The Newsvendor Challenge | Interactive Teaching Platform

Your role

Order today. Learn demand tomorrow.

You manage a campus pastry counter. Every morning brings a new forecast or a change in the unit economics. Choose how many pastries to prepare before demand is revealed, then learn which input moved the optimal quantity. The model assumes normally distributed demand with the displayed mean and standard deviation.

1

Identify the change

Compare the morning’s brief with the baseline.

2

Predict, then choose Q

First predict the direction of Q*, then commit a quantity.

3

Explain the movement

Use immediate feedback to connect the changed input to Q*.

The eight-morning challenge

What will change?

After the baseline, five mornings change exactly one input at a time. The seventh combines two opposing changes, and the eighth is a capstone. This lets you learn what moves Q* before solving the full problem.

Your mission Maximize cumulative profit over eight rounds. After each result, compare your choice with that morning’s Q* on the same realized demand.

Demand level: μ
Uncertainty: σ
Economics: C_u/C_o

Time: about six minutes. No calculator required.

Round 1 of 8

Your profit$0

Gap vs Q*—

Morning 1 brief

Regular weekday

A normal day at the campus counter.

Baseline

Mean demand: 100
Std. deviation: 20
Sell: $10
Cost: $4
Salvage: $1

Demand will be revealed only after you commit.

Before calculating: where should Q* sit relative to mean demand?

Below mean At mean Above mean

Order quantity, Q

units

Need a decision hint?

Compare the marginal costs:

C_u = r − c = $6 for a unit you could have sold but did not stock.

C_o = c − s = $3 for a unit ordered but left unsold.

The critical ratio is 0.667. Because this is above 0.5, Q* lies above mean demand. Greater uncertainty makes that upward buffer larger.

Decision log

Your mornings so far

Order, demand, outcome, and profit for completed rounds
Morning	Market brief	Order Q	Demand	Outcome	Profit
Your first result will appear here.

Eight mornings complete

Your policy, explained

Your total profit $0 Decision quality: avg. 0 units from Q* · Predictions: 0/8

Adaptive Q* policy $0 Uses that morning’s optimal order

Mean-demand policy $0 Orders that morning’s forecast mean

Comparing replays: Market scale changes between versions, so compare your distance from Q* and prediction score—not raw profit totals.

The long-run solution

Why Q* changed across mornings

1

C_u = selling price − unit cost

2

C_o = unit cost − salvage value

3

CR = C_u/(C_u+C_o) sets the target demand percentile

4

Q* = μ + z(CR)σ combines the forecast, economics, and uncertainty

Swipe the table to compare your Q with Q*

Brief	μ	σ	CR	Prediction	Your Q	Q*