Code
Simulating Queueing for a 40-Person SaaS Support Team
Overview
What this challenge is about.
Build a discrete-event simulation of the ticket handling process: tickets arrive randomly (Poisson), are triaged, then assigned to specialists (tier 1 and tier 2). Calibrate using historical data (provided). Run experiments to determine the minimum number of agents per tier to achieve 95% of tickets answered within 2 hours. Deliver a simulation model (Python or AnyLogic), a cost-benefit analysis of hiring vs. automation, and a final recommendation.
The Brief
What you'll do, and what you'll demonstrate.
Determine the optimal staffing levels for a two-tier support queue to meet enterprise SLA of 2-hour response time while minimizing labor cost.
Earning criteria — what you'll demonstrate
- Model a stochastic service system using queueing theory and simulation
- Calibrate simulation parameters from real-world data
- Design and analyze simulation experiments to support decisions
- Evaluate trade-offs between service level and cost
Program Fit
Where this fits in your program.
Sharpens the same skills your degree expects you to demonstrate.
Skills
Skills you'll demonstrate.
Each one shows up on your verified credential.
Careers
Roles this prepares you for.
Real titles. Real skill bridges. Pick the one closest to your trajectory.
Career mappings coming soon.