AI Slotting Optimization

Real-time pick path optimization cuts labor hours, improves accuracy, and delivers ROI in three months.

CLIENT

e-Commerce fulfillment provider

INDUSTRY

Logistics

SERVICE

AI-powered slotting optimization | Real-time pick path planning | WMS integration| Dashboards development

Overview

A U.S.-based e-commerce fulfillment provider wanted to boost operational throughput and improve order accuracy without costly infrastructure upgrades. Their existing warehouse management system lacked real-time intelligence, forcing staff to work with static slotting layouts and inefficient pick routes. This led to excessive travel times, congestion in high-traffic aisles, and frequent order delays. Partnering with Sphere, the provider deployed an AI-powered slotting and pick path optimization solution that delivered measurable efficiency gains in weeks, not years.

Challenges

The fulfillment provider’s existing warehouse setup could not adapt quickly to changing demand patterns or operational bottlenecks. Static layouts, manual routing, and limited real-time oversight created inefficiencies that slowed order processing, increased labor costs, and impacted accuracy. Four main issues stood out:

Static Slotting Layouts

SKU placement was only updated quarterly, ignoring seasonal demand changes and promotional spikes. High-demand products were often stored far from packing stations, forcing pickers to make long trips and slowing order fulfillment.

Inefficient Pick Pathing

Pick routes were manually planned at the start of the day and rarely adjusted, even when priorities shifted. This caused unnecessary backtracking, increased congestion in certain aisles, and lowered overall productivity.

Order Inaccuracy and Rework 

The layout and routing inefficiencies increased the likelihood of mispicks. Each error required rework, delaying shipments and pulling labor away from fulfilling new orders.

Lack of Real-Time Responsiveness

The WMS provided no ability to adjust slotting or routes during active shifts. Supervisors lacked data-driven tools to correct bottlenecks or respond quickly to operational disruptions.

Our Solution

Sphere designed and deployed an AI-driven optimization engine that integrated directly with the client’s WMS via APIs — no physical changes to the warehouse were required. The solution had three key components:

Dynamic Slotting Optimization

The AI continuously analyzed SKU velocity, product dimensions, order history, and demand forecasts to determine the best placement for each product in the warehouse. It weighed multiple factors, balancing reduced picker travel with efficient replenishment cycles, safety stock placement, and space utilization. High-volume SKUs were moved closer to packing areas while slow-moving items were relocated to peripheral storage. This reconfiguration reduced average picker travel time by double digits without disrupting the overall storage logic.

Real-Time Pick Path Planning

Using live order queues, congestion data, and workforce availability, the AI recalculated pick routes throughout the day. The routing engine applied graph optimization algorithms that factored in aisle traffic and order priority to find the fastest possible sequence of picks. When high-priority orders entered the system or certain aisles became congested, routes were updated instantly on handheld picker devices, keeping productivity levels high.

Supervisor Control & Monitoring Dashboard

Sphere built an intuitive dashboard that provided supervisors with real-time visibility into slotting recommendations, pick path efficiency, and congestion alerts. The dashboard pulled data from barcode scanners, RFID checkpoints, and WMS order logs, enabling supervisors to approve AI-generated slotting adjustments or push updated routes to the floor with a single action.

Seamless WMS Integration

The solution was connected via secure REST APIs, using encrypted data exchange to protect operational information. Integration was designed to cause zero downtime, and the pilot-to-rollout process was completed in just eight weeks.

Key Achievements

+12% Picking Efficiency

Reduced average travel distance per order.
+9% Order Accuracy

Fewer mispicks and reduced rework.
-5% Labor Hours

Lowered labor costs per fulfilled order.
Fast ROI

ROI achieved within three months of deployment.

Result

By deploying AI-powered slotting and pick path optimization, the fulfillment provider achieved measurable operational improvements without investing in new physical infrastructure. Real-time intelligence allowed the warehouse to adapt instantly to changing demand, improving speed, accuracy, and customer satisfaction.

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Luke Suneja

Client Partner

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