Expose the demand accumulated during a delivery delay

Keep the uncertain event visible

MIT’s supply-chain notes explain that transit-time variability affects inventory requirements.

Identify the pending receipt and the decision that depends on it. Separate a supplier’s current commitment, an internal estimate and a planning assumption. These may be useful for different purposes, but they should not become interchangeable labels in a spreadsheet. Ask the responsible owner which evidence supports each possible arrival date.

Choose a small set of scenarios that captures the actual unresolved question. A low, central and high label is not enough if nobody can explain the dates behind it. Retain a scenario even when it is inconvenient for the launch plan, provided the owner considers it plausible. Conversely, do not invent extreme cases merely to make an ordinary decision appear alarming.

Calculate the consequence before averaging

Fictional demand is 10 units daily. Arrival after 2 or 6 days means 20 or 60 units requested. Starting with 35 leaves 15 or a shortage of 25. Equal scenario weights give average demand 40; they do not erase the two outcomes. Expected shortage is (0 + 25)/2 = 12.5, not the five-unit shortfall obtained from average demand.

Place the alternatives on a dated timeline with the assumed demand pattern. Explain where the starting quantity came from and whether other commitments have already been accounted for. Keep the scenario inputs fixed while comparing the outcomes; changing the customer scope halfway through can make an unfavorable case appear to improve without any operational remedy.

The equal weights above are stipulated for illustration, not estimated from a real supplier history. In an actual decision, ask what supports the proposed weights and show the consequences if they remain unresolved. The owner can compare conditional outcomes without pretending to know their probabilities. Do not silently turn a plausible date into a statistically calibrated forecast.

Test which assumption drives the decision

Hold the receipt alternatives constant and ask whether an expected promotion changes the demand pattern. Then hold the demand assumption constant and examine the receipt evidence. This disciplined comparison helps the team identify which uncertainty matters most instead of changing every input simultaneously and attributing the result to whichever explanation is convenient.

Check whether the assumptions could move together. A busy season might affect both customer activity and supplier performance. If the team lacks evidence about that relationship, state the limitation rather than multiplying marginal probabilities automatically. This lesson does not fit a joint distribution or endorse a convenient independence assumption.

Ask what additional evidence would actually change the pending decision. A confirmed release event, a verified customer commitment or an approved revised campaign scope may be more useful than another generic status update. Assign the evidence request to the existing owner; do not contact a supplier or customer without the required authorization.

Choose a response that survives the relevant case

Compare authorized response proposals against the scenario table. The options might include a later campaign, a narrower offer or a separately reviewed replenishment alternative. Describe what each proposal changes and what remains exposed. Do not label an option safe merely because it works in the central case.

Keep the financial and customer consequences visible. Delaying a campaign can have a cost, while proceeding with an unsupported promise can create a different cost. The planner’s arithmetic informs this tradeoff but does not supply the organization’s risk preference or purchasing authority. Record who accepts the remaining uncertainty.

Close with a specific decision and a reopening trigger. If the relevant receipt evidence changes, recompute the affected scenarios rather than replacing the whole discussion with a reassuring update. Preserve the original assumptions so the post-event review can distinguish an inaccurate forecast from a reasonable decision made under uncertainty. The purpose is a transparent choice, not an artificial claim that the team could have predicted every delay.

Make the reasoning concrete.

A fictional publisher plans a stationery launch around an unconfirmed receipt. The campaign owner prefers one average arrival estimate, while the operations owner wants the two plausible cases kept visible.

The publisher records the conditional response and its owner. No shipment, purchase or customer promise is changed by the exercise.

Take the questions into your next brief.

Use these prompts in your own notes. Nothing is submitted from this page.

1. Event

   Which uncertain receipt matters to the decision?

2. Evidence

   What supports each proposed arrival case?

3. Assumption

   Which demand pattern is being held fixed?

4. Consequence

   What operational exposure does each case reveal?

5. Trigger

   What evidence would change the selected response?

Watch for the wrong conclusion.

• Treating a convenient average as the only possible operating state.
• Assigning probabilities without evidence.
• Changing several assumptions without showing which one changed the decision.

Sources & scope

Original operational reasoning and explicitly fictional numerical fixtures. Source-derived definitions and mathematical applications remain attributed. Qualified owners decide real policy and purchasing. No account changes, purchase, stock movement or deployment occurs.

• MIT CTL: Supply Chain Fundamentals Key Concepts, Winter 2020 →Teaching material · Supports: Supports lead-time variability relevance.