Logistics Automation Market Projected to Reach US$ 79.80 Billion by 2030 at a CAGR of 10.98%
Logistic Automation Market Size Was Valued at USD 31.10 Billion in 2022, and is Projected to Reach USD 79.80 Billion by 2030, Growing at a CAGR of 12.5% From 2023-2030.
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Logistics automation refers to the application of technology and automated systems to improve the efficiency, accuracy, and reliability of logistics operations. This includes a wide range of technologies such as robotics, artificial intelligence (AI), machine learning, Internet of Things (IoT), and automated vehicles. The primary objectives of logistics automation are to streamline processes, reduce operational costs, minimize errors, and enhance overall supply chain management. Key areas where automation is applied include warehouse operations (e.g., automated storage and retrieval systems), transportation (e.g., autonomous vehicles and drones for last-mile delivery), inventory management (e.g., RFID technology for real-time tracking), and order processing (e.g., automated order picking and packing).
Key Prominent Players In The Logistic Automation Market:
· Dematic (US)
· Honeywell Intelligrated (US)
· Manhattan Associates (US)
· Mantis (US)
· Blue Yonder (US)
· Oracle (US)
· IBM(US)
· Zebra Technologies (US)
· O9 Solutions (US)
· JR Automation (US)
· Automated Logistics Systems (US)
· Symbotic (US)
· Locus Robotics (US)
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Global Logistic Automation Market Segmentation:
· Hardware
· Software
· Services
By Function
· Inventory and Storage Management
· Transportation Logistics
By Logistics Type
· Sales Logistics
· Production Logistics
· Recovery Logistics
· Procurement Logistics
By Organization Size
· Large Enterprise
· Small and Medium-Sized Enterprises (SMEs)
By Software
· Inventory Management
· Order Management
· Yard Management
· Shipping Management
· Labour Management
· Vendor Management
· Customer Support
· Transportation Management
· Order Management
· Warehouse Management
By Mode of Freight Transport
· Air
· Road
· Sea
By Application
· Transportation
· Infotainment System
· Safety and Alerting System
· Others
By End-User
· Manufacturing
· Healthcare and Pharmaceuticals
· Fast-Moving Consumer Goods
· Retail and E-commerce
· Automotive
· Others
Geographic Segment Covered in the Report:
· North America (US, Canada, Mexico)
· Eastern Europe (Bulgaria, The Czech Republic, Hungary, Poland, Romania, Rest of Eastern Europe)
· Western Europe (Germany, UK, France, Netherlands, Italy, Russia, Spain, Rest of Western Europe)
· Asia Pacific (China, India, Japan, South Korea, Malaysia, Thailand, Vietnam, The Philippines, Australia, New Zealand, Rest of APAC)
· Middle East & Africa (Turkey, Bahrain, Kuwait, Saudi Arabia, Qatar, UAE, Israel, South Africa)
· South America (Brazil, Argentina, Rest of SA)
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This study conducts a SWOT analysis to evaluate the strengths and weaknesses of key players in the Logistic Automation market. Additionally, the report performs a complex inspection of drivers and restraints operating in the market. The report also evaluates the observed trends in the parent market along with macroeconomic indicators, dominant factors and market attractiveness in relation to other segments. The report forecasts the impact of various industry aspects on the Logistic Automation market segments and regions.
Logistics Automation Market Trend Analysis:
Quality Control and Assurance Requirements:
E-commerce Expansion: The rapid growth of e-commerce has significantly increased demand for efficient logistics solutions. Automation technologies such as automated order fulfillment systems, robotic picking systems, and automated sorting systems are essential to meet the demands for faster order processing and delivery times.
Technological Advancements: Advances in robotics, artificial intelligence (AI), machine learning, and Internet of Things (IoT) are driving innovation in logistics automation. Robotics are increasingly used in warehouse operations for tasks like picking, packing, and palletizing, while AI and machine learning optimize routing, inventory management, and predictive maintenance.
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