Demand Propagation

Objective

After completing this lesson, you will be able to understand the premises behind demand propagation.

Some History: The Bullwhip or Forrester Effect (1960s)

Forecast uncertainty at the customer demand stream is propagated throughout the supply chain:

Forecasts at customer demand streams are provided as input.
Forecast error CV and hence forecast error standard deviation is calculated from historical data at weekly level.
Forecast (order) uncertainty is propagated from customer demand streams throughout the supply chain.
Rolling (time-varying), based on order cycles (PBR), so bullwhip effect is not created by each stage using its own forecast.
Customer forecasts are propagated throughout the supply chain by calculating net requirements at each stage.

Forecast error propagation considers all complexities of real-world supply chains without making simplifying assumptions.

Cycle time/periods between review.
Reduced forecast error propagated during ramp-down demand.
At hybrid nodes, forecast error CV exists for external demand, while internal demand forecast error is propagated from downstream.
Non-stocking nodes are flow-through nodes.
For bills of materials, forecast error is propagated to each component.

For end nodes:
- The CV Calculation operator reads the forecast and sales signals, typically in weeks. However, SAP IBP for inventory is very flexible, and allows any bucket length including lead time.
- A multistage inventory optimization operator reads the standard deviation and the forecast in weeks to calculate the standard deviation as the multiplication of CV by the forecast.
For network:
Demand variability propagation as described earlier.
Safety stock for every node:
The exposure within the standard deviation is equal to the lead time plus the periods between replenishment.