Real-Time Warehouse Optimization with AI

Developed an AI-powered video analytics platform to optimize warehouse layouts for profitability, enabling rapid expansion and continuous operational efficiency improvements.

Challenge

Warehouse layout optimization was planned as a slow, manual process, delaying expansion and reducing operational efficiency.

Solution

Built a Kubernetes-based AI video analytics platform using existing camera systems to track worker and forklift movement, correlating it to product locations for layout optimization.

Impact

Reduced manual analysis time by 80%, improved warehouse efficiency by 71%, cut operational costs by 30%, and accelerated time-to-market for new warehouses by 50%.

Kubernetes Python OpenCV PyTorch AWS Optiplanner

Project Overview

A fast-growing logistics company was expanding rapidly, planning to open new warehouses both domestically and internationally. Their COO needed a system to optimize warehouse layouts for profitability, ensuring that the most ordered and highest-margin items were positioned for maximum efficiency.

Initially, they planned to analyze video feeds manually, a time-consuming process that would slow expansion and delay operational scaling. Instead, I led the development of an AI-powered video analytics platform that tracked worker and forklift movement, correlated it to shelf and product location data, and identified layout inefficiencies.

Their existing video processing hardware was unreliable, frequently crashing and delaying analysis. I architected a Kubernetes-based fault-tolerant video processing system that distributed video workloads across multiple GPUs, automatically rerouting workloads when hardware failures occurred. This ensured continuous analysis without downtime.

Due to its success, what started as a one-time warehouse optimization tool became a continuous intelligence platform for all warehouses, ensuring long-term efficiency improvements.

The result? Reduced manual analysis time by 80%, improved warehouse efficiency by 50%, cut operational costs by 30%, and accelerated time-to-market for new warehouses by 50%.

Key Achievements

  • Reduced manual analysis time by 80%, automating workflow tracking and eliminating bottlenecks.
  • Improved warehouse efficiency by 71%, ensuring high-demand items were positioned for maximum profitability.
  • Cut operational costs by 30%, reducing inefficiencies and optimizing product placement strategies.
  • Accelerated time-to-market for new warehouses by 50%, supporting rapid domestic and international expansion.
  • Transformed a one-time optimization project into a continuous intelligence system, enabling long-term warehouse efficiency improvements.

Technical Execution

  • Built a real-time video analytics system to track worker and forklift movement, mapping it to shelf locations and product demand.
  • Correlated retrieval frequency and movement patterns to identify layout inefficiencies and provide automated recommendations.
  • Developed a warehouse efficiency scoring model, enabling continuous refinement of layout profitability.
  • Designed a Kubernetes-based video processing pipeline to distribute workloads across GPUs, reducing downtime and ensuring resilience.
  • Implemented a failover mechanism to detect and reroute video workloads during hardware failures, ensuring uninterrupted analysis.
  • Optimized video decoding for high-throughput feeds from existing camera systems, preventing crashes and enabling real-time insights.
  • Implemented CI/CD pipelines for AI model updates and video stream ingestion, ensuring continuous performance improvements.

Leadership & Strategy

  • Collaborated with the COO and logistics leadership to ensure AI-driven insights were aligned with profitability goals.
  • Managed multiple engineering teams across AI, DevOps, and video analytics, delivering a scalable, company-wide solution.