Abstract: In retail, macro space optimization is carried out at individual stores to allocate optimum space for each category. Each retailer has many stores and macro space optimization is experimented with different objectives such as expand space, reduce space and constant space of a store individually. Thus, the number of space recommendations analyzed at corporate level increases extremely high and making it difficult to bring key inferences out of these recommendations and creating challenges in implementation of results such as creation of planograms and floor plans. Embodiments of the present disclosure provide a method and system for identifying underlying patterns that reside in space recommendations across stores and creating drastically reduced number of floor plans and planograms in accordance with the identified set of patterns unlike large number of floor plans or planograms generated by state of the art space planning systems.

Abstract: Retailers face challenges in setting up an integrated system for hyper localization to optimize operations associated with hyper localization across value chain. Method and system for automation of assortment tool and integration of applications to realize hyper localization in retail value chain is disclosed. Localized complementary and competitive items are identified through suitable data format using canonical analysis at category level and used to automate rules in assortment tool. Quantification of hyper localization at various level such as item, category, store, and store cluster and updating central database with quantified measures are achieved to enable integration of applications across value chain to optimize operations associated with hyper localization.

Abstract: This disclosure relates to a system and method to optimize price of products with varying length of life cycle that are returned in online retailing. Sets of information pertaining to a SKU group is collected and integrated at an individual transaction level. The integrated data is processed to create an attribute repository matrix followed by attribute component matrix. In addition, time interval between introductory date of fashion apparel and its date of transaction is noted for each transaction. Effect of price and attribute components on time interval are trained using machine-learning model. Length of life cycle is estimated at an attribute component level. Price of a return product is optimized based on effect of price, its attribute components and their length of life cycle, presence of promotion, presence of number of new attribute values, product cost, additional cost involved in returning process and time of return.