Data on all products moving across the entire retail network is stored and analysed
a = Finished/produced products are shipped from warehouses to retail stores b = at the end of the season remaining of that season is moved to web channel; also products that do not sell as expected can be moved to web. c = products designed for the previous season that at the end of the current season have still not been sold by web are moved to factory stores. d = sales to the customer e = web customer return f = retail store return of a retail product (it could be kept by the returned store or sent to another retail store or to the web) g = retail store return of a factory product h = factory return of a factory product or older retail product i = factory return of a recent retail product (it is shipped to a “sister” retail store) m = products that do not sell as expected or more in general products that BB doesn’t want to keep anymore on retail floors can be moved to factory stores
The tool analyses all collected data for decision making improvement and constructs dynamic and static reports. Some key features include: easy and fast slicing and dicing of all Dimensions (entities) and Measures (KPI, Statistic) thanks to the OLAP Cube technology; Possibility to configure almost all Measures like quantity sold in a given non-standard time period etc; creating a dynamic dashboard related to the data analysis.
Flexible data inputs that the user can construct easily depending on the analysis that he wants to perform. (e.g. all Classes, all Classes by a time period, some Classes by time period by Country, etc); Cross Price Elasticity Computation; Five different types of functions are used to fit qty/price relation with the best one in terms of fitting being chosen; Exploits non-linear relations between price and qty; Is based on empirical data with no strong assumption made.
The tool employs a sophisticated statistical algorithm which estimates a baseline representing the quantity/dollar volume sold with no promotions taking place whilst taking into consideration promotional periods, changes in average price, seasonality and weather conditions.
The user can easily choose the entities (using a BI-like entry view) with the possibility of pre-clustering a list based on some qualitative attributes in order to then perform a quantitative clustering process.
The user can construct the metrics on which the analysis will run (e.g. what are the best/worst sellers in this season w.r.t. qty sold, margin and average inventory stock?). The user can choose dynamically the group of colorway or class or group department on which he wants to perform the analysis and based on which he wants to create the benchmarks. Moreover, the user can choose what are the inputs/outputs that he wants to analyze (e.g. output: margin and qty sold; input: dollar average inventory, location costs, traffic).
The User can choose the inputs/outputs that he wants to analyse (e.g. output: margin and qty sold; input: dollar average inventory, location costs, traffic). The result will be produced in the form of a DEA score, where 1 is the maximum relative efficiency and 0 is the minimum.