GreenLeaf Logistics: AI-Powered Fulfillment

The Challenge

GreenLeaf Logistics operates a 400,000 sq ft fulfillment center processing 15,000+ orders daily for e-commerce clients. As volume grew, their manual processes buckled:

• Order Assignment: Supervisors manually assigned orders to pickers, creating bottlenecks
• Inventory Prediction: Stockouts and overstock situations were common
• Quality Control: Manual inspection couldn't keep pace with volume
• Client Reporting: Staff spent hours compiling reports from disparate systems

They needed automation that could integrate with their existing WMS without a forklift upgrade—and they needed it fast. Peak season was 4 months away.

Our Approach

Rather than replacing their warehouse management system, we built an intelligent layer on top of it. Our automation platform:

1. Ingests real-time data from WMS, conveyor PLCs, and IoT sensors
2. Analyzes patterns using machine learning models
3. Decides optimal actions (order routing, replenishment, staffing)
4. Executes via API integrations and human task queues
5. Learns from outcomes to improve future decisions

We also upgraded their network infrastructure to support IoT sensors and real-time data transmission across the facility.

Technical Implementation

Intelligent Order Routing

Orders are now automatically assigned based on picker location, skill level, current workload, and zone congestion. The algorithm optimizes for pick path efficiency while balancing workload across the team.

# Simplified routing logic
def assign_order(order: Order, pickers: List[Picker]) -> Picker:
    scores = []
    for picker in pickers:
        score = calculate_assignment_score(
            picker_location=picker.current_zone,
            order_zones=order.pick_zones,
            picker_skill=picker.skill_rating,
            current_workload=picker.active_orders,
            zone_congestion=get_zone_congestion(order.pick_zones)
        )
        scores.append((picker, score))

    return max(scores, key=lambda x: x[1])[0]

Predictive Inventory

We trained models on 18 months of historical data to predict demand by SKU. The system now automatically generates replenishment recommendations 72 hours ahead, reducing stockouts by 84%.

Computer Vision QC

Cameras at pack stations capture images of completed orders. Our CV model verifies correct items, quantities, and packaging. Exceptions are flagged for human review, while clean orders proceed automatically. This replaced a 6-person QC team with a 2-person exception-handling team.

Infrastructure Upgrades

To support real-time automation, we deployed:

• Warehouse-wide WiFi 6 coverage with seamless roaming
• Industrial IoT gateway for PLC and sensor integration
• Edge computing nodes for latency-sensitive AI inference
• Redundant fiber backbone between zones

Results

The automation platform launched 3 weeks before peak season and performed flawlessly:

• Throughput: Daily order capacity increased from 15K to 24K without adding staff
• Accuracy: Pick-and-pack error rate dropped from 2.1% to 0.3%
• Labor Efficiency: 1,200 labor hours saved monthly through automation
• Client Satisfaction: NPS score increased 18 points due to fewer errors and faster shipping

The system paid for itself in 4 months through labor savings alone.

Client Testimonial

"I was skeptical about AI—we've seen a lot of vaporware in this industry. DolphyTech delivered real, measurable results in a real warehouse environment. Our supervisors went from skeptics to evangelists within a month. The system just works, and it gets smarter every week."

— David Okonkwo, COO, GreenLeaf Logistics