Planning

This module enable merchandisers and planners to optimize inventory mix at stores level, with the computational power to go up to a granularity of design-level attributes of styles, identify the top selling and consistent selling styles.

Often heavy discounts are given on products to bump up sales and liquidate excess inventory. Such sales should not be considered for forecasting, as it distorts real demand. This module used a concept of sales data clean up to clean up / ignore such sales for better analysis of data. Also, having right product sizes at stores is important for good sales. This module also finds out which sizes are not selling well at stores and cleans up that sales data.

This module also groups the Styles according to the attributes so that each Attribute Group contains styles that are similar to each other but are different from the ones in other groups.

Reasons for Grouping:

• In a highly dynamic industry like Fashion, most of the styles don’t get repeated from season to season but the patterns get repeated. Hence, grouping the products ensures that intelligence is carried from season to season while analyzing a longer period of time

• Whenever a Style is out of Stock, grouping helps the tool in identifying the similar styles that can be a replacement

Some examples of attributes include Brand, Category, Sub Category, Gender,Price Segment, Sleeves, Material etc.

EX - Puma - Shoes - Running - Women - 2500-3500 - TPU