E-COMMERCE

Smart Inventory System

AI-driven inventory optimization reducing stockouts by 80% and cutting holding costs by 35% for retail chain.

6 months4 engineers8 min read

Key Results

Stockout Reduction80%

Holding Cost-35%

Forecast Accuracy94%

Order Fill Rate99.2%

Client

Multi-Channel Retail Brand

Industry

E-commerce / Retail

Location

Los Angeles, USA

Overview

A multi-channel retail brand selling across their own website, Amazon, and 200+ wholesale accounts was struggling with inventory management. Frequent stockouts on bestsellers cost them an estimated $4M in lost revenue annually, while overstocking of slow-moving items tied up $2M in excess capital.

We built an AI-driven smart inventory system that predicts demand at the SKU level, automatically generates purchase orders, and optimally allocates inventory across channels and warehouses. The system considers seasonality, promotions, lead times, and real-time sales velocity.

The Challenge

Demand Forecasting

Predict demand for 15,000+ SKUs across multiple channels with varying seasonality, promotional impacts, and external factors like weather and economic trends.

Multi-Channel Sync

Keep inventory levels synchronized in real-time across the DTC website, Amazon FBA, and 200+ wholesale accounts to prevent overselling and stockouts.

Supply Chain Variability

Account for variable supplier lead times (2–12 weeks), minimum order quantities, and container optimization when generating purchase recommendations.

Warehouse Optimization

Optimally distribute inventory across 3 warehouses to minimize shipping costs and delivery times while maintaining fill rate targets.

Our Solution

Architecture Overview

Forecasting Engine

Python ML + Prophet

Orchestration Layer

Golang Services + Event Bus

Data Layer

MongoDB + TimescaleDB

ML Demand Forecasting Engine

Built a hierarchical forecasting model using Prophet and gradient boosting that predicts demand at the SKU-channel-warehouse level. Incorporates seasonality, promotions, price elasticity, and external signals.

ProphetXGBoostTime Series

Real-Time Inventory Sync

Developed an event-driven inventory synchronization system using Golang that maintains a real-time single source of truth across all sales channels. Updates propagate in under 500ms.

GolangNATSEvent-Driven

Automated Replenishment

Created an optimization engine that automatically generates purchase orders based on forecasted demand, current stock levels, supplier lead times, and MOQ constraints.

OptimizationPO AutomationConstraints

Warehouse Allocation Optimizer

Built a linear programming model that optimally distributes incoming inventory across 3 warehouses to minimize shipping costs while meeting regional demand forecasts.

Linear ProgrammingAllocationCost Optimization

Performance Metrics

Transaction Throughput

Response Time Distribution

80%

Stockout Reduction

35%

Cost Savings

94%

Forecast Accuracy

99.2%

Fill Rate

Technology Stack

Backend & Services

Golang 1.21
NATS Streaming
Python Workers

Data & ML

MongoDB
TimescaleDB
Prophet + XGBoost

Infrastructure

GCP
Cloud Run
BigQuery

Outcomes & Impact

Financial Impact

80% reduction in stockouts recovered an estimated $3.2M in annual revenue
35% decrease in inventory holding costs freed up $700K in working capital
Automated PO generation saved 20 hours per week of procurement team time

Operational Excellence

Order fill rate improved from 91% to 99.2% across all channels
Real-time inventory visibility eliminated overselling incidents
Warehouse utilization optimized with 15% reduction in storage costs

Forecasting Performance

94% demand forecast accuracy at the SKU-channel level (MAPE < 6%)
Promotional impact predictions within 8% of actual for 90% of campaigns
Seasonal patterns detected automatically, reducing manual planning effort

Scalability

System handles 15,000+ active SKUs across 200+ wholesale accounts
New sales channel integration takes days instead of months
Architecture supports 10x growth without infrastructure changes

“We went from constant stockouts and angry customers to a 99.2% fill rate. The AI forecasting is remarkably accurate, and our procurement team now focuses on strategy instead of firefighting. BeluMind delivered incredible ROI.”