Doughnut Truck
A fast, focused simulation where each participant runs their own doughnut truck and decides, day after day, how many to make against uncertain demand. Perishable inventory, decisions under risk, profit that compounds.
One simple decision. A lesson that sticks.
Doughnut Truck strips perishable-inventory management down to its essence: a single, repeated decision under uncertainty. No clutter, no manuals, just the core trade-off every operations manager faces, made vivid.
Newsvendor model
The classic perishable-inventory problem in its purest form: how much to stock when demand is unknown and leftovers are lost.
Uncertain demand
Demand changes every day, with a weekday and weekend rhythm, and is never known in advance. You forecast, then live with it.
Perishable goods
What you do not sell today is wasted tomorrow. There is no second chance and no carry-over: every unit is a bet.
Minimal interface
A sleek, single-screen design keeps the focus on the decision, not on learning the platform. Productive in minutes.
Welcome to your doughnut truck
No spreadsheets, no backstory to memorize. A clean, relatable premise that any participant grasps in thirty seconds, and that hides a genuinely hard optimization problem.
The setup
Monday is the first day of your new doughnut shop. You have no prior information about demand, but you know that, given the size of the market, it will never exceed 20 doughnuts a day.
Each day you decide how many doughnuts to make for the next day, knowing that any unsold doughnut is wasted. Each one costs $1 to produce and its selling price is fixed by the authorities at $2. How much profit can you generate over 30 days?
Starting parameters
| Production cost | $1 / unit |
| Selling price (fixed) | $2 / unit |
| Margin per sale (Cu) | $1 |
| Overstock cost (Co) | $1 |
| Daily demand | uncertain · ≤20 |
| Horizon | 30 days |
The question the whole simulation turns on: how many doughnuts should you make today to maximize tomorrow profit?
Two ways to be wrong. One sweet spot.
Every single day you are caught between two opposite risks. Make too many and you eat the cost of waste; make too few and you leave margin on the table. The art is finding the point that balances them.
Make too few
Demand shows up and you have nothing to sell. Each missing doughnut is $1 of margin walking out the door, and a customer who may not return.
Underage cost · Cu = $1The sweet spot
Produce up to the point where the chance of selling the next doughnut just covers its risk of being wasted. That balance is the critical fractile.
Critical fractileMake too many
Whatever does not sell goes in the bin. Each unsold doughnut is $1 you already spent and will never recover. Caution has a price too.
Overage cost · Co = $1Underage cost
The margin lost on each unmet sale: price minus cost, $2 − $1 = $1.
Overage cost
The money sunk into each unsold, perishable unit: the $1 production cost.
Optimal quantity
The order quantity at the critical ratio. Here the costs are symmetric, so the answer is the median of demand.
Because Cu and Co are equal in the base case, the model is beautifully symmetric: the perfect first encounter with the formula before introducing asymmetric costs.
Move the dial, watch the profit move
Set how many doughnuts you make each day and what the authorities let you charge. The calculator runs the same newsvendor arithmetic the participants meet in the simulation.
Your daily decision
Demand is uniform between 0 and 20 doughnuts a day, and each one costs $1 to make.
Illustrative model: uniform demand between 0 and 20 and a fixed $1 production cost. In the simulation demand follows a weekday and weekend rhythm, and the instructor sets the economics.
The loop of every day
The same short cycle repeats thirty times. Each pass takes seconds, but the feedback accumulates into a clear, personal learning curve.
Decide and produce
Choose how many doughnuts to make for tomorrow.
Demand is revealed
The day closes and real demand appears.
Sold, wasted, lost
Sales, waste and missed demand are tallied.
Profit banked
The day profit is added to your running total.
Next day
Repeat for 30 days, learning as you go.
Daily order decision
Each day, participants decide how many doughnuts to produce for the next day, without knowing exact demand.
Demand uncertainty
Demand varies day to day, with weekday and weekend patterns. Overstock means waste; understock means lost sales.
Profit maximization
Players hunt for the order quantity that maximizes cumulative profit across the whole run.
Instant visualization
Each day shows the doughnuts sold, wasted and unmet as clear icons: feedback you feel, not just read.
What participants take away
A focused experience built on the Kolb experiential cycle: do, reflect, conceptualize, try again. Concepts land because participants feel the consequences first-hand.
Demand forecasting
Grasp the essentials of forecasting demand for perishable goods and the underlying principles of the newsvendor model.
Decide with stock costs
Build decision-making skills to manage inventory optimally, weighing the costs of stockouts against overstocks.
