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Learn through experiments

How a short surge becomes repeated swings

Can correcting a stock gap too aggressively amplify orders?

Entering midway? Revisit Count stock that has not arrived yet for the preceding worked example, or start here and use the explanation below.

1 · Try

Start with a concrete question

Original synthetic scenario. A supplier fills orders after a delay. A store targets 20 items, normally sells six per day, and faces one demand shock on day four.

Try the initial inputs, then write a prediction before changing one setting. All calculations run locally on your CPU.

Go directly to the experiment · Review basic mathematics

2 · Predict

What do you expect?

Will doubling correction gain make ordering settle faster at a two-day delay?

3 · Experiment

Change one thing. Inspect what happens.

  1. Set the surge to zero and confirm the steady flow.
  2. Restore a six-item surge; inspect how deliveries lag orders.
  3. Compare gains 0.5, 1 and 2 while keeping delay fixed.
  4. Explain one unintended consequence of targeting a stock number without observing the pipeline.
Experiment inputs
Inputs
Your settingsMechanism
Calculation belowEvidence
Compare outcomes
Largest order12 items
Smallest order0 items
Ending stock20 items
Unfilled demand0 items

Compare the current results

Every bar starts at zero and uses the same numeric scale in this chart. The upper bound automatically adjusts to the largest current value (at least 1), so compare the numbers when changing inputs. Units and quantities come from each label; different kinds of quantity are not interchangeable. Displayed values are rounded to at most six significant digits.
012
  • Day 1 order (items)6
    Day 1 order (items): 6Horizontal bar from zero to 6, on the shared zero to 12 numeric axis.
  • Day 2 order (items)6
    Day 2 order (items): 6Horizontal bar from zero to 6, on the shared zero to 12 numeric axis.
  • Day 3 order (items)6
    Day 3 order (items): 6Horizontal bar from zero to 6, on the shared zero to 12 numeric axis.
  • Day 4 order (items)12
    Day 4 order (items): 12Horizontal bar from zero to 12, on the shared zero to 12 numeric axis.
  • Day 5 order (items)12
    Day 5 order (items): 12Horizontal bar from zero to 12, on the shared zero to 12 numeric axis.
  • Day 6 order (items)6
    Day 6 order (items): 6Horizontal bar from zero to 6, on the shared zero to 12 numeric axis.
  • Day 7 order (items)0
    Day 7 order (items): 0Horizontal bar from zero to 0, on the shared zero to 12 numeric axis.
  • Day 8 order (items)0
    Day 8 order (items): 0Horizontal bar from zero to 0, on the shared zero to 12 numeric axis.
  • Day 9 order (items)6
    Day 9 order (items): 6Horizontal bar from zero to 6, on the shared zero to 12 numeric axis.
  • Day 10 order (items)12
    Day 10 order (items): 12Horizontal bar from zero to 12, on the shared zero to 12 numeric axis.
  • Day 11 order (items)12
    Day 11 order (items): 12Horizontal bar from zero to 12, on the shared zero to 12 numeric axis.
  • Day 12 order (items)6
    Day 12 order (items): 6Horizontal bar from zero to 6, on the shared zero to 12 numeric axis.

Follow the calculation

  1. Day 1: delivery 6.000, demand 6, stock after service 20.000; order = clamp(6 + 1 × (20 − 20.000), 0, 30) = 6.000.
  2. Day 2: delivery 6.000, demand 6, stock after service 20.000; order = clamp(6 + 1 × (20 − 20.000), 0, 30) = 6.000.
  3. Day 3: delivery 6.000, demand 6, stock after service 20.000; order = clamp(6 + 1 × (20 − 20.000), 0, 30) = 6.000.
  4. Day 4: delivery 6.000, demand 12, stock after service 14.000; order = clamp(6 + 1 × (20 − 14.000), 0, 30) = 12.000.
  5. Day 5: delivery 6.000, demand 6, stock after service 14.000; order = clamp(6 + 1 × (20 − 14.000), 0, 30) = 12.000.
  6. Day 6: delivery 12.000, demand 6, stock after service 20.000; order = clamp(6 + 1 × (20 − 20.000), 0, 30) = 6.000.
  7. Day 7: delivery 12.000, demand 6, stock after service 26.000; order = clamp(6 + 1 × (20 − 26.000), 0, 30) = 0.000.
  8. Day 8: delivery 6.000, demand 6, stock after service 26.000; order = clamp(6 + 1 × (20 − 26.000), 0, 30) = 0.000.
  9. Day 9: delivery 0.000, demand 6, stock after service 20.000; order = clamp(6 + 1 × (20 − 20.000), 0, 30) = 6.000.
  10. Day 10: delivery 0.000, demand 6, stock after service 14.000; order = clamp(6 + 1 × (20 − 14.000), 0, 30) = 12.000.
  11. Day 11: delivery 6.000, demand 6, stock after service 14.000; order = clamp(6 + 1 × (20 − 14.000), 0, 30) = 12.000.
  12. Day 12: delivery 12.000, demand 6, stock after service 20.000; order = clamp(6 + 1 × (20 − 20.000), 0, 30) = 6.000.

A delayed response can make replenishment orders swing after a short demand shock. Strong corrections can amplify variation instead of settling it.

Captures keep your inputs, prediction and observation. Results are recalculated from those inputs. Resetting inputs keeps your captures and challenge history.

4 · Understand

Explain the mechanism

What the experiment explains

Original synthetic scenario. A supplier fills orders after a delay. A store targets 20 items, normally sells six per day, and faces one demand shock on day four.

A feedback policy reacts to a stock gap. Delayed deliveries mean earlier corrections may still be traveling when the next correction is calculated.

With no surge, deliveries and demand both equal six and stock stays 20. With a six-item day-four surge, stock falls to 14 and gain one orders 6 + (20−14) = 12.

Subsequent corrections can keep responding before those extra items arrive, then overshoot the target. The policy intentionally ignores pipeline stock so the failure is inspectable.

Read the relationship

Order = clamp(normal demand + gain × (target − shelf stock), 0, 30). Delivery today equals the order placed lead-time days earlier.

Inspect the evidence

The numerical trace is recalculated from your current settings. Compare a controlled pair of trials before changing several inputs together. Inspect intermediate quantities as well as final metrics; state which assumptions make the comparison meaningful.

Boundaries of the model

  • Fixed delay, a single demand shock and fractional item flows allowed.
  • This is a deliberately limited policy, not an optimized supply-chain controller.

Return to the systems thinking and operations course and project to connect this mechanism to the broader investigation.

5 · Apply

Try a fresh case

Normal demand 5, target stock 15, actual stock 9, gain 0.5. With an upper order limit of 30, what order is placed?

This is a public practice check. Retries support learning; matching a number does not certify independent proficiency.

Take the investigation further

Choose any extension. These are project prompts for you to carry out; they are not automatically assessed.

School investigation

Mark which decisions use old information in a stock-flow diagram.

College investigation

Implement a pipeline-aware policy and compare order variance under identical shocks.

Workplace investigation

Propose an intervention that shares demand and pending-order information rather than only raising local safety stock.

6 · Continue

Keep the evidence, choose your next step

Completion records your own decision that you have worked through this investigation. Practice checks and independent project evidence are separate.