The Feed Sports Nutrition Automated Fulfillment Center with Brightpick

Project Overview

The Feed, a leading sports nutrition retailer, has implemented what is described as "the world's most advanced e-commerce warehouse run entirely by AI robots." This cutting-edge fulfillment center operates 24/7 with just a handful of human staff, processing 50,000 items per day through a fleet of 48 Brightpick Autopicker autonomous mobile robots.

The facility represents a dramatic transformation from traditional warehouse operations. Previously requiring 50-100 workers who walked the equivalent of two marathons per week to fulfill orders, the operation now relies on AI-powered robots that handle everything from inventory replenishment to robotic picking and order consolidation.

The Brightpick solution stands out for its infrastructure-free approach—unlike shuttle systems or cube ASRS solutions that require fixed grids and conveyors, these AMRs operate with standard shelving and totes, enabling rapid deployment and flexibility. The system seamlessly integrates robotic picking, goods-to-person operations, and pallet picking into a unified automated workflow.

Technical Solution

Brightpick Autopicker AMR System

The Brightpick Autopicker is the only autonomous mobile robot in the world that robotically picks and consolidates orders directly in warehouse aisles, functioning like a human with a cart. Each robot starts by grabbing an empty tote, navigates autonomously into the storage area, picks items from storage totes, and places them into the order tote—repeating this process until the entire order is complete.

The robots are powered by proprietary 3D machine vision and artificial intelligence that has been trained on more than one billion picks to date. The AI uses data from 3D scanners to map all individual items inside storage totes and identifies the optimal picking point for each item based on its size, shape, and form. The system uses machine learning to improve with each pick, learning from any fumbled attempts to enhance future performance.

The robots can reliably pick diverse product types including ambient and chilled groceries, pharmaceuticals, medical devices, packaged goods, cosmetics, electronics, and polybagged apparel—even items they've never encountered before.

Key Features

The 48 Brightpick Autopicker AMRs with AI-powered robotic picking represent the core of this operation, functioning autonomously 24/7 without fatigue or errors. The fleet processes 50,000 items daily, handling the entire workload that previously required 50-100 human workers.

The 3D machine vision system has been trained on over one billion picks, giving it exceptional reliability across diverse product types. The AI can identify optimal picking points for items it has never seen before, and uses machine learning to continuously improve performance with each pick.

The 11,000 tote storage capacity with 10-foot tall shelving provides substantial inventory holding while maintaining a compact footprint. The dynamic slotting algorithm optimizes storage placement based on item velocity and picking method, minimizing robot travel time.

The hybrid picking approach seamlessly combines robotic picking, goods-to-person operations, and pallet picking within a single system. This versatility ensures 100% pick reliability—robots handle what they can, while humans assist with challenging items at goods-to-person stations.

The order buffering capability enables overnight picking operations and smooths peak hour volumes. Robots can pick orders while staff is away and store them until pack-out is ready, maximizing throughput without additional labor.

The infrastructure-free design uses standard shelving and totes with no fixed grids, conveyors, or complex floor markings. This approach enables fast deployment, easy reconfiguration, and the ability to relocate the system to a different warehouse if needed.

Results & Benefits

The facility dramatically reduced its workforce from 50-100 people to minimal staff while maintaining 24/7 operations. The business was growing fast enough that existing employees were reassigned to other roles rather than laid off, and the company could essentially stop hiring for picking positions.

Workers no longer walk the equivalent of two marathons per week to fulfill orders. The robots handle all the walking and picking, allowing human staff to focus on packing and getting products out the door efficiently.

The goods-to-person stations achieve zero idle time by handling up to four robots simultaneously from all sides. This ensures human operators are continuously productive without waiting between picks.

The machine learning system continuously improves pick accuracy with each attempt. When a robot fumbles a pick, it learns from the mistake and performs better on subsequent attempts, creating a self-improving system.

Training time for staff was minimal—workers simply wait for robots to approach, interact with the intuitive interface, and send robots on their way. The transition from the old manual picking system to the automated operation was seamless.

Challenges & Solutions

The primary challenge was handling the diverse range of product types found in sports nutrition retail—from bottles and packaged goods to poly bags and items with varying grippability. The solution came through the AI-powered 3D vision system trained on over one billion picks. The system maps each item's geometry and identifies optimal picking points regardless of whether it has encountered that specific product before. Machine learning ensures continuous improvement with each pick attempt.

Another challenge involved managing items that robots cannot pick autonomously due to excessive weight, unstable center of gravity, or lack of smooth grippable surfaces. The hybrid system addresses this by incorporating goods-to-person stations where human operators handle challenging items. Each station can manage four robots simultaneously, ensuring 100% pick reliability while maintaining high productivity and maximizing labor savings.

Managing volume fluctuations and enabling overnight operations presented operational challenges. The order buffering system solved this by allowing robots to pick orders overnight and store them until pack-out is ready. This capability smooths out peak hour spikes and enables higher daily throughput without requiring additional automation or labor investment.