Assortment Planning,
explained for modern retail
Assortment planning is the retail process of deciding which products (SKUs), in what mix and depth, should sit in each store or channel for a given season. Done well, it lifts revenue, cuts dead stock, and protects gross margin. Done in spreadsheets, it leaks money. This guide covers the process, the inputs, the math, and how AI-driven store-level allocation changes the game — with a focus on Indian retail.
What is assortment planning?
Assortment planning sits between merchandise financial planning (the budgets and OTB) and execution (buying, allocation, replenishment). It translates category-level financial targets into a specific list of styles, colors, sizes, and quantities — and then decides how those flow to each store cluster. The output is a plan that respects brand strategy, price architecture, capacity, and predicted demand simultaneously.
The core inputs
- Demand signal — historical sales, seasonality, search and trend data, weather, and festival calendars (critical in India).
- Store clusters — groups of stores with similar demand DNA: tier, format, catchment, climate, customer mix.
- Curves — size curves, color curves, price ladders, brand mix targets.
- Constraints — store capacity, minimum presentation quantities, supplier MOQs, calendar windows.
- Strategy — newness rate, depth-vs-breadth, exclusivity, range life cycle.
A modern assortment planning workflow
- Ingest & normalize the master catalog and historical sales into a unified product graph.
- Forecast demand at SKU × store × week using machine learning, not flat averages.
- Cluster stores by behavior — not just geography — so similar stores get similar plans.
- Build the assortment — choose styles, set depths, apply size and color curves, validate against brand and price rules.
- Allocate to individual stores with a constraint-based optimizer: that's store-level allocation.
- Monitor & replenish in-season; re-cluster and re-flow as demand reveals itself.
Assortment planning for Indian retail
India retail isn't one market. A 1,200 sq ft store in a tier-3 town in Tamil Nadu and a 6,000 sq ft store in BKC, Mumbai have nothing in common except a brand name. Festival demand spikes for Diwali, Onam, Pongal, Eid, and Durga Puja are non-trivial and regional. Sizing, color preference, and price sensitivity vary sharply by tier and state. A good assortment planning system for India must handle this granularity natively — not as an afterthought.
How Assort Agent helps
Assort Agent is purpose-built for this workflow. It ingests your master catalog, normalizes attributes, runs ML-based demand forecasts, applies your merchandising logic, and outputs store-level allocations in minutes — not weeks. Layer it on top of your existing planning stack — Assort Agent focuses on assortment and allocation, and integrates with your upstream merchandise financial planning systems.
Ready to see it on your data?
Talk to us about a tailored walkthrough on your assortment.
Contact sales