Probabilistic thinking
Move from a single guess to thinking in distributions, and confront the pull-to-the-mean bias head-on.
From data to decision
Read each day outcome and turn it into the next day choice, closing the analysis-to-action loop.
Collaboration and communication
When run in teams, strengthen the ability to align on a strategy and defend it with evidence.
Fast, comparable debrief
Benefit from quick review sessions that solidify understanding and pinpoint operational improvements.
Pedagogical anchor: the critical fractile Cu / (Cu + Co), introduced through experience and then formalized at the debrief.
What gets measured gets discussed
Throughout the run, three live views give participants and the instructor an honest mirror of each decision and its consequences.
Orders
Produced, sold and actual demand, day by day: the full decision trail.
Cumulative profit
The headline score: how the running total climbs, or stalls, across 30 days.
Customer satisfaction
The fill rate: how often demand was met, the human cost of stocking out.
Comparable across cohorts
With a consistent demand profile, results line up across participants and sessions, turning the debrief into a benchmarking exercise and enabling clean cross-cohort analysis.
Full control, zero friction
A real-time instructor panel runs the whole room: launch and pace the game, tune the economics, and toggle the features that fit your group.
Live monitoring
See who is connected, who has played and overall progress, round by round.
Pause and resume
Freeze the room for a discussion, then resume automatically on the next round.
Tune the economics
Set price, cost, number of days and the per-day demand profile, which can be uploaded by CSV.
Send access by email
One click delivers each participant their personal link and credentials.
AI assistant
Optionally give participants an in-game AI chat assistant for guidance.
OptionalRound timer
Show a countdown each round to add time pressure and keep the room in sync.
OptionalTips and news
Surface start-of-round tips and an optional, AI-driven news feed for context.
OptionalRooms and alerts
Assign physical or virtual rooms and push real-time alerts to participants.
OptionalWhere the learning happens
The game ends and the conversation begins. The simulation is the experience; the debrief is where it turns into transferable knowledge.
Introduction
Frame the doughnut truck, the rules and the goal. Participants are playing within minutes.
Define a strategy
Before playing, each participant or team commits to an approach: how many to make, and why.
Play the 30 days
Run the simulation. Pause mid-way to let teams re-plan as the demand pattern reveals itself.
Debrief and present results
Project the comparative results, surface the patterns and formalize the critical fractile.
Discussion questions that drive it home
What was your average daily production? Was it closer to the mean of demand, or did you systematically play it safe?
Did you over-react to a single bad day, swinging your order up or down? That is the bullwhip instinct in miniature.
Did you spot the weekday and weekend rhythm in demand, and adjust production accordingly?
How does your profit compare with the theoretical optimum of about $5 a day? Where did the gap come from?
Most people under-order to avoid visible waste. Did you? What does that say about loss aversion in operations?
What would change if the price were $4 instead of $2, pushing the critical ratio toward stocking more?
The insight to land
Most participants systematically under-produce: the waste of an unsold doughnut feels worse than the invisible loss of an unmet sale. Naming that bias, then deriving the critical fractile, is the moment the model clicks.
Doughnut Truck at a glance
Everything you need to position, schedule and run the simulation.
An ideal first simulation for students new to operations management: quick to run, easy to debrief, and reproducible across cohorts.
Common questions answered
It is the classic perishable-inventory question: how many units should you make when demand is uncertain and whatever you do not sell is lost? Make too few and you forgo margin; make too many and you pay for waste. The balance point is the critical fractile, Cu / (Cu + Co).
Under an hour. The 30 trading days take 30 to 45 minutes; a typical run is 10 minutes of introduction, 5 to define a strategy, 15 of play and 20 of debrief.
There is no participant limit: everyone runs their own truck. It can also be played in teams, with the group agreeing a single production strategy, and results are compared across the room at the debrief.
Yes. From the instructor panel you set the selling price, the production cost, the number of days and the day-by-day demand profile, which can be uploaded as a CSV. Raising the price moves the critical ratio and rewards producing more.
None. The premise takes thirty seconds to explain and the interface is a single screen, so the room is playing within minutes. It is an ideal first simulation for anyone new to operations management.
Let us talk about your implementation
Write to us about how we can help you achieve your learning objectives.
General inquiries
info@eurekasimulations.comPhone
(+34) 877 245 676Technical support
tech@eurekasimulations.comReady to get started?
Bring the newsvendor model into your classroom
Request a demo and we will show you how Doughnut Truck fits into your operations course.