Ask most garment store owners in India how they decide what quantities to order from their suppliers, and the honest answer is: experience and intuition. They know roughly that M and L sizes sell more than S and XXL. They know that darker colours sell better in winter. They know certain styles will be popular.
What they don't know — because they don't have the data — is exactly how much faster M and L sell than S, the precise split between colours in their specific customer demographic, and which styles from the last season were profitable versus margin drains.
This imprecision has a direct financial cost: overstock in sizes that don't move (₹3–5 lakh tied up in working capital and eventual markdowns) and stockouts in popular sizes (lost sales at full margin when demand is highest).
The Size-Colour Variant Problem in Garment Planning
Garment retail is unique among retail categories because every style multiplies into dozens of individual SKUs. A single kurta design in 5 sizes and 4 colours is 20 SKUs. Order 100 pieces of the style and you need to decide how to distribute those 100 pieces across the 20 combinations.
Most garment buyers make this decision intuitively. An experienced buyer in a Ludhiana market might be right 70% of the time — but 30% wrong means a significant portion of every order ends up in the wrong sizes and colours.
The alternative is to use your own sales history to guide the decision.
Size Curve Analysis: What Your Sales Data Tells You
A size curve shows the percentage distribution of sales across sizes for a specific category. GoClixy's variant-level sales report shows, for any category and time period:
Example for men's cotton shirts (July–December): | Size | Units Sold | % of Total | |------|-----------|------------| | S | 48 | 8% | | M | 192 | 32% | | L | 228 | 38% | | XL | 102 | 17% | | XXL | 30 | 5% |
This is your size curve. When placing your next order for a similar style, this curve tells you how to split the quantities: if you're ordering 500 pieces of the new style, order approximately 40 S, 160 M, 190 L, 85 XL, and 25 XXL.
This is not a perfect science — style variations affect the curve, and your customer base shifts over time. But it's significantly better than ordering 100 of each size, which would leave you with 52 extra S and 90 extra L while running out of M.
Colour Sell-Through Analysis
The same logic applies to colours. Within a style, some colours consistently outsell others. Track sell-through rate per colour (percentage of stock that sold within the selling season) to identify your reliable performers.
A colour with 90% sell-through is a safe order. A colour with 40% sell-through is a risk — order fewer pieces and mark down the excess more aggressively.
For seasonal colours (bright colours that sell in spring, dark colours in winter), track the colour performance by season rather than annually to avoid averaging out the seasonality.
The Multiple Small Orders Strategy
The traditional garment buying approach is one large opening order for the season. This approach maximises discount from the supplier (larger orders get better rates) but maximises inventory risk (you're committed to all quantities before seeing actual demand).
A more effective strategy for stores with good supplier relationships is multiple smaller orders:
- Opening order (40–50% of planned seasonal buy): Conservative quantities across the full range. Test the market.
- Mid-season reorder (30–35%): Based on early sell-through data, reorder the fast movers and skip the slow ones.
- Closing top-up (15–20%): Fill in specific size-colour gaps identified from the mid-season data.
This approach requires faster supplier turnaround and slightly higher per-unit cost, but dramatically reduces end-of-season overstock. The overall margin improvement from fewer markdowns typically more than offsets the higher per-unit cost.
Identifying Dead Stock Early
Slow-moving garment stock has a timing problem: it's most visible when it's too late to do anything about it at full price. A kurta that's been on the shelf for 90 days with no movement should have been marked down at day 45 — when there was still time to sell it at a modest discount rather than a deep one.
GoClixy's inventory reports show days-in-stock per item. Setting an alert for items that have been in stock without a single sale for 30 days gives you early visibility into slow movers — when you still have options beyond a deep markdown.
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Frequently Asked Questions
How should garment stores plan purchase orders for new collections? Based on sales velocity data from the previous season by size-colour variant, current stock levels, selling period length, and supplier MOQ. Use your own data to order proportionally to actual demand.
What is size curve analysis? The percentage distribution of sales across sizes in a category. It tells you how to split a purchase order across sizes to match likely demand — avoiding the equal-quantity mistake.
How can stores reduce end-of-season overstock? Use sales velocity data, place multiple smaller orders during the season, track sell-through weekly, and mark down slow movers early while still in season.
How does GoClixy help with purchase planning? Variant-level sales reports show which size-colour combinations sold fastest and slowest. This data directly informs purchase order quantities for the next season.
What should a garment store purchase order contain? Style/design code, fabric specs, colour variants, size breakdown per colour, quantity per size-colour, unit price, delivery date, and payment terms. GoClixy generates POs shareable via WhatsApp.
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Also read: Garment Store Size & Colour Variant Inventory Management · How to Manage Alterations in a Garment Store