SIP Premium content

Node performance

Node performance is one of the six fulfillment objectives that the Optimization service considers when optimizing orders. When this optimization objective is prioritized, the Optimization service makes fulfillment decisions by prioritizing the node with low cancellation rates and reschedule rates for order fulfillment.

As a fulfillment manager, you aim to get a real-time sales picture at each node. However, a sales capture doesn’t always indicate timely fulfillment of each order. When a shopper places an order, the fulfillment of the order might get affected for reasons such as lost or misplaced items, shopper-initiated cancellation, unavailability of an item at the sourcing node, and back ordered items.

In such cases, the fulfillment manager needs to optimize the order fulfillment from the high-performing nodes. The node performance is monitored by the success rate of order fulfillment at the specific node. However, in real-time fulfillment networks, the node performance reduces due to order cancellation and rescheduling. IBM® Sterling Intelligent Promising helps a fulfillment manager to predict node cancellation rates and node reschedule rates by using the AI-powered node risk model.

With the help of the node performance feature, Sterling Intelligent Promising helps the fulfillment manager to understand various aspects of node performance. The fulfillment manager can use the Node performance feature to gain the following node level insights and optimize the order fulfillment.
  • Operational insufficiency
  • Insufficient capacity
  • Backlog at the node
  • Average backlog days
  • Historical fulfillment rate
  • Item backorder

Sterling Intelligent Promising uses a node risk model to gain insights into historical fulfillment rates on order fulfillment. By using the insights, a fulfillment manager can predict and minimize risk of cancellations during fulfillment optimization. For more information, see Node risk model.

Sterling Intelligent Promising then converts the predicted rates into costs with the help of the node risk model. The costs are further considered for order optimization along with other cost factors. Each cost factor is assigned with a certain objective optimization weight according to the optimization profile. The fulfillment managers can adjust the objective optimization weight according to the business objectives. The fulfillment managers can also set rules and conditions for the optimization profile so that appropriate regions and channels can be selected for order fulfillment.

For more information about monitoring node performance in a real-time scenario, see Scenario: Monitoring node performance.