Capacity Planning Simulation Agent
Simulates production and warehouse capacity scenarios against forecasted demand to identify bottlenecks and recommend staffing or equipment investments.
Capacity planning is typically done through static annual spreadsheet models that quickly go stale as demand forecasts, staffing levels, and equipment availability change throughout the year, leaving operations leaders flying blind on whether they can actually meet an upcoming demand surge
Running what-if scenarios manually (e.g., "what if we add a third shift" or "what if Line 2 goes down for maintenance during peak") is slow and rarely done more than once or twice a year, so decisions get made without rigorous analysis
Bottleneck identification often happens reactively, after a facility has already missed output targets
This agent continuously simulates capacity against updated demand forecasts, runs configurable what-if scenarios on demand, and proactively identifies bottlenecks with quantified investment recommendations before they materialize into missed targets
The agent ingests current demand forecasts, production line throughput rates, labor capacity by shift, and planned maintenance/downtime schedules, then runs a discrete-event simulation model to project capacity utilization across the planning horizon. Users or scheduled triggers can request what-if scenarios (e.g., demand +20%, an added shift, a line outage) which the agent runs through the simulation engine to quantify impact on throughput, backlog, and on-time delivery. An LLM layer translates simulation output into plain-language findings and recommendations, quantifying the cost and lead time of addressing each identified bottleneck (e.g., overtime, additional shift, capital equipment). Results are published to a capacity planning dashboard reviewed in S&OP cycles.
Build Current-State Capacity Model
- Ingest production line throughput rates and labor capacity
- Pull demand forecast by SKU/period
- Load planned maintenance and downtime schedule
- Establish baseline capacity utilization projection
Run What-If Scenarios
- Accept scenario parameters (demand change, shift change, downtime)
- Run discrete-event simulation against each scenario
- Quantify impact on throughput, backlog, and on-time delivery
- Compare scenarios side by side
Identify Bottlenecks and Recommend Investment
- Flag lines/resources projected to constrain output
- Quantify cost and lead time of addressing each bottleneck
- Rank recommendations by ROI and urgency
- Translate findings into plain-language summary via LLM
Publish and Track Against Plan
- Publish scenario results to capacity planning dashboard
- Track actual throughput against simulated projections
- Refine simulation model based on actual performance
- Update recommendations as new forecast data arrives