AI Inventory Forecasting for Ecommerce
94% prediction accuracy. $220K+ in annual savings from day one.
Client details anonymized under NDA. The work, approach, and results shown here are real. Contact me for references.
Prediction Accuracy
Dead Stock Eliminated
Stockouts (from 12)
Reorder Speed
The Challenge
What they were dealing with
A DTC ecommerce brand with over 2,000 SKUs across three warehouses was doing inventory forecasting in spreadsheets. One person owned the entire process. The result was over $200K in dead stock annually from overstocking slow movers, plus stockouts on bestsellers during peak periods that cost $40K or more per event.
The forecasting model was literally "last year's numbers plus 10%"
No accounting for marketing campaigns, seasonal trends, or supplier lead time variability
Reorder decisions were made weekly in a team meeting, which was far too slow for fast moving products
Three warehouses with absolutely no cross location visibility into stock levels
Before
62%
Forecast Accuracy
$200K
Dead Stock / Year
12+
Stockout Events / Year
2 weeks
Reorder Lag
The Approach
How I solved it
Spreadsheet forecasting fails because it treats every SKU the same way. A hoodie that sells ten times more during winter needs a completely different model than a year round staple. I built a prediction system that segments SKUs into behavioral clusters (seasonal, trending, steady, declining) and applies different forecasting models to each cluster.
The system ingests sales velocity, the marketing calendar (planned campaigns spike demand), supplier lead times (which vary by vendor), and even weather data for seasonal categories. Instead of weekly meetings, the system generates daily reorder recommendations with confidence levels.
High confidence recommendations auto generate purchase orders. Low confidence ones get flagged for human review. The team went from guessing once a week to making data informed decisions every morning before their first coffee.
Data Audit and Cleanup
Consolidated three years of sales data across all three warehouses, cleaned SKU inconsistencies, and mapped every supplier lead time.
Segmentation and Modeling
Clustered over 2,000 SKUs into behavioral groups and built separate forecasting models per cluster using historical patterns.
Dashboard and Automation
Real time dashboard with stock levels, predictions, and alerts. Auto PO generation for high confidence reorders. Cross warehouse transfer recommendations.
Campaign Integration
Connected to the marketing calendar so the system pre positions inventory before planned promotions and flash sales.
The Results
What changed
94%
Prediction Accuracy
$180K
Dead Stock Eliminated
2/yr
Stockouts (from 12)
Same day
Reorder Speed
“Six people were doing nothing but data entry and inventory guessing. He automated the whole thing. Took about four weeks. Those six people now do work that actually matters and we process orders faster than before.”
Sarah Mitchell
Head of Ops, DTC Brand
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