Identifying the impact of time-varying demand

Objective

After completing this lesson, you will be able to identify the impact of time-varying demand.

Example

The example in the following figure shows a scenario with period between review (PBR) = 2, lead time (LT) = 1, non-stock out probability (NSP) = 95%, and a demand, as indicated in the figure.

Note

Constraints only make sense in a time-varying system, for example, in seasonal businesses (candy at Halloween), where production capacity is fixed, but peak periods of demand are experienced. This is currently handled by pre-build and pre-build planning.

The figure describes the Demand and PBR.

The initial on-hand value is 257 units and the target inventory position (TIP) is calculated, as indicated in the figure.

The figure describes the Safety Stock and Target Inventory.

The values for the subsequent period are calculated, as indicated in the figure.

The figure describes the Inventory Position.

The target safety stock for the third period is calculated, as shown in the figure.

The figure describes the Target Inventory Position.

Finally, the values for the subsequent periods number 4 and 5 are determined, as shown in the figure.

This figure shows the result for this example in both diagram and in tabular format.

Time-varying Safety Stock

Time-varying safety stock creates the opportunity for:

Less safety stock when risk is low
Greater safety stock to buffer greater uncertainty
Safety stock is time-phased, to protect against uncertainty when needed, and reduced when safety stock is less likely to be used
Safety stock is ordered when needed, by later lead time and by the execution system, based on targets created by SAP IBP for inventory

Demand: 200, 0, 100, 200, 100
Period between reviews (PBR) equals two and lead time equals one
Initial on-hand value equals 257 units
95% non-stock out probability (NSP)

Inventory Planning Fundamentals

A simple single-stage inventory calculation can become complex, even without considering multi-stage, supply uncertainty and batch sizes. Cycle stock is driven by replenishment frequency (PBR) or batch size. Pipeline stock is driven by length of lead time. Safety stock is dependent on uncertainty through the exposure period. Use of gamma distribution allows for skewed distributions to be correctly modeled.

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