We are looking for a Battery Factory Simulation Engineer. As such, you will develop and own factory-level simulation models for battery manufacturing to perform bottleneck analysis, capacity studies, line balancing, throughput optimization, logistics flow design, ramp-up decision-making, and what-if scenario analysis.
You will build a multi-scale process simulation approach linking factory-level material/energy flow and overall throughput with process constraints, equipment behavior (utilization, yield, WIP, energy, cycle time, takt time, MTBF, MTTR, …), 0D models, operational logic, and cost.
You will define model requirements, collect equipment and process model inputs, contribute to constructing the manufacturing database, and interface with process, automation, CAE, and cost engineers.
Main responsibilities:
1-Build and maintain factory simulation models - Develop and own a factory simulation framework (discrete event simulation) for battery manufacturing covering:
- Electrode Manufacturing: Mixing, Coating, Drying, Calendering, Notching
- Cell Assembly: Stacking, tab welding, EL filling, Sealing
- Formation and Aging
- Module Assembly: Cell sorting, Welding, Assembly, end-of-Line testing
- Internal Logistics: Buffers, Conveyors, AGVs, Storage and Material replenishment
- shared factory resources/constraints (dry rooms, utilities, …).
- Integrate 0D models, engineering correlations, equipment constraints, process windows, and other key operational rules required to keep factory scenarios technically and operationally viable.
- Model and workflow standardization, change tracking, automation and documentation
2-Equipment/process Model requirements, development, and parameter identification
Establish and own model requirements for each equipment/process unit and collect database/models required for system-level integration.
Develop equipment/process 0D models for integration into factory model in collaboration with process engineers, data scientists and CAE engineers
Develop and implement control strategies in the model per equipment, zone or at factory level
Integrate domain knowledge into the equipment/process models in collaboration with process engineers, data scientists and CAE engineers
Maintain model assumptions and limitations and parameter/model-change traceability
3-Perform production flow/bottleneck analysis and optimization and evaluate advanced process technologies
Perform production flow, bottleneck, capacity, and line balancing studies to identify constraint stations, queue formation, WIP accumulation, starvation/blockage patterns, utilization losses, and throughput limitations.
Run what-if scenario studies for equipment/buffer sizing, resource allocation, layout changes, logistics strategies, product mix, ramp-up trajectories, changeovers, downtimes, and utility-constrained cases.
Identify feasible/optimized operational logic that achieves pilot line/gigafactory objectives (production, yield, scrap, …) including machine states, dispatching assumptions, batching/sequencing logic, material replenishment triggers, formation scheduling, …
Assess new equipment technologies and process innovations for throughput, yield, cost, quality, … within the full factory model
Examine control strategies to suggest/assess potential automation routes
Support cost-related studies by generating scenario-based inputs for bottom-up manufacturing cost modeling.
Analyze, validate, and communicate regularly simulation results and actionable recommendations to stakeholders
Requirements:
Master's (Bac+5) degree in Industrial Engineering, Manufacturing Engineering, Systems Engineering, Mechanical Engineering, Chemical Engineering or equivalent
5+ years of experience in discrete event simulation / manufacturing process simulation.
Experience in battery/semiconductor manufacturing or other complex high-volume industrial environments.
Experience with capacity studies, bottleneck analysis, line balancing, ramp-up studies
Experience with industrial simulation tools such as FlexSim, Siemens Plant Simulation, AnyLogic, Simio, Arena, or equivalent.
Good knowledge of Python, data analysis, and model/data integration practices.
Proficiency in English. French is a plus
