Run a controlled garage experiment, calculate job economics, diagnose the operating constraint, and recommend a change that protects customers, workers, cash, and repair quality.
Name:
Date:
Team role:
Learning goal: explain how repair demand, diagnostic speed, staff and bay capacity, parts availability, comeback risk, trust, and cost connect to profit. The highest revenue is not automatically the healthiest result.
1. Frame one useful investigation
Choose one decision question. Keep the city, garage location, format, challenge, and observation length stable so the comparison remains interpretable.
My investigation question:
Why this question matters to drivers, workers, cash, or long-term trust:
2. Plan a controlled comparison
Record a full baseline month before changing one important decision. Observe the test for the same time. Repeat it when a storm, fleet inquiry, review, shipment delay, lift outage, or travel rush makes one period unusual.
Independent variable—the one decision I will change:
Controlled variables—the settings I will keep stable:
Baseline setting:
Test setting:
Prediction: If I change __________ from __________ to __________, then __________ will change because:
Guardrail: I will reject the change even if profit rises when it causes this unacceptable safety, diagnostic-time, repair-quality, comeback, workload, trust, inventory, or cash outcome:
3. Calculate job economics before judging the strategy
Use the simulator’s profit breakdown, unit economics, and Finance tab. Keep dollars, jobs, days, and percentages labeled. These calculations organize evidence; they do not replace checking repair quality, trust, inventory, comebacks, or lost jobs.
Variable cost per job (parts cost + comeback cost + job-level utilities) ÷ jobs completed
Contribution per job average repair order − variable cost per job
Break-even jobs per day daily payroll, rent, and marketing ÷ contribution per job
Capacity check: Can current mechanics, support staff, parts flow, and bays complete the break-even volume without unsafe workload, long diagnostics, or poor-quality work? ☐ Yes ☐ No ☐ Unsure
Show one calculation with units:
4. Record baseline, test, and repeat evidence
Use the same observation length for each run. Record an event note so random demand or supply shocks are not mistaken for the effect of your decision.
Run
Decision tested
Requests
Jobs completed
Lost jobs
Average order
Diagnostic time
Repair quality
Parts inventory
Comeback risk
Customer trust / review
Month revenue
Month profit
Event note
Baseline
Test
Repeat
Profit difference, test − baseline:
Diagnostic-time difference:
Comeback-risk difference:
Strongest repeated pattern:
Confounding event or uncertainty:
5. Diagnose the constraint before recommending a change
Choose the pattern best supported by the run table, then explain the chain from decision to operating result to financial result.
Evidence chain: The decision changed __________, which changed __________, which changed profit/cash because:
Alternative explanation:
6. Make an evidence-bounded recommendation
Keep, reverse, or revise the tested decision? ☐ Keep ☐ Reverse ☐ Revise ☐ Gather more evidence
Recommendation with two measures:
Next one-variable test:
Expansion stop rule: Do not recommend another bay merely because requests increased. Require repeated long diagnostic times and lost jobs after suggested staff coverage and parts flow are stable, positive contribution per job, enough cash for the bay and added labor, and acceptable quality, comeback, trust, and workload results.
My stop or proceed rule in one sentence:
Teacher guide and suggested answers
Recommended 50-minute route: 5 minutes to introduce the operating loop; 8 minutes to plan; 10 minutes for a baseline; 10 minutes for a one-variable test; 7 minutes for a repeat or calculation check; and 10 minutes for diagnosis and discussion. Equal simulated months matter more than equal clock time.
Short and accessible routes
15 minutes: provide one completed baseline and ask students to choose, predict, and explain one test.
30 minutes: run one baseline and one test; calculate contribution or margin; write a two-measure recommendation.
No-device route: give teams fictional baseline and test rows, including one event note, and ask them to diagnose the constraint.
Shared device: rotate operator, recorder, calculator, and skeptic roles; collect an individual exit sentence.
What strong answers notice
Contribution per job must cover payroll, rent, and marketing before creating profit.
More requests do not help when diagnostics, mechanics, parts, or bays limit completed work.
High-ticket complex work can lose value through labor, parts, delay, or comeback costs.
A bay adds physical capacity but also consumes cash and may require more staff.
