Inventory Forecasting for Fashion Brands India with BI
A fashion brand was buying inventory from gut feel while Shopify, ad spend, returns, and warehouse data lived separately. We built a demand dashboard that turned sales velocity and campaign signals into planning inputs.
Client
Omnichannel fashion and apparel brand
Team size
Shopify store, 3 marketplaces, 2 warehouses, and 180 active SKUs
Industry
Fashion & Apparel
Build
Shopify and marketplace exports -> BigQuery model -> ad and returns data -> SKU forecast table -> Looker Studio planning dashboard
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.
Documented delivery scope
What this case study can evidence
This case study is based on the delivery scope documented on this page: shopify and marketplace exports -> bigquery model -> ad and returns data -> sku forecast table -> looker studio planning dashboard for a omnichannel fashion and apparel brand in Jaipur, Rajasthan, India. It is an implementation record, not a promise that another business will see the same outcome.
Workflow evidence
The recorded build includes sales data ingestion, inventory sync, marketing signal merge, forecast table creation, planning dashboard. Each step is shown below so readers can see the stated handoffs rather than infer hidden work.
Tools and ownership
The listed tools support the defined workflow: BigQuery for stores and models sales, returns, ad, and inventory data by sku variant; Dataflow for normalizes recurring exports before loading structured tables for analysis; Shopify for provides online store orders, product variants, refunds, and customer purchase data; Meta Lead Ads for supplies campaign and collection-level demand signals for launch periods; Looker Studio for gives teams a shared dashboard for reorder risk, slow movers, and sku velocity. Final operating ownership remains with the client team.
How to read results
The results section reports only the stated planning source and stockout awareness and dead-stock control and campaign context. Context, offer quality, team response, and adoption affect any future implementation.
Evidence-led delivery record
Context, scope, handover, and measurement
The details below are limited to the documented record on this page. They clarify what was in scope for this omnichannel fashion and apparel brand in Jaipur, Rajasthan, India; they do not add unverified client facts, guarantees, or transferable outcome claims.
Starting constraint
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.
Agreed implementation scope
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.
Launch and handover boundaries
The recorded workflow identifies the delivery path from sales data ingestion through planning dashboard. The listed tools and owners are the reference for testing, exception handling, and operational handover; this page does not claim work beyond that recorded scope.
How the change is assessed
The recorded measures are planning source, stockout awareness, dead-stock control, campaign context. They provide the stated review points for this implementation; results depend on the client context, adoption, and operating process.
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.
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