eCommerce - FASHION
AI Forecasting for Trend-Driven Inventory
goal
Challenge Faced
Fashion retailers face unpredictable demand due to fast-changing trends, leading to frequent stockouts or overstocks, both of which hurt revenue.
solution
Our Solution
We implemented an AI-driven demand forecasting system that predicts sales patterns using historical data, seasonal trends, and real-time social media signals.
Tools we used
Approach
Step-by-Step Workflow
Step 1
Aggregate sales & seasonal data.
Step 2
Scrape social and trend data (hashtags, mentions).
Step 3
Apply ML time-series forecasting models.
Step 4
Deliver SKU-level demand projections to supply chain systems.
Quantifiable Benefits
Reduction in stockouts
%
Reduction in excess inventory
0
%
Faster supply chain decision-making
0
%
Increase in revenue from trend alignment
0
%
Who’s Using This
Fast-fashion giants like Zara, H&M
Online-first fashion brands scaling operations
Retailers managing global multi-SKU inventories
Why It Matters
By aligning inventory with demand, retailers can capture more sales opportunities while reducing waste and operational costs.