Trust and reviews are outcome measures, not permission to ignore safety or truthful service.
Suggested diagnostic responses
Long diagnostics plus lost jobs: compare actual and suggested coverage before adding demand or a bay. Low parts inventory plus lower quality: test a supplier or stock action without changing promotion. Higher revenue plus lower profit: inspect parts, comeback, payroll, and marketing shares. Higher order value plus weaker trust: the premium or complex-work promise is unsupported. Stable quality but weak demand: a measured price or promotion test may be appropriate if capacity and cash are healthy.
Example calculation: if the average repair order is $220 and job-level parts, comeback, and utilities average $104, contribution is $116 per completed job. If daily payroll, rent, and marketing total $1,020, break-even volume is $1,020 ÷ $116 = 8.79, rounded up to 9 jobs per day. Students should still test whether nine jobs can be completed with acceptable diagnostics and quality.
Discussion prompt: Which result would make you stop a profitable strategy—rising comeback risk, falling trust, unsafe workload, depleted cash, or something else—and what evidence supports that boundary?
12-point assessment rubric
Criterion
2 points
1 point
0 points
Fair test
One main variable, useful controls, equal periods, and an event note.
Comparison is partly controlled.
Several unexplained changes.
Evidence record
Baseline, test, and repeat contain labeled operating and financial measures.
Some important evidence is missing.
Little usable evidence.
Calculation
Formula, numbers, units, and rounding are correct.
Reasonable method with a small error.
No usable calculation.
Diagnosis
Connects decision, constraint, customer or worker outcome, and profit/cash.
Names a pattern with limited connection.
Claim does not follow from evidence.
Recommendation
Uses at least two measures, acknowledges uncertainty, and proposes one next test.
Recommendation uses limited evidence.
Recommendation is unsupported.
Responsibility
Sets a clear safety, quality, trust, workload, legal, or cash guardrail.
Mentions a boundary without applying it.
Ignores important harms or limits.
Scoring note: award reasoning, not a particular profit result. A failed simulated test can produce excellent work when the student identifies why it failed and designs a safer next test.
Real-world safety, legal, and advertising boundaries
This simulator is a simplified learning model, not automotive repair, employment, financial, environmental, insurance, or legal advice. Real garages need qualified people, safe equipment and procedures, appropriate licenses and permits, lawful waste and hazardous-material handling, worker protections, accurate records, customer authorization, required disclosures, and compliance with local consumer-protection and repair rules.
Never trade real safety, inspection quality, required repairs, or honest diagnosis for simulated speed or margin.
Do not use deceptive urgency, invented faults, hidden fees, fake discounts, fake reviews, or unsupported warranty, price, safety, or environmental claims.
Protect customer and vehicle information. Do not enter real names, contact details, vehicle identifiers, payment data, or private records into a classroom activity.
Treat promotions as demand experiments, not proof that advertising caused every later result. Use voluntary, lawful measurement and never generate invalid ad traffic.
Compare modeled tradeoffs; do not present simulator output as a real repair estimate, staffing rule, safety standard, or forecast.
Auto Repair Simulator worksheet FAQ
What should students change in an auto repair experiment?
Change one major decision—pricing, repair mix, staffing style, parts supplier, quality investment, promotion, or bay count—while keeping the garage format and other important settings stable.
How do students calculate contribution per repair job?
Subtract parts cost, comeback cost, and other job-level variable costs from the average repair order. The result estimates how much one completed job contributes toward payroll, rent, marketing, and profit.
Does completing more repair jobs always improve profit?
No. More work can increase parts use, payroll pressure, diagnostic delay, rushed repairs, and comeback cost. Compare profit with quality, trust, reviews, inventory, lost jobs, and cash.
When should students recommend another repair bay?
Recommend a bay only after repeated evidence shows a properly staffed garage has long diagnostic times and loses jobs because physical capacity is full, while cash can support the bay and added staffing.
Can this worksheet be graded without rewarding profit alone?
Yes. Reward the fair test, calculations, connected evidence, uncertainty, responsible boundaries, and justified recommendation. A strong diagnosis can earn full credit even when the simulated garage loses money.