Avoiding stockouts
Avoiding stockouts is one of the five fulfillment objectives that Fulfillment Optimizer considers when optimizing orders. When this objective is prioritized, fulfillment decisions are based primarily on preventing stockouts at nodes while still meeting customer expectations. Fulfillment Optimizer analyzes the possibility of a potential future stockout at each node and favors options with the lowest probability of a future stockout to fulfill the order.
Overview
- In-store and online sales
- Future replenishments
- Markdown plans
- Inventory availability
The inventory model analyzes the possibility of a stockout at a node, balances the inventory of a SKU at the network level, and avoids stockouts by shipping from alternative nodes. This model is based on the probability and predicted demand of an item. It is calculated for the SKU-ship node combination by using a proprietary algorithm based on feeds of inventory, markdown, TLOG, order, replenishment, and SKU data. The system uses demand predictions to calculate the stockout avoidance cost at the SKU-ship node level. The stockout avoidance cost represents the estimated amount of money that would be lost due to the predicted stockout.
- Learns sell-through patterns by using inventory and sales data to assess the risk of a stockout.
- Assigns a value to the risk of stockout based on the probability of in-store and e-commerce sales during stockout and replenishment schedules.
- Sources SKUs to maintain the safety stock level.
The following example demonstrates how the inventory model takes a potential stockout at a node into consideration and ultimately optimizes the order to avoid a stockout at that node. When a sale occurs at this node in the future, the benefit is claimed as the stockout avoidance benefit, which is then reported in Benefits report.
Example
In a scenario where an order can be shipped from multiple nodes and the Avoiding stockouts objective is prioritized, Fulfillment Optimizer considers a potential stockout to select a ship node. It then optimizes the order so that the initial investment results in larger savings from stockout avoidance.
For example, Fulfillment Optimizer might ship an order from a node that is further away from a destination to avoid a stockout at a node that is closer to the destination. As a result, you can reinvest part of your stockout avoidance benefit toward the additional shipping cost. If the store that avoided the stockout sells that item to a walk-in customer, the benefit that is obtained by avoiding the stockout might be higher than the initial shipping cost.
| Shipping node | Distance between node and destination | Shipping cost | Processing cost | Load-balancing cost | Distance penalty | Stockout avoidance cost | Markdown avoidance cost | Total cost of optimization with inventory model | Total cost of optimization without inventory model |
|---|---|---|---|---|---|---|---|---|---|
| Node A | 759 miles | $4.93 | $3.50 | $.007 | $.0023 | $11.18 | $0 | $19.6186 | $8.4392 |
| Node B | 352 miles | $4.44 | $3.25 | $.003 | $.0012 | $77.41 | $0 | $85.1012 | $7.6946 |
- Total cost of optimization with inventory model = Shipping cost + Processing cost + Load-balancing cost + Distance penalty + Stockout avoidance cost - Markdown avoidance cost
- Total cost of optimization without inventory model = Shipping cost + Processing cost + Load-balancing cost + Distance penalty
If the inventory model was not used in this example, node B would likely be selected to fulfill the order since it is closer to the destination. However, the inventory model predicts that if the order is shipped from node B, a high possibility of a stockout at node B exists in the future. The predicted loss due to stockout at node B is $77.41 compared to $11.18 at node A. To minimize the loss due to stockout at node B, node A is selected for shipping. As a result, the total cost of optimization at node A is $19.6186 compared to $85.1012 at node B.