About The Position

This role focuses on leveraging industrial engineering analytics to optimize manufacturing systems and processes through modeling. The engineer will be responsible for developing and implementing models related to capacity, labor, cost, and material flow, as well as performing scenario analysis and forecasting. A key aspect of the role involves integrating shop-floor data with analytics systems to drive improvements.

Requirements

  • 7+ years in industrial engineering analytics/manufacturing modeling.
  • Experience building end-to-end IE models (capacity + labor + cost).
  • Strong exposure to factory operations, throughput optimization, and cost analysis.
  • Experience with business case development and financial modeling.
  • Hands-on work integrating shop-floor data, MES, and analytics systems.
  • Proficiency in capacity modeling (OEE, cycle time, bottleneck analysis).
  • Proficiency in labor and cost modeling (COGS, LOH, ROI/NPV/IRR).
  • Proficiency in manufacturing analytics & process optimization.
  • Experience with simulation tools (FlexSim / AnyLogic / Simio).
  • Data analytics skills: Excel (advanced), SQL, Python, BI tools (Power BI/Tableau).
  • Experience with PFEP and material flow modeling.
  • Experience with scenario analysis, sensitivity modeling, and forecasting.

Nice To Haves

  • AI/ML-based analytics experience.

Responsibilities

  • Develop and maintain capacity models, including OEE, cycle time, and bottleneck analysis.
  • Create and manage labor and cost models, encompassing COGS, LOH, and ROI/NPV/IRR.
  • Perform manufacturing analytics and process optimization.
  • Utilize simulation tools (FlexSim, AnyLogic, Simio) for modeling manufacturing environments.
  • Conduct data analysis using Excel, SQL, Python, and BI tools (Power BI/Tableau).
  • Model PFEP and material flow.
  • Perform scenario analysis, sensitivity modeling, and forecasting.
  • Integrate shop-floor data, MES, and analytics systems.
  • Develop business cases and perform financial modeling.
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