BACKGROUND

In large e-commerce environments, properly planning fulfillment capacity, such as by stocking and staffing fulfillment centers (FCs), contributes to efficient operations. Understaffing risks frustrating customers with missed delivery dates (e.g., breaking service level agreements, SLAs), while overstaffing risks inefficient use of budget and sub-optimal resource utilization. Although forecasting tools exist, which often use historical data, customer purchasing behavior largely depends on events such as holidays, sales promotions, weather incidents, and other events. Multiple factors may complicate planning, such as demand spikes during peak periods (holiday seasons), in which the daily demand may increase sharply. Additionally, for e-commerce applications, different delivery date promises (e.g., SLAs) may co-exist within the daily demand forecast, resulting in differences for fulfillment deadlines in demand fragments (e.g., different customer orders) received on the same day.

Common resource optimization techniques, which seek to efficiently allocate resources to demand, require solving a multi-dimensional problem as a simultaneous solution. In large e-commerce environments, such techniques may rapidly become intractable, due to the high number of variables and constraints.

SUMMARY

The disclosed examples are described in detail below with reference to the accompanying drawing figures listed below. The following summary is provided to illustrate some examples disclosed herein. It is not meant, however, to limit all examples to any particular configuration or sequence of operations.

A solution for capacity planning includes: for each of a plurality of fulfillment centers (FCs), receiving an inventory allocation; receiving a demand forecast and delivery time information for customer orders; performing a simulation comprising: partitioning a simulation period into multiple simulation stages; for each simulation stage: assigning portions of the demand forecast to a demand pool for one of the FCs; for each FC, determining a backlog, based at least on the demand pool for the FC and a simulation stage fulfillment capacity for the FC; and transferring each existing backlog to a subsequent simulation stage or an alternate FC; and based at least on the simulation, generating at least one planning control logic action, for example, adjusting delivery time options available on an e-commerce node, identifying a change in staffing levels at one or more FCs, adjusting inventory allocation for at least one FC, or another action identified herein.

