Dock-to-Stock in 8 Hours: How Vision AI Shrinks Internal Travel Miles

Why Dock to Stock Speed Matters for Throughput

Most warehouses still need a full day to move freight from receiving to storage. Vision AI cuts that dock-to-stock cycle to about eight hours by mapping the fastest routes. With vision AI, that dock-to-stock cycle drops to about eight hours because the system maps the fastest routes. Best-in-class sites move even faster, shelving inbound pallets in under three hours, while APQC reports the average is still 12 to 24 hours. Every extra hour ties up capital, clogs aisles, and leaves pickers hunting for product.

Vision-driven heat maps and automated tasking remove these hidden search miles. After cameras confirm an unload is complete, the platform dispatches the nearest available forklift, plots the shortest put-away path, and verifies placement—no barcode gun required. The result is a dock-to-stock cycle that fits into a single shift instead of a full day.

Overhead view of a warehouse aisle: one forklift follows a blue “PICK UP” path from the inbound staging area toward storage racks, while another follows a yellow “DROP” path out toward outbound staging

1. Map the anatomy of internal travel waste

Internal travel steals up to 30 percent of a forklift’s available hours:

• Search miles: driver hunts for an empty slot

• Double handling: product staged twice before final location

• Mis-sequenced tasks: put-away and picking fight for aisle space

Vision AI records every forklift trail and displays traffic density so process-improvement teams see exactly where time leaks.

2. Heat maps spotlight dead zones and golden zones

Plot 30 days of forklift GPS or camera tracks. Hot colors show over-travel; cool colors show under-used aisles.

Example fix: Move slow-moving SKUs out of the “golden zone” (waist-to-shoulder, near shipping) and re-slot fast-movers. One electronics DC cut average pick path by 18 percent using this method.

3. Vision-triggered task assignment

• Cameras detect that Door 12 unload is finished.

• WMS pings the closest free forklift with a task and optimal slot.

• Tablet displays a map arrow and ETA.

• On arrival, camera verifies pallet placement and clears the task.

Early adopters report 15–25 percent faster put-away and virtually zero wrong-location errors.

4. Tie maintenance into the same data loop

Hours-of-use data from vision tracks feeds the maintenance CMMS automatically. Forklifts approaching service threshold get queued for PM after they finish current runs—no mid-shift breakdown surprises.

5. Coach operators with video-backed KPIs

Weekly scorecards pair metrics with short video clips of good and bad runs. Operators see how smoother turns or tighter pallet placement trims seconds. Targeted micro-coaching beats blanket retraining and raises engagement scores.

ROI example

Cutting dock-to-stock from 18 hours to 9 frees goods for sale half a day sooner. For a DC turning $500,000 of inbound inventory daily at 30 percent gross margin, that’s roughly $75,000 in incremental monthly margin just from faster availability—plus lower labor and equipment wear.

FAQ sneak peek

Q: What is dock-to-stock cycle time?

A: It’s the elapsed time between a carrier’s proof of delivery at your dock and the moment the SKU is available for picking in the WMS. Many sites still take 24 hours, but vision AI can cut that dock to stock cycle to about eight hours by mapping smarter routes.

Q: What is a good benchmark?

A: Best-in-class operations shelve inbound pallets in under three hours; average performers take 12–24 hours.

Q: Does vision AI need new cameras?

A: No. Seeteria retrofits your existing CCTV network, with cloud or on-prem options that keep IT teams happy.

Q: How hard is integration?

A: A REST API feeds events to major WMS and YMS platforms, and a typical pilot connects in roughly two weeks.

Next step

Schedule a discovery call to see how Seeteria’s vision analytics can unlock extra picking hours—without adding a single forklift.