Position Summary:The
Supply Chain Simulation Engineer is a highly technical, execution-focused role dedicated to building, calibrating, and executing discrete event simulation models of Gordon Food Service's physical distribution network. Acting as the critical bridge between raw operational data and physical facility layout changes, this engineer transforms historical transactional logs, database queries, and CAD drawings into dynamic virtual operating environments.
This role is fully responsible for modeling, testing, and optimizing modular automation solutions (such as modular conveyor networks, automated sorting subsystems, and flexible robotic pick-and-place cells) alongside advanced Automated Storage and Retrieval Systems (AS/RS). Rather than relying strictly on pre-built software libraries, this engineer is expected to write custom scripting and build dynamic model logical pathways from scratch to reflect GFS's unique operational flows.
What you will do:- Model Construction & Customization: Build, customize, and calibrate high-fidelity, stochastic 3D warehouse layouts in FlexSim, Simio, or equivalent discrete-event simulation platforms. Write custom object logic, parameters, and physical routing behaviors from scratch when standard software libraries do not match GFS's unique facility layouts or slotting rules.
- Modular & Advanced Automation Validation: Construct simulation models to validate throughput, routing capacity, and operational impacts of modular automation systems (e.g., modular conveyor loops, automated sorting lines, and collaborative robotics) as well as automated storage systems.
- Cross-Functional Validation & Change Management: Collaborate directly with corporate Engineering teams, Change Managers, and Divisional representatives to ensure baseline simulations accurately reflect actual, on-the-floor warehouse behaviors, establishing trust and ensuring smooth post-pilot adoption.
- Data Preparation & Statistical Analysis: Partner with GTS and Data Analysts to write SQL and Alteryx queries to extract, clean, and analyze historical sales, transit, and inventory logs. Transform messy, raw datasets into clean statistical distributions (curve-fitting) to drive highly accurate simulation inputs.
- Process-Logic & Algorithmic Validation: Load and test standardized and custom process-decision rules (such as dynamic slotting, travel-path optimization, replenishment logic, and task-interleaving schemes) within the simulation environment to evaluate throughput efficiency, potential travel bottlenecks, and resource allocation before physical deployment.
- Scenario Testing & Sensitivity Analysis: Execute systematic "what-if" stress tests against proposed physical and logical changes under varying peak volume profiles, documenting system bottlenecks, congestion zones, and throughput limits.
- CAD Layout Maintenance: Ingest, optimize, and maintain master 2D/3D AutoCAD layouts from facility teams to ensure the physical dimensions and coordinate systems of the digital twin footprints remain current.
When you will work:- Monday to Friday, 8am to 5pm
- Hybrid schedule, 4 days in office in Wyoming, MI with 1 day remote
What you'll bring to the table:- Bachelor's Degree in Industrial Engineering, Operations Research, Systems Engineering, Computer Science or related quantitative field required.
- Masters Degree in Industrial Engineering preferred.
- Five years related Supply Chain experience including at least two years building discrete-event simulation models in logistics, distribution, or manufacturing environment required.
- Programming & Logic: Solid scripting capability within simulation software (e.g., custom logic scripting, FlexScript, C++, or Simio process steps) to program custom object interactions.
- Data Tools: Hands-on proficiency in SQL, Alteryx, or Python for data extraction, manipulation, and cleansing.
- Communication & Change Management: Exceptional interpersonal skills, with the ability to translate complex statistical outputs into clear, non-technical business recommendations and change-management strategies.
- Operations Knowledge: Deep understanding of traditional warehouse operations, material flow principles, rack layouts, and labor dynamics.
- Automation Exposure: Prior experience modeling modular automation solutions, conveyor systems, or AS/RS subsystems is highly preferred.
Equipment / Tools / Technology:- Simulation & Modeling Platforms: Discrete Event Simulation (DES) tools (such as FlexSim, Simio, AnyLogic, Arena, or equivalent).
- Data Prep & Analytics Pipelines: SQL , Alteryx, Microsoft Excel (advanced functions), and Python (for data cleaning and parameter setting).
- Drafting & Layout Software: 2D/3D AutoCAD (for ingesting, cleaning, and managing facility master drawings).
- Business Intelligence (BI) Tools: Power BI or Tableau to design operational dashboards and share bottleneck visualizations with division leaders.
- Warehouse Systems (Operational Context): Exposure and alignment with Warehouse Management Systems, Warehouse Control Systems (WCS), and automated material handling equipment (MHE) layout rules.
