Industry 03 · Retail · DC · 3PL · Multi-client

Store replenishment.
Plus everything
you ship for clients.

Retail and 3PL share the same problems: mixed SKUs, both pallets and cartons, repetitive replenishment with unpredictable peaks. And — in 3PL — different client requirements, in the same warehouse.

Typical operation profile
Stores / clients50 – 800
Lines / order15 – 80
Active SKUs10,000 – 80,000
Carton / pallet mix60/40 ÷ 80/20
Replen cycles / week2 – 7

Typical retail / 3PL pain points

What hurts retail and 3PL DCs.

Recurring complaints we hear from operations directors in big-box retail and multi-client 3PL.

Pallet + carton mix

Replenishing a hypermarket needs full pallets for rapid movers and cartons for long-tail. The system has to handle both.

Picking for hundreds of stores

Each store = unique multi-line order with strict delivery window. Manual = constant overtime and errors.

Multi-client in 3PL

Clients with different rules in the same warehouse: KPIs, labeling, cut-off windows. The system must adapt without redesign.

Congested loading docks

Multiple daily departures to stores alternating with supplier inbound. Without orchestration, docks become the bottleneck.

Per-category seasonality

Water in summer, hot drinks in winter, toys in December. Volumes vary 3–5× per SKU by season.

Per-client / per-lot traceability

Recalls, audit, per-brand reporting. Lot and SSCC tracking must be native in the WCS.

Typical flow in a retail / 3PL DC

Five stations. Two speeds.

A retail / 3PL DC operates simultaneously on pallets (volume) and cartons (variety). ZEDlog builds both lanes in the same system.

01
Receiving

Pallets onto MPP, cartons onto MCP. SSCC scan, zone allocation.

MPP · MCP
02
Storage

Pallets on racks with MPP I/O. Cartons on shelves or AS/RS.

MPP · MCP
03
Order picking

Mixed pick — full pallet for fast movers, carton for long-tail.

MCP · WCS
04
Order consolidation

Cartons of the same store aggregated, palletized for delivery.

MCP · MPP
05
Per-store dispatch

Final pallets to dock by route. Auto-generated manifest + ASN.

MPP · WCS

Typical post-implementation results

Numbers we hear from real retailers.

Figures from projects we've completed — variation depends on mix and starting state.

2.5×
picking throughput per labor-hour
−55%
order errors per store
+30%
dock utilization (more frequent windows)
22–32
months payback typical ROI

Portfolio reference

Retail chain · 280 stores · 2024.

Logistics director

"We had two DCs and were thinking of building a third. With the automated system we now ship from one — and have free capacity for another 40% expansion."

Anonymized at client request · Confirmation available for 1:1 meetings
2.1×
delivery throughput same DC
−42%
labor force at picking
18
months from start to go-live
26
months realized payback

Fewer DCs. More throughput.

Send us the profile: store / client count, carton-pallet mix, delivery windows. We return a stack proposal and ROI estimate in 10 days.