Simulation games

Each game can run as a single session or slot into a module. Below you’ll find what students decide, the data you receive, and the typical run profile. Use the links to jump to a game.

McKesson Car Wash

A full-service wash on a tight lot: manage attendants, waiting space, and an optional time-guarantee promotion while balancing waits and profitability. Lost space leads to balking (lost sales). Weather and weekend patterns add realism.

Students decide

• Attendant staffing levels (and when to adjust)
• Whether to lease overflow waiting capacity
• Whether to run a time-guarantee promotion (demand vs. speed trade-off)
• Optional: process standardization to reduce variability (Case B)

Data you get

• Arrive / start / finish / balk timestamps
• Wait & flow times, throughput, utilization
• Profit breakdown (labor, overflow, promo effects)

Run profile: 20–45 min • 12–80 students • Pairs with Capacity & Variability.

Widget Wizards Assembly Line

Operate a three-stage serial line, locate the bottleneck, and tune buffers and staffing to hit throughput targets with minimal WIP. Designed to make bottlenecks and Little’s Law tangible in one class period.

Students decide

• Worker allocation across stages
• Buffer limits / release pacing
• Optional: variability or downtime “twists” (Case B), depending on your setup

Data you get

• Station utilization & queueing/WIP by stage
• Job-level flow times and throughput
• Time series for WIP and completions

Run profile: 20–45 min • 12–80 students • Pairs with Flow & Bottlenecks.

Widget General Hospital

Balance ED and ICU capacity under stochastic arrivals and variable lengths of stay. ED has finite beds and waiting-room capacity; patients may balk if the waiting room is full or renege after long waits. ICU bottlenecks can stall ED flow.

Students decide

• ED and ICU bed surge capacity (add/remove with costs)
• When to expand vs. control costs given variability
• Optional: elective scheduling / weekly seasonality (Case B, if enabled)

Data you get

• Time-stamped events (arrivals, waits, balk/renege, ED/ICU admissions & discharges)
• Queue & bed occupancy time series
• Profit/cost components and “critical event” counts

Run profile: ~60 min class • 30 simulated days • Debrief with ED↔ICU coupling and LWOBS-style metrics.

ReliableCo Manufacturing

Plan workforce and overtime to meet strongly seasonal demand while minimizing total cost. Long queues risk balking/reneging (lost sales). Building inventory reduces lost sales but increases holding cost. Teams learn the planning logic behind capacity smoothing vs. chasing demand.

Students decide

• Monthly FTE (hire/layoff) and overtime rules
• Whether to pre-build inventory vs. rely on overtime
• Optional: Sustainability Considerations (Case B, if enabled)

Data you get

• Timestamps for arrivals, balks/reneges, completions/shipments
• Inventory, queue, service outcomes over time
• Cost breakdown (labor, OT, hiring/layoff, holding, lost sales)

Run profile: 45–75 min • 12 months simulated • Objective: minimize total cost (with service constraints).

Beer Game

The classic multi-stage supply chain game (retailer–wholesaler–distributor–factory) modernized for fast classroom play. Students experience information delays, shipment lags, and ordering dynamics that generate the bullwhip effect—even when customer demand is stable. Includes instructor controls for round length, horizon, and scenario variants.

Students decide

• Weekly order quantities under lead time and backlog risk
• How aggressively to “correct” inventory vs. smooth orders
• Optional: coordination/information sharing rules (if enabled)

Data you get

• Orders, shipments, inventory, backlog by role over time
• Total cost and cost components (holding vs. backlog)
• Variance/bullwhip metrics for debrief

Run profile: 30–60 min • 12–80 students • Pairs with Inventory, Lead Times & Bullwhip.

BeanWorks (Inventory Replenishment)

Manage a single-stage inventory system with lead times using a continuous-review policy (e.g., (r, Q)). Students choose reorder point and order quantity, then experience the real trade-off between stockouts/backlogs and holding cost under noisy demand. Designed to fit cleanly before (or alongside) the Beer Game.

Students decide

• Reorder point r and order quantity Q
• Optional: adjust policy after observing performance (if enabled by instructor)
• How to interpret historical demand + lead time to set safety stock

Data you get

• Demand, inventory position, orders, receipts over time
• Service outcomes (stockouts/backlogs), fill rate
• Cost breakdown (ordering, holding, backlog/stockout penalties)

Run profile: 25–50 min • 12–80 students • Pairs with Inventory Policy (r,Q) & Safety Stock.

Looking for how these map into class? See curriculum-aligned modules.