Industrial Engineer

Role Overview

• The Industrial Engineering Analytics Engineer will lead the development and application of advanced analytical models to drive manufacturing efficiency, capacity planning, and cost optimization.
• This role is responsible for building and managing integrated IE models that connect capacity, labor, material flow, PFEP, and cost (COGS) to enable data-driven decision making across factory and site operations. The ideal candidate will combine strong industrialengineering fundamentals with advanced analytics, simulation, business case development, and AI-driven systems to support large-scale manufacturing environments.

Key Responsibilities

• Develop and own integrated IE models that connect capacity, labor, material flow, PFEP, and cost (COGS) to support factory planning and operations
• Build and maintain capacity models (target vs. forecast vs. gated capacity), incorporating cycle time, OEE, yield losses, and bottleneck analysis
• Develop labor models to optimize headcount, utilization, and labor cost (LOH) across production systems
• Create and evaluate business cases for capital investments, including ROI, IRR, NPV, and cost benefit analysis
• Lead COGS modeling, including labor, overhead, scrap, and process-driven cost components
• Develop and track scrap and yield models, quantifying cost impact and identifying improvement opportunities
• Design and maintain OEE models (availability, performance, quality) to drive operational efficiency and continuous improvement
• Perform buffer and WIP analysis to optimize inline and interline storage, reduce bottlenecks, and stabilize production flow
• Develop process flow diagrams (PFDs) and value stream maps to represent manufacturing systems and identify inefficiencies
• Integrate PFEP (Plan for Every Part) data into models to optimize material flow, storage, and line-side delivery strategies
• Support factory layout, site planning, and material flow decisions through data-driven insights and modeling
• Perform scenario analysis and sensitivity studies to evaluate production strategies and capacity expansion plans
• Utilize and/or develop factory simulation models (e.g., FlexSim, AnyLogic, Simio) to analyze throughput, bottlenecks, and system performance
• Support factory ramp-up, installation, and operational readiness through model validation and performance tracking
• Collaborate with cross-functional teams (Manufacturing, Operations, Supply Chain, Finance,
• Engineering) to align models with real-world constraints and business needs
• Translate complex analytical outputs into clear, executive-level insights and recommendations
• Collaborate with MES and Controls teams to integrate shop-floor data with IE models, ensuring accurate OEE measurement and enabling real-time, scalable dashboards for operational visibility and executive decision-making

AI & Data Systems

• Introduce and implement AI-driven tools and platforms to enhance industrial engineering analytics and decision-making
• Design and manage scalable data models and data architecture for IE, capacity, labor, PFEP,and cost analytics
• Develop standardized systems, frameworks, and governance for data modeling, analytics, and reporting
• Automate data collection, validation, and reporting pipelines using AI and advanced analytics tools
• Enable predictive analytics and intelligent decision-making for capacity, throughput, and cost optimization
• Establish best practices for data quality, model standardization, and system integration across the organization

Basic Qualifications

1. Greenfield or brownfield project experience (good to have)

2. Equipment planning

3. Capacity planning

4. Labour planning

5. CAPEX management (good to have)

6. Supplier validation

7. Capital investments – ROI, IRR, NPV, and cost-benefit analysis

8. Design and maintain OEE models

9. Support factory ramp-up, installation, and operational readiness through model validation and performance tracking

10. Material planning

11. PFMEA

12. Lean Manufacturing

13. Six Sigma

14. Layout planning (good to have)

15. Simulation tools experience (not mandatory)

16. Strong expertise in Excel

17. Knowledge of AI-driven tools (good to have)