Abstract: A device may include one or more processors. The device may receive first information identifying a plurality of nodes and transactions associated with the plurality of nodes. The transactions may be between nodes, of the plurality of nodes, and entities of a plurality of entities. The device may determine geographical locations corresponding to the plurality of nodes. The device may determine second information, based on the first information, that may identify nodes, of the plurality of nodes, that are associated with shared entities. The device may generate, based on the geographical locations and the second information, a geographical network. The device may select a selected node, of the geographical network, on which to perform an action. The device may determine third information based on predicting future performance of the geographical network assuming that the action is performed. The device may store or provide the third information.

Abstract: A device may include one or more processors. The device may receive first information identifying a plurality of nodes and transactions associated with the plurality of nodes. The transactions may be between nodes, of the plurality of nodes, and entities of a plurality of entities. The device may determine geographical locations corresponding to the plurality of nodes. The device may determine second information, based on the first information, that may identify nodes, of the plurality of nodes, that are associated with shared entities. The device may generate, based on the geographical locations and the second information, a geographical network. The device may select a selected node, of the geographical network, on which to perform an action. The device may determine third information based on predicting future performance of the geographical network assuming that the action is performed. The device may store or provide the third information.

Abstract: A system uses elasticity modeling to enable cross category optimization of store display space for merchandise for any group of stores and items using point-of-sale data. A space elasticity curve is periodically created for each combination of performance metric, store cluster, planogram, and item segment, and is then scaled by individual store. A user can select any combination of stores and planograms, and the system then reconstructs the elasticity curves as individual “synthetic items,” which are averaged across the selected stores by creating average synthetic SKU records that reflect the estimated average store performance in each performance metric. The synthetic items are then used to either evaluate macro space performance across or within store areas, or to determine the best use of overall store display space available to permit management to optimally allocate display space for merchandise.

Abstract: The system includes a comparison unit that identifies products in an initial assortment of products, a desired assortment of products and the performance metric for each product. The comparison unit compares the initial assortment of products and the desired assortment of products to determine kept products, added product and deleted products. The system includes an interaction unit simulating the interaction of the kept products, the added products and the deleted products based on the performance metric. The system generates simulation results identifying the interactions between the kept products, the added products and the deleted products.

Abstract: The system includes a comparison unit that identifies products in an initial assortment of products, a desired assortment of products and the performance metric for each product. The comparison unit compares the initial assortment of products and the desired assortment of products to determine kept products, added product and deleted products. The system includes an interaction unit simulating the interaction of the kept products, the added products and the deleted products based on the performance metric. The system generates simulation results identifying the interactions between the kept products, the added products and the deleted products.

Abstract: A system uses elasticity modeling to enable cross category optimization of store display space for merchandise for any group of stores and items using point-of-sale data. A space elasticity curve is periodically created for each combination of performance metric, store cluster, planogram, and item segment, and is then scaled by individual store. A user can select any combination of stores and planograms, and the system then reconstructs the elasticity curves as individual “synthetic items,” which are averaged across the selected stores by creating average synthetic SKU records that reflect the estimated average store performance in each performance metric. The synthetic items are then used to either evaluate macro space performance across or within store areas, or to determine the best use of overall store display space available to permit management to optimally allocate display space for merchandise.