aligned to real-world conditions (including high-mix/low volume realities)
  • Update flows/routings/process steps end-to-end
  • Maintain capacity inputs: cycle times, changeovers, uptime/downtime
  • Maintains shift/labor patterns, buffers/WIP rules, operating contraints
  • Model internal logistics: warehouse/supermarkets, tugger/forklifts routes, replenishment logic
  • Validate and refresh inputs with stakeholders using MED and SAP data
  • Run studies for bottlenecks, constraint behavior, line balancing, rate changes
  • Evaluate buffer sizing/WIP strategies, material flow improvements
  • Assess staffing concepts and operating policies
  • Support investment/expansion scenarios (new lines, automation, warehouse, redesign)
  • Use DOE/parameter sweeps, heuristic optimization, structured experimentation
  • Identify new Digital Twin use cases with Manufacturing Engineering, Supply Chain, CI/Lean
  • Translate operational problems into simulation questions with defined inputs/outputs
  • Build repeatable templates for recurring analyses
  • Provide recommendations supporting capex justification, investment prioritizaiton, through output improvements, and robustness/sensitivity analysis
  • Communicate results to leadership with clear assumptions, scenarios, findings, and recommendation
  • Validate alternative layouts/logistics concepts/capacity changes through simulation
  • Collaborate with engineering while protecting model integrity and ownnership
  • Partner with the Global Digital Twin Program Lead
  • Package reusable standards: templates, modeling conventions, scenario libraries
Profile

Education

  • Bachelors degree in Engineering preferred
  • Equivalent combinations of education and/or experience will be considered.

Experience

  • 3 to 5 years of manufacturing engineering, industrial engineering, simulation engineering, or related roles in a manufacturing environment
  • Tool/application experience: hands-on building models in Flex Sim (or comparable DES tool) and using AutoCAD for industrial/manufacturing layouts.

Skills & Competencies

  • Discrete-event simulation (DES) capability
  • Flex Sim including building reusbale model components
  • AutoCAD in manufacturing layout context
  • Strong understanding of manufacturing systems (flows, constraints, variability, boittlenecks)
  • Ability to create models from ambiguous inputs and validate with stakeholders
  • Strong analytical and communication skills; explain assumptions/results to non-simulations audiences
  • Provide training and coach team members on the use of Flex Sim to support simulation modeling, analysis, and decision-making

Preferred Experience

  • Experience working in the automotive industry
  • Basic scripting/analytics exposure: SQL, Python, PowerBI
  • Using MES and SAP data for operational analysis
  • Optimization approaches: DOE, parameter sweeps, heuristic optimization
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