Warehouse Order Picking

Global Warehouse Order Picking Market to Reach US$17.2 Billion by 2030

The global market for Warehouse Order Picking estimated at US$9.9 Billion in the year 2024, is expected to reach US$17.2 Billion by 2030, growing at a CAGR of 9.6% over the analysis period 2024-2030. Single Order Picking, one of the segments analyzed in the report, is expected to record a 8.1% CAGR and reach US$9.7 Billion by the end of the analysis period. Growth in the Multiple Order Picking segment is estimated at 11.8% CAGR over the analysis period.

The U.S. Market is Estimated at US$2.7 Billion While China is Forecast to Grow at 12.9% CAGR

The Warehouse Order Picking market in the U.S. is estimated at US$2.7 Billion in the year 2024. China, the world`s second largest economy, is forecast to reach a projected market size of US$3.4 Billion by the year 2030 trailing a CAGR of 12.9% over the analysis period 2024-2030. Among the other noteworthy geographic markets are Japan and Canada, each forecast to grow at a CAGR of 7.0% and 8.4% respectively over the analysis period. Within Europe, Germany is forecast to grow at approximately 7.5% CAGR.

Global Warehouse Order Picking Market – Key Trends & Drivers Summarized

Why Is Warehouse Order Picking Becoming a Priority for Logistics Efficiency?

Warehouse order picking is a crucial component of supply chain operations, directly impacting order accuracy, fulfillment speed, and customer satisfaction. As consumer expectations for fast and accurate deliveries rise, businesses are prioritizing efficient order-picking strategies to reduce operational costs and improve service levels. Traditional manual picking methods are being replaced by advanced solutions such as pick-to-light, voice-directed picking, and robotic picking systems to enhance efficiency. The growth of e-commerce and direct-to-consumer (DTC) shipping has further increased the complexity of warehouse order picking, requiring warehouses to handle high order volumes while maintaining precision. Industries such as retail, pharmaceuticals, and manufacturing are adopting order-picking automation to minimize errors and optimize workflow, ensuring streamlined warehouse operations. As businesses strive to improve last-mile logistics and reduce order processing times, order-picking innovations are becoming central to warehouse efficiency strategies.

How Are Robotics and AI Transforming Warehouse Order Picking?

Technological advancements in robotics, AI, and data analytics are revolutionizing warehouse order-picking processes, making them faster, more accurate, and cost-efficient. AI-driven warehouse management systems (WMS) analyze real-time order data to optimize picking routes and prioritize urgent shipments, reducing picking time and labor costs. Robotic picking systems, including autonomous mobile robots (AMRs) and robotic arms, are increasingly being integrated into warehouse operations, allowing for seamless item retrieval and order consolidation. Vision-guided picking technology enhances accuracy by enabling robots and warehouse workers to identify and select items with greater precision. Wearable technology, such as smart glasses and voice-guided picking headsets, further streamlines order fulfillment by providing hands-free navigation and real-time inventory tracking. As AI-powered automation continues to evolve, warehouse order picking is becoming more adaptive, data-driven, and scalable, improving overall supply chain efficiency.

What Challenges Are Impacting the Adoption of Automated Order Picking?

Despite its advantages, warehouse order picking faces several challenges, including high implementation costs, workforce adaptation, and integration with existing logistics infrastructure. The deployment of automated picking systems requires significant capital investment in robotics, AI, and warehouse automation technologies, making it less accessible for small and mid-sized enterprises. Additionally, integrating order-picking solutions with legacy warehouse management systems (WMS) and enterprise resource planning (ERP) software can be complex, requiring extensive customization. Another challenge is workforce adaptation, as employees need training to operate AI-driven picking technologies effectively. Furthermore, maintaining accuracy in high-mix, low-volume warehouse environments presents difficulties, particularly when dealing with fragile or irregularly shaped items. Addressing these challenges requires cost-effective automation solutions, enhanced training programs, and flexible picking technologies that adapt to diverse warehouse layouts.

What Factors Are Driving the Growth of the Warehouse Order Picking Market?

The growth in the warehouse order picking market is driven by several factors, including the expansion of e-commerce, increasing adoption of robotics in warehouses, and the push for real-time inventory visibility. The rise of omnichannel retailing has intensified the need for efficient order fulfillment, compelling businesses to invest in automated picking solutions that improve accuracy and reduce lead times. The increasing use of AI-driven analytics and predictive demand forecasting has further optimized order-picking processes, allowing businesses to anticipate and manage fluctuations in order volumes. Additionally, the shift towards smart warehouses and digital supply chain integration has fueled demand for real-time tracking and automated sorting technologies. The expansion of urban fulfillment centers and last-mile delivery hubs has also contributed to market growth, as companies seek to enhance distribution efficiency in high-density areas. As warehouse automation continues to advance, the demand for intelligent order-picking solutions is expected to grow, driving further innovation in robotics, AI, and logistics technology.

SCOPE OF STUDY:

The report analyzes the Warehouse Order Picking market in terms of units by the following Segments, and Geographic Regions/Countries:

Segments:
Method (Single Order Picking, Multiple Order Picking); Technology (Automated Storage & Retrieval Systems, Automated Guided Vehicles / Autonomous Mobile Robots, Conveyor Systems, Scanners, Others); Deployment (Cloud, On-Premise); End-Use (Construction, Manufacturing, Retail, E-Commerce, Healthcare, Pharma & Cosmetics, Transportation & Logistics, Others)

Geographic Regions/Countries:
World; United States; Canada; Japan; China; Europe (France; Germany; Italy; United Kingdom; Spain; Russia; and Rest of Europe); Asia-Pacific (Australia; India; South Korea; and Rest of Asia-Pacific); Latin America (Argentina; Brazil; Mexico; and Rest of Latin America); Middle East (Iran; Israel; Saudi Arabia; United Arab Emirates; and Rest of