3x
Inventory Efficiency
Case study · Multi-Channel Retail
AI-Powered Inventory Optimization for Multi-Channel Retailer
ML-based demand forecasting, real-time inventory sync, and automated reorder management for a multi-channel retail operation.
Next.js
React
Python
Django
PyTorch
AWS
PostgreSQLProject details
- Client
- Multi-channel retailer with warehouse and retail locations
- Industry
- Retail / E-Commerce
- Timeline
- 16 weeks
- Platforms
- Next.js + Django + AI/ML
The challenge
What we walked into.
The retailer was managing product inventory across multiple warehouses and retail locations with no predictive capabilities. Replenishment decisions were entirely reactive -- stock was allocated based on gut feel, leading to stockouts of high-demand SKUs and overstock tying up capital in slow-moving products.
Product quality assurance and catalog management were major bottlenecks. New products required manual categorization, attribute validation, and quality review before they could go live across channels. The operations team was overwhelmed, creating a backlog that delayed seasonal launches and time-sensitive promotions.
Without demand forecasting, the retailer had no way to anticipate buying patterns or pre-position stock for upcoming seasons, promotions, or regional demand shifts. Every inventory decision was a guess that cost real money.
What we built
The engineering decisions.
Modern Full-Stack Architecture
We built a Next.js frontend paired with a Django backend, designed for real-time inventory operations at scale. The frontend delivers a responsive, fast inventory management dashboard while Django handles the heavy lifting -- API orchestration, ML model serving, and background processing pipelines. PostgreSQL provides the transactional backbone with optimized queries for inventory analytics across all locations.
ML-Based Demand Forecasting
Using PyTorch, we trained demand forecasting models on historical sales data, seasonal trends, external signals (promotions calendar, regional events), and product metadata. The models predict product demand 7-14 days ahead, enabling the retailer to pre-position stock before demand materializes. The system continuously retrains on new sales data to improve accuracy over time.
Product Quality Assurance & Catalog Management
We built an automated catalog pipeline that handles the majority of product onboarding. The system validates product attributes, detects incomplete listings, auto-tags categories and product types, and routes edge cases to human reviewers. What previously took the operations team hours now happens in seconds, with human oversight reserved for genuinely ambiguous cases.
Automated Reorder & Stock Optimization
The forecasting models feed directly into an inventory management system that automatically triggers reorder points, adjusts safety stock levels, and optimizes allocation across warehouse and retail locations based on predicted demand. Fast-moving SKUs are pre-positioned at high-velocity locations before seasonal spikes. The system also identifies slow-moving inventory and recommends markdowns or redistribution strategies.
The results
Measured outcomes.
3x
Inventory efficiency improvement
10K+
SKUs managed across all locations
85%+
Demand prediction accuracy
- 3x improvement in overall inventory efficiency, eliminating stockouts and reducing overstock across all locations
- Real-time management of 10K+ SKUs across warehouse and retail with automated reorder triggers and allocation
- Automated catalog pipeline handling 90% of product onboarding, freeing the team to focus on merchandising strategy
- Demand prediction accuracy of 85%+, enabling proactive stock positioning 7-14 days ahead of seasonal demand spikes
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