Direct classroom scenario launch codes
Teachers and students can start all devices on the exact same baseline parameters using these direct scenario URLs:
Printable investigation and teacher guide
Test one operating decision while protecting garment-care quality, reliable turnaround, worker capacity, machine condition, cash, and customer trust.
Name: Date: Team role:
Investigation rule: keep the city, location, shop model, operating term, challenge, and all non-tested controls the same. Compare the same reporting interval. A changed result is evidence only when the comparison is fair.
Choose one question. Predict the operational chain, not merely whether profit will rise or fall.
Shop model and location:
Decision question:
Prediction: If we change , then because .
Record settings before advancing time. Use a baseline, change only one variable for the test, then repeat the baseline or test. If a random event occurs in only one run, label it and avoid treating the difference as a clean causal result.
| Design choice | Baseline | Test | Repeat / confirmation |
|---|---|---|---|
| Variable changed | None | ||
| Exact value | |||
| Controls held constant | |||
| Comparison interval | |||
| Random event or shock | |||
Fair-test check: Are starting cash, model, machines, staffing, pricing, service quality, stain care, turnaround, promotion, campaign, and ad budget matched except for the tested variable?
Use values from the profit breakdown and reports. Keep units and time periods consistent.
(Order revenue − supplies − utilities − delivery) ÷ completed orders
( − − − ) ÷ =
Daily fixed costs ÷ contribution per completed order
÷ = orders
Completed orders ÷ walk-in demand × 100
÷ × 100 = %
Month profit ÷ month revenue × 100
÷ × 100 = %
Reasonableness check: contribution should use per-order variable costs; profit also includes payroll, maintenance, marketing, and rent. A profitable day may not represent a profitable month if periodic costs have not yet appeared.
Capture the same measures at the same point in each run. Do not select only the metrics that support the prediction.
| Measure | Baseline | Test | Repeat | Test − baseline |
|---|---|---|---|---|
| Walk-in demand | ||||
| Orders completed | ||||
| Backlog / lost orders | ||||
| Capacity used | ||||
| On-time rate | ||||
| Stain quality / rework rate | ||||
| Machine condition | ||||
| Average order value | ||||
| Contribution per order | ||||
| Month revenue / costs | ||||
| Month profit / margin | ||||
| Satisfaction / review score | ||||
| Cash / staff morale | ||||
| Random event note |
| Evidence pattern | Likely explanation | Safer next test |
|---|---|---|
| Demand rises, but completed orders do not | Marketing or rush promises pushed work into a capacity constraint. | Hold demand settings constant; test the constrained role or slower turnaround. |
| Capacity use, backlog, and lost orders stay high | Cleaner, presser, intake, or machine capacity may be binding. | Change one role or service machines before buying capacity. |
| Stain quality falls while rework rises | Care level, service quality, machine condition, or workload is inadequate. | Test stain care or maintenance with price and demand held steady. |
| On-time rate falls under rush turnaround | The promise raises demand while reducing effective capacity. | Compare standard turnaround using matched staff and promotion. |
| Revenue rises but profit or cash falls | Discounts, payroll, delivery, supplies, rework, marketing, or maintenance absorbed the gain. | Compare contribution and each cost share before expanding demand. |
| Profit rises while quality or trust falls | The result may shift costs to later periods through rework, refunds, or lost demand. | Repeat longer and use quality, on-time, and satisfaction as stop rules. |
Machine expansion stop rule: do not add a machine merely because utilization is high once. Require repeated high utilization plus backlog or lost orders, acceptable stain quality and on-time performance, serviceable current machines, positive contribution, sufficient cash after the purchase, and a plan for any added labor.
Use this structure: We recommend [specific action] for [shop model and conditions]. Compared with the baseline, it changed [primary outcome] by [amount], while [guardrails] changed by [amounts]. The result is limited by [event, short interval, or model assumption]. Next we would test [one variable].
Strong answers explain a chain such as: a rush promise may increase conversion and average order value, but it also reduces effective capacity and increases delivery burden; if backlog then lowers on-time performance, extra demand may not become reliable profit. Machine service can be preferable to expansion when condition is limiting current capacity and raising rework. Advertising is not automatically productive: judge it by completed contribution after marketing cost, not by walk-in demand alone.
Accept different recommendations when the calculations are correct and the evidence supports them. Ask students to revise claims based only on one daily observation, unmatched settings, or revenue without cost and quality evidence.
| Criterion | 3 points | 2 points | 1 point | 0 points |
|---|---|---|---|---|
| Experiment design | One variable, matched controls, repeat | Mostly controlled | Several settings differ | No comparison |
| Calculations | Accurate, labeled, reasonable | Minor error | Major error or unclear units | Missing |
| Evidence diagnosis | Connects finance, flow, quality, and trust | Uses several relevant metrics | Uses one isolated metric | Unsupported |
| Recommendation | Specific, bounded, ethical, testable | Supported but incomplete | Overstated | Missing |
The simulator is a simplified fictional model, not operating, chemical, environmental, legal, financial, or safety advice. Real dry cleaning businesses must verify local requirements for solvent and chemical handling, ventilation, fire prevention, worker protection, equipment inspection, hazardous waste, wastewater, packaging, garment labels, accessibility, employment, taxes, consumer remedies, privacy, and advertising claims.
Never treat speed or margin as permission to compromise garment care, worker safety, truthful turnaround promises, complaint handling, or environmental controls. Promotions should state material conditions clearly. Do not use deceptive claims, fake reviews, hidden fees, unsolicited bulk messages, or invalid traffic. Classroom records should use fictional shop and customer information.
Use at least three matched runs: a baseline, one changed-variable test, and a repeat or confirmation run. Add runs when random events make two outcomes hard to compare.
Do not optimize one metric alone. Connect profit or contribution with completed orders, backlog, on-time rate, stain quality, rework, machine condition, and customer outcomes.
Add capacity only after repeated evidence shows high utilization, persistent backlog or lost orders, healthy quality and machine condition, and enough contribution and cash for the investment and extra staffing.
Yes. Teams can rotate roles on one device, or students can diagnose projected or teacher-recorded matched results on paper.
No. It is a fictional learning model. Real operators must follow all applicable safety, chemical, environmental, employment, accessibility, and consumer requirements.