The stockout students see now may have begun with an order they did not place two Rounds ago. A supply chain simulation classroom lesson makes that delay visible. Students order before demand is known, track the pipeline, produce under constraints, and discover why resilience is a tradeoff among cost, service, buffers, diversification, and sourcing evidence rather than an instruction to "buy more inventory."
Build the lesson around the flow of one unit from purchase order to pipeline, receipt, production, finished inventory, sale, or ending stock. Once students can trace that flow, disruptions and sustainability measures have an accounting and operational context.
Start with the material-flow timeline
Draw a timeline before the first decision. If a supplier has a two-Round lead time, an order paid in Round 1 normally arrives in Round 3. It cannot solve a Round 1 or Round 2 shortage. A one-Round supplier offers more responsiveness, usually at a different purchase cost or risk profile.
Ask students to annotate four dates for each order: placed, paid, expected arrival, and usable in production. If the simulation treats an order as a prepaid pipeline asset, state that explicitly. Students often assume cash leaves only when goods arrive or that ordered units are already inventory on hand.
Use consistent terms.
- On-hand raw inventory means received material available for production.
- Pipeline inventory means paid or committed material not yet received.
- Finished inventory means completed product available for sale.
- Service level is the proportion of demand served, using the scenario’s defined measure.
The timeline is the foundation for every later debrief.
Scroll sideways inside the graph and table to see all the data. You can also focus it and use the left and right arrow keys.
In this fictional example, one raw unit makes one finished unit. A 120-unit order placed and paid in Round 1 arrives before production in Round 3. Starting raw stock is 40 units. Assume no other receipts, no capacity limit, no finished stock carried between Rounds, and lost demand rather than backorders. These are teaching assumptions, not a simulation result.
| Round | Opening raw stock | Receipts | Demand | Produced and sold | Closing raw stock |
|---|---|---|---|---|---|
| 1 | 40 | 0 | 30 | 30 | 10 |
| 2 | 10 | 0 | 30 | 10 | 0 |
| 3 | 0 | 120 | 40 | 40 | 80 |
Each row follows opening stock plus receipts minus production equals closing stock. Service is 100% in Round 1, 33.3% in Round 2, and 100% in Round 3. Buying more in Round 2 cannot repair that Round when every available supplier takes at least one Round to deliver.
Treat forecasts as decisions, not answers
A demand forecast should record what the team expects. It should not change customer demand. Tell students which outcome the forecast will be compared with, ideally unconstrained demand rather than units sold. A forecast can appear accurate when a stockout caps sales, so the measurement definition matters.
Require three numbers from each team: point forecast, downside case, and upside case. Then ask what operational plan corresponds to each. The production target may match the point forecast, while pipeline coverage and safety stock reflect the range and service objective.
After the Round, compare forecast error with service and inventory. A team can forecast accurately and still fail to serve demand because it ordered too late. Another can maintain service with a weak forecast by carrying an expensive buffer. The lesson is the connection among forecasting, lead time, and policy.
Compare purchase price with total landed cost
The lowest quoted supplier price is not necessarily the lowest-cost choice after logistics, delays, quality, inventory, lost demand, and policy charges. Ask teams to construct a landed-cost ledger using only measures the scenario provides. Do not invent values for reputational or sustainability effects that are not modeled.
A practical comparison can include the following.
| Evidence | Low-price supplier | Responsive supplier | Certified supplier |
|---|---|---|---|
| Purchase cost | |||
| Normal lead time | |||
| Units delayed or cancelled | |||
| Service consequence | |||
| Certified share and emissions evidence |
Ask students to distinguish direct cost from operational consequence. Lost demand may be visible as a service gap. It should not be converted into a fabricated dollar amount unless the model supplies one.
Teach buffers and diversification as conditional choices
Inventory, spare capacity, and supplier diversification can protect service, but each consumes cash or increases cost. Resilience is not maximized by buying from every supplier in every Round. It is improved when the chosen buffer addresses a defined exposure at an acceptable tradeoff.
Before a disruption, ask each team to name its largest exposure, whether that is long global lead time, supplier concentration, insufficient raw stock, capacity, or demand uncertainty. It must then choose one mitigation and one measure that will show whether it worked.
After the event, compare teams that diversified with teams that concentrated. Did diversification protect service? What purchase or holding cost accompanied it? Was the disrupted supplier actually due to deliver in that Round? This last question prevents students from crediting a strategy for avoiding an exposure it never had.
Use conservation checks to strengthen operational reasoning
Students can audit a supply chain with simple identities.
Ending raw inventory = Beginning raw inventory + receipts – raw material used
Ending finished inventory = Beginning finished inventory + production – units sold
Adjust the equations only when the scenario explicitly includes spoilage, cancellation, or another flow. Ask teams to reconcile one Round. The calculation catches confusion between orders and receipts, production targets and actual output, or demand and sales.
If costs use first-in, first-out accounting, connect the physical layers to cost of goods sold. A later high-cost receipt does not necessarily determine the cost of units sold today when older inventory leaves first.
Run the lesson through four phases
Phase 1: Forecast and lead-time baseline
Teams forecast demand, set production, and place supplier orders. Debrief which orders can affect which future Rounds. Establish the normal service and inventory pattern before introducing disruption.
Phase 2: Constraints and capacity
Ask whether a current service problem comes from missing raw material, roasting capacity, finished inventory, or a late prior decision. When capacity investment becomes available, require a cash and utilization case.
Phase 3: Diversification and landed cost
Teams compare supplier mixes using purchase price, lead time, receipts, delays, inventory, and lost demand. A disclosed disruption can test whether the policy addressed the actual exposure.
Phase 4: Resilience and sustainability evidence
Students recommend a future sourcing policy using service, concentration, certified share, emissions, and Shareholder Value. Keep normative judgments explicit. The model provides evidence, while the recommendation depends on the stated objective and tradeoffs.
Debrief the delay between cause and effect
Choose one Round with a service failure and trace backward. Which production constraint bound? Which receipt was missing? When was the corresponding order placed? What information was available then? This "causal clock" stops students from blaming the most recent decision automatically.
Use these questions.
- Which prior order protected current service?
- Was the forecast wrong, or was execution constrained?
- Did the lowest purchase cost produce the lowest landed cost?
- What did diversification cost, and which exposure did it reduce?
- Which measure supports the recommended supplier mix?
INFORMS notes that simple games can quickly illustrate focused supply chain concepts, including inventory, contracting, bidding, trust, and collaboration. Preserve that focus even in a richer simulation. Choose one flow or policy to analyze deeply.
Assess a sourcing recommendation
Ask teams to submit a policy memo that includes a demand assumption, supplier mix, pipeline-coverage rule, service target, and one contingency. Require a material-flow reconciliation and a table with at least three evidence measures. Students should identify one tradeoff and one limitation.
Grade whether the policy fits the lead times and evidence. Do not reward the highest inventory, lowest purchase price, or highest service in isolation. Each can be produced by an expensive or fragile plan.
Use Coffee Roaster as the operating context
Supply Chain Resilience: Coffee Roaster is an eight-Round ClassTycoon Pro scenario for Introductory Operations Management, designed for about 100 minutes. Team Companies set price, forecast demand, plan production, order from suppliers with one- or two-Round lead times, and later invest in capacity.
The scenario offers three immutable paths: Stable Planning, Logistics Resilience, and Climate Transition. Depending on the selected path, disclosed events can affect arrival timing, quantities, demand, certified preference, or carbon cost. The simulation exposes pipeline, supplier, service, capacity, certification, emissions, and financial evidence.
Coffee Roaster is available now. Real instructor and student pilot validation remains to be completed. Use the simulation library to inspect the decisions and evidence before you adopt, and review current access on Pricing.
Frequently asked questions
Is more inventory always more resilient?
No. Inventory can buffer delay or demand variation, but it ties up cash and may add holding cost. Evaluate whether the buffer addresses a defined exposure and improves service enough to justify the tradeoff.
Should students see disruptions in advance?
Use the scenario contract. Disclosed advance signals let students plan. Undisclosed randomness teaches a different lesson. Tell students what information is available and avoid changing event rules mid-session.
How do I distinguish a forecast error from a service failure?
Compare the forecast with unconstrained demand, then compare demand with units served. A team may forecast well but lack the materials, capacity, or finished inventory to fulfill demand.