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Getting Started

Welcome To IRIS

IRIS is a collection of patent-pending algorithms which make merchandising activities super-fast and accurate. Merchandising is a complex subject and this space-age software captures that complexity to increase your prediction accuracy with lightning speed. There is a fair amount of training required to understand and use each module. So please read this document and all other module specific documents in detail before you start.


Projects

Your company may be organized in different Business Units (BU) handling different brands or categories. Projects help each BU manage and analyze their own data. Projects can also be used to do any test analysis. One of the places people make most mistakes is in uploading data or playing with parameter values. So, feel free to create as many projects as you want – to – test, learn, explore and become an expert in IRIS.


Clusters

When customers walk into a store, they have certain parameters in mind for buying. E.g. I want to buy a Victor’s Secret casual shoe in the range of $100-$150. IRIS captures this set of customer demand parameters in what are known as clusters. Clusters are at the heart of IRIS merchandising algorithms. The table below contains an illustration of clusters.

brand category subcategory gender mrp_bucket exit_flag
Victor’s Secret shoe casual shoe M 101-200 true
Victor’s Secret shoe casual shoe M 201-300 true

All the sales data is aggregated and analyzed at a cluster level. All projections are done for clusters, not styles. This helps us answer questions such as How many quantities should I procure for Victor’s Secret brand, in casual shoes, for males, in the price range of $101-$200?


Merchandising

The system provides you with different modules to analyze different aspects of merchandising.

Module What it does Modules to run before Required Inputs Output Reports
Masters Upload basic masters like your stores, seasons, styles and SKUs. For any transactional and business constraints data you upload, the relevant master data should have been uploaded – None – Stores
– SKU Attributes
– Category Sizes
– Styles
– SKUs
– Summaries of all uploaded data
Sales Data Cleanup Removes unwanted, heavily discounted sales data as such data skews true demand – Masters
– Sales
– Summary of cleaned up sales data
Top Sellers Finds the bestselling Styles. These are called Top Sellers. You should always have these Styles in your stock – Masters
– Sales Data Cleanup
– None – Top Seller Styles
Ideal Assortment Creates an optimum assortment, for every month+store+cluster+size combination – Masters
– Sales Data Cleanup
– Top Sellers
– None – Optimum Assortment at store level
Revenue Split Based on your AOP (Annual Operating Plan), finds out optimum quantity and number of options, for every month+cluster combination – Masters
– Sales Data Cleanup
– Top Sellers
– Assortment
– Annual Operating Plan
– Demand Prediction at month+cluster level
– Optimum Size Set Ratios at month+cluster level
Buy Plan
Under development
Creates a Buy Plan based on revenue split and your current inventory. Coming soon Coming soon Coming soon
Distribution Finds out how to distribute your warehouse inventory to stores – Masters
– Sales Data Cleanup
– Top Sellers
– Assortment
– Warehouse inventory
– Store inventory
– Store planogram
– Allocation data
– Replenishment data
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