Inventory Forecasting for Fashion Brands India

Inventory decisions lagged behind demand signals

The fashion brand sold through Shopify, marketplaces, and seasonal pop-ups, but inventory planning happened in monthly spreadsheet meetings. Ad campaigns could spike demand for a style while warehouse and return data arrived late, creating stockouts for winning sizes and dead stock in weak variants. Merchandising, marketing, and operations argued from different numbers.

Inventory forecasting for fashion brands india with shared data

We built a lightweight data pipeline using Shopify exports, marketplace reports, Meta Ads data, warehouse sheets, BigQuery, and Looker Studio. SKU, size, color, sales velocity, return rate, ad spend, and stock-on-hand feed a forecast table that highlights reorder risk, slow movers, and campaign-sensitive products. Teams plan from one dashboard instead of parallel spreadsheet versions.

Workflow Built

Sales data ingestion

Shopify and marketplace exports load into BigQuery with SKU, size, color, order date, discount, channel, and returned status.

Inventory sync

Warehouse Google Sheets update stock-on-hand, inbound purchase orders, and reserved quantities by SKU variant.

Marketing signal merge

Meta Ads campaign data joins to product collections and launch periods so demand spikes are connected to spend and creative pushes.

Forecast table creation

BigQuery calculates recent velocity, return-adjusted demand, weeks of cover, reorder risk, and slow-moving variants.

Planning dashboard

Looker Studio shows merchandising, marketing, and operations the same SKU-level forecast, stock risk, and channel performance.

Results

Planning source

Merchandising, marketing, and operations moved to one SKU-level planning dashboard

Stockout awareness

Fast-moving sizes and colors were flagged before the monthly planning meeting

Dead-stock control

Slow-moving variants were visible by return-adjusted demand and weeks of cover

Campaign context

Inventory planning included ad-driven demand spikes instead of looking only at historical sales

FAQs

How does inventory forecasting for fashion brands india use Shopify data?+

Inventory forecasting for fashion brands india can use Shopify orders, variants, refunds, discounts, and channel data. Scallar combines that with stock-on-hand and ad data so each SKU has sales velocity and reorder context.

Can inventory forecasting for fashion brands india handle size and color variants?+

Yes. Fashion forecasting needs SKU-variant detail because a style can sell well while one size or color creates dead stock. BigQuery and Looker Studio can model demand at that level.

Is this an alternative to expensive inventory planning software?+

For growing apparel brands, a BigQuery and Looker Studio dashboard can be a practical planning layer before buying enterprise software. It gives teams shared data while keeping the workflow flexible.

Can Meta Ads data improve fashion demand forecasting?+

Yes. Meta Ads data adds context when campaigns or launches create unusual demand. The dashboard can separate organic sales velocity from campaign-driven spikes so buying decisions are less reactive.