Abstract: Embodiments herein describe a return network simulation system that can simulate changes in a retailer's return network to determine the impact of those changes. Advantageously, being able to accurately simulate the retailer's return network means changes can be evaluated without first making those adjustments in the physical return network. Doing so avoids the cost of implementing the changes on the return network without first being able to predict whether the changes will have a net positive result (e.g., a positive result that offsets any negative results). A retailer can first simulate the change on the return network, review how the change affects one or more KPIs, and then decide whether to implement the change in the actual return network. As a result, the retailer has a reliable indicator whether the changes will result in a desired effect.

Abstract: The embodiments herein provide techniques for selecting an optimal return location from a plurality of candidate return locations for returning an item based on an expected recovery associated with each location. As discussed above, using predesignated return location(s) ignores many factors that can increase costs that affect returning items such as shipping costs, inventory, handling costs, operational transfer costs, as well as several predicted costs. Further, these techniques do not consider expected future revenue (which can offset these costs). In one embodiment, a net expected recovery is determined for each location using the costs and future revenues discussed above. By comparing the net expected recovery associated with each candidate return location, the optimal return location can be identified.