Global and U.S. Intralogistics Robots Market Report, Published by QY Research.

QY Research has released a comprehensive new market report on Intralogistics Robots, automated robotic systems designed to handle internal material movement, storage, picking, sorting, and palletizing within warehouses, distribution centers, and manufacturing facilities. Intralogistics robots form the physical execution layer of smart logistics, integrating closely with WMS, MES, and ERP platforms to enable high-throughput, flexible, and data-driven material flows. As labor shortages, SKU complexity, and fulfillment speed requirements intensify, this report delivers an in-depth assessment of global market size, robot categories, deployment models, and competitive dynamics.

Core Market Data

Global market size: USD 7.6 billion
CAGR (2024-2030): 18.9%
Average price: USD 18000 per unit
Annual production: 0.42 million units
Gross margin: 36%
Production capacity: 0.62 million units

List of Main Players

Rockwell Automation (NYSE: ROK, USA)
ABB Group (SIX: ABB, Switzerland)
FANUC (TSE: 6954.T, Japan)
OMRON (TSE: 6645.T, Japan)
Daifuku (TSE: 6383.T, Japan)
Toyota (TSE: 6201.T, Japan)
KION Group (XETRA: KGX, Germany)
Teradyne (NASDAQ: TER, USA)
Siasun Robot (SZSE: 300024, China)
Estun Automation (SZSE: 002747, China)
TGW Logistics Group (Private, Austria)
Schaefer Group (Private, Germany)
Amazon Robotics (private, USA)
Locus Robotics (private, USA)
GreyOrange (private, USA)
Hai Robotics (private, China)
Quicktron (private, China)
Seegrid (private, USA)
Hikrobot (private, China)
VisionNav Robotics (private, China)
inVia Robotics (private, USA)
Magazino (private, Germany)
Caja Robotics (private, USA)
IAM Robotics (private, USA)
ForwardX Robotics (private, China)
Mushiny Intelligence (private, China)
Libiao Robotics (private, China)
Eoslift Robotics (private, China)
SLAMTEC (private, China)

1. By Robot Category

Autonomous Mobile Robots (AMRs)
Automated Guided Vehicles (AGVs)
Autonomous forklifts and pallet movers
Robotic picking and piece-handling systems
Palletizing and depalletizing robots

2. By Navigation & Control Technology

LiDAR-based SLAM navigation
Vision-based navigation and AI perception
Magnetic tape / QR code guided systems
Hybrid navigation and sensor-fusion platforms

3. By Payload Capacity

Below 50 kg (piece picking, tote handling)
50-500 kg (case handling and carts)
Above 500 kg (pallet transport and forklifts)

4. By Application

E-commerce and omnichannel fulfillment
Manufacturing intralogistics
Third-party logistics (3PL) warehouses
Cold-chain and pharmaceutical logistics
Retail distribution and spare-parts hubs

Case Study for Bidding

Time: June 2024
Supplier: SSI Schaefer
Buyer: JD Logistics
Product: Integrated intralogistics robot systems (AMRs + pallet movers + control software)
Quantity: 55,000 robots
Contract Value: USD 510 million
Delivery Timeline: Q2 2026

Case Study for End Using

End User: Zara (Inditex)

Application: Automated material handling, order consolidation, and store replenishment in fashion distribution centers.

Installation Location / Usage Stage

Deployed across inbound, storage, picking, and outbound zones.
Integrated with WMS and demand-forecasting systems.
Operates continuously with dynamic task allocation.

Purpose

Accelerates order picking and replenishment cycles.
Reduces manual transport and congestion.
Enables rapid response to fast-changing SKU demand.

Effect

Increased order throughput by 40-60%.
Reduced labor dependency and peak-season bottlenecks.
Improved inventory accuracy and on-time delivery rates.

Alternative Intralogistics Solutions
Manual material handling offers flexibility but is labor-intensive and error-prone.
Fixed conveyor systems provide high throughput but lack layout flexibility.
Traditional AGVs are reliable but slower to reconfigure.
AS/RS systems maximize storage density but require high upfront investment.

Therefore, Intralogistics Robots are becoming the operational backbone of modern warehouses and factories, enabling scalable automation, real-time adaptability, and data-driven material flows-supporting the evolution toward smart logistics, resilient supply chains, and Industry 4.0 manufacturing.

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