Abstract: This disclosure relates generally to a system and method for product demand transfer estimation in retail. Demand transfer happens across one or more products within a category of substitutable products. Product level demand transfer indicates the amount of demand that is going to be transferred from the unavailable product to another product within the category. An inventory availability is considered herein rather than assortment to ensure that the product is present or not for a particular point in time, The system is configured to estimate product level demand transfer value based on the attributes values present in the one or more products considered. Moreover, the embodiments herein further provide product level demand transfer using the attribute value and demand transfer of values of those attributes that are different between two products.

Abstract: Input data for each of a plurality of stores is obtained. The plurality of stores are clustered into one or more department-level clusters based on the input data corresponding to at least one department value demographic for calculating a plurality of department space elasticity values. The plurality of stores are clustered into a plurality of store-level clusters based on a store level demographic. Ranking, by a space optimization module corresponding to at least one department, for each department-level cluster, to obtain a set of optimal departments for the space planning and optimization using a rapid linearization algorithm. Ranking the plurality of stores, for each department-level cluster, to obtain a set of optimal stores for the space planning and optimization. Generating for each of the set of optimal stores, by processing information associated with the set of optimal departments and the set of optimal stores using a nonlinear space optimization mechanism.

Abstract: The present subject matter relates to systems and methods for assortment planning and optimization in a retail environment. In one implementation, a method for assortment planning and optimization is described. The method includes receiving assortment parameter data, and input information. The input information includes performance data, product data, fixture data and store data. Further, the method includes ranking product items based at least on the assortment parameter data and the input information. Furthermore, the method includes creating a listing of the product items based at least on the ranking. Such listing of the product items is processed based at least on predefined business rules, to generate one or more assortment solutions for providing optimal gross margins.