Column Mapping

Column Name Description
store name of store
store_code unique code of store
channel channel to which store belongs
store_class class assigned to store in store master
store_cluster cluster mentioned for store in store master
city city in which store is located
region region of the store
ean ean code of style (unique)
style Unique code for style
parent_style parent code of style if any, otherwise same as style code
style_description description details of style code as mentioned in the style master
master_category master category to which style belongs
category category to which style belongs
subcategory subcategory of style
brand brand mentioned in style master
brand_segment name of the brand for which a particular article is belonged to in style master table
season season to which style belongs- AW/SS
gender gender of style as mentioned in the style master
size size of style
size_group size group- pivotal/non-pivotal
initial_qty_at_store Actually present at store
ist_in_transfer Any stock in movement due to inter store transfer
goods_in_transit Stock in transit from warehouse to store as on today
open_orders Stock reserved in warehouse against store as on today but not in transit
dispatch_suggested Stock suggested for movement from warehouse to store to address demand
final_qty_at_store Stock in store after implementing this replenishment output
pull_back Quantity suggested for pull back. Will be seen only if pullback is allowed by user
type type of iteration
iteration_flag Level of iteration where it was resolved
segment Top seller or Normal Seller. System identifies some styles as local top sellers for each store based on relative performance with the category this style belongs to
remarks Reason
suggested_allocation Stock required at store as per the demand. Rate of sale multiplied by cover days
period_one_sales Sales in the recent xx days
period_two_sales Sales in the recent yy days
period_three_sales Sales in the recent zz days
period_one_ros Rate of sale based on recent xx days sale. Rate of sale = Sales/actual live days for SKU in this xx days
period_two_ros Rate of sale based on recent yy days sale. Rate of sale = Sales/actual live days for SKU in this yy days
period_three_ros Rate of sale based on recent zz days sale. Rate of sale = Sales/actual live days for SKU in this zz days
recent_sku_ros Rate of sale based on weighted average of period 1 ROS and period 2 ROS. 80% of period 1 ROS + 20% of period 2 ROS
pre_psa Availability of pivotal sizes in store pre replenishment / overall number of pivotal sizes for that store
post_psa Availability of pivotal sizes in store after replenishment/ overall number of pivotal sizes for that store