AI Agent/Logistics & Warehousing

AI Inventory Forecasting That Cut Stockouts by 89%

-89%

Stockouts

-34%

Excess Inventory

$400K

Revenue Recovered

92%

Forecast Accuracy

The Challenge

A regional warehouse distributor managing 15,000+ SKUs was stuck in a cycle of overstocking slow movers and running out of bestsellers. Their Excel-based forecasting was 6 weeks behind reality. Stockouts were costing $400K/year in lost sales, while $1.2M sat in dead inventory. Seasonal demand spikes caught them off guard every time.

Our Solution

We built a machine learning forecasting system that predicts demand at the SKU level and automatically triggers reorder workflows.

Data Pipeline with Python + Supabase

Consolidated 3 years of sales history, supplier lead times, seasonal trends, and external signals (weather, holidays, promotions) into a clean Supabase data warehouse. Python ETL scripts run nightly to keep the dataset current.

Demand Forecasting with TensorFlow

Trained a TensorFlow LSTM model on historical demand patterns for each SKU category. The model accounts for seasonality, trend, promotional uplift, and day-of-week effects — generating 12-week rolling forecasts updated daily.

Automated Reorder Workflows via N8N

N8N workflows monitor inventory levels against forecast thresholds. When a SKU is projected to run low, the system auto-generates a purchase order, sends it to the supplier via email, and updates the ERP — no human intervention needed.

Dashboard & Alerts

Built a real-time dashboard showing forecast vs. actual demand, inventory health scores, and cash tied up in stock. Daily Slack alerts notify the warehouse manager of upcoming stockout risks and overstock situations.

Tech Stack

Built With

Delivered in 6 weeks

Python

Data pipeline & model training

TensorFlow

LSTM demand forecasting model

N8N

Reorder automation & alerts

Supabase

Data warehouse & real-time dashboard

Slack

Inventory alerts & daily reports

Pandas

Data transformation & analysis

The Outcome

Stockouts dropped 89% in the first quarter. Excess inventory was reduced by 34%, freeing up $420K in working capital. The model achieves 92% forecast accuracy at the SKU level, and the warehouse team now plans proactively instead of reactively.