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: Aspects of the invention include obtaining product hierarchy information for an early lifecycle product offered for sale by a retailer and obtaining order data for each order of the early lifecycle product during an early lifecycle period. The aspects also include obtaining customer data for a customer associated with each order of the early lifecycle product during the early lifecycle period and determining an expected return rate for the early lifecycle product based by inputting the product hierarchy information, the order data and the customer data into a trained return prediction model. Aspects also include performing an action based on a stored profile of the retailer based on a determination that the expected return rate exceeds a threshold value.

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.

Abstract: A simulator is configured to simulate the fulfillment of orders by nodes. Each node has an inventory of products and is capable of shipping the products to destinations in response to receipt of a corresponding order. The simulator divides the nodes into groups and assigns a different priority to each group based on input provided by a user to the simulator to generate an ordered sequence of priorities. The simulator maintains safety stock data corresponding to each node that indicates minimum quantities of the products required to be present at the corresponding node. The simulator selects a current priority of the sequence and next simulates a first group among the groups having the current priority fulfilling the orders for a given product among the products while a quantity of the given product at each of the nodes in the first group is below the minimum quantity in the corresponding safety stock data.

Abstract: A simulator is configured to simulate the fulfillment of orders by nodes. Each node has an inventory of products and is capable of shipping the products to destinations in response to receipt of a corresponding order. The simulator divides the nodes into groups and assigns a different priority to each group based on input provided by a user to the simulator to generate an ordered sequence of priorities. The simulator maintains safety stock data corresponding to each node that indicates minimum quantities of the products required to be present at the corresponding node. The simulator selects a current priority of the sequence and next simulates a first group among the groups having the current priority fulfilling the orders for a given product among the products while a quantity of the given product at each of the nodes in the first group is below the minimum quantity in the corresponding safety stock data.

Abstract: A simulator is configured to simulate the fulfillment of orders by nodes. Each node has an inventory of products and is capable of shipping the products to destinations in response to receipt of a corresponding order. The simulator divides the nodes into groups and assigns a different priority to each group based on input provided by a user to the simulator to generate an ordered sequence of priorities. The simulator maintains safety stock data corresponding to each node that indicates minimum quantities of the products required to be present at the corresponding node. The simulator selects a current priority of the sequence and next simulates a first group among the groups having the current priority fulfilling the orders for a given product among the products while a quantity of the given product at each of the nodes in the first group is below the minimum quantity in the corresponding safety stock data.

Abstract: A simulator is configured to simulate the fulfillment of orders by nodes. Each node has an inventory of products and is capable of shipping the products to destinations in response to receipt of a corresponding order. The simulator divides the nodes into groups and assigns a different priority to each group based on input provided by a user to the simulator to generate an ordered sequence of priorities. The simulator maintains safety stock data corresponding to each node that indicates minimum quantities of the products required to be present at the corresponding node. The simulator selects a current priority of the sequence and next simulates a first group among the groups having the current priority fulfilling the orders for a given product among the products while a quantity of the given product at each of the nodes in the first group is below the minimum quantity in the corresponding safety stock data.

Abstract: A method, apparatus, and computer implemented instructions for processing a form in a data processing system. A markup language form is received in which the markup language form includes first data input by a user and second data in a hidden field identifying how the data is to be processed. A process is identified to process the first data using the second data to form an identified process. The first data is processed using the identified process.