Production Planning Simulations

Production planning requires balancing demand forecasts, inventory targets, capacity limits, and operational constraints under uncertainty. Simulation provides a way to test planning decisions before committing resources.

This project builds discrete-event simulation models for production systems, focusing on:

• Scenario analysis — what happens when demand spikes, a machine goes down, or a supplier is late.
• Capacity planning — identifying bottlenecks and evaluating the impact of adding or reconfiguring resources.
• Policy evaluation — comparing push vs. pull scheduling, batch size rules, and release policies.

The simulations are implemented in Python using SimPy and are designed to be parameter-driven, so the same model can be adapted to different production environments by swapping input data.

Outputs include utilization reports, throughput distributions, and inventory trajectory visualizations — giving planners a quantitative basis for decision-making before committing to a schedule.