Inventory Forecast
Predictive ML model forecasting demand with seasonal trends and market signals.
Services
Machine Learning, Analytics
Stack
TensorFlow, Python, AWS
Timeline
3 Weeks
Client
Fashion Retailer
The Challenge
The retailer faced a "bullwhip effect" problem: they either overstocked unpopular items (wasting warehouse space) or ran out of best-sellers during peak seasons, losing significant revenue.
The Solution
We implemented a time-series forecasting model that analyzes historical sales data alongside external factors.
- Trend Analysis: Incorporates Google Trends and seasonal data to predict spikes in demand.
- Auto-Reorder: Automatically generates purchase orders for suppliers when stock hits optimal levels.
- Dead Stock Alert: Identifies slow-moving items early so they can be discounted before they become liabilities.
The Impact
Inventory turnover improved drastically, and stockouts on top items were virtually eliminated.
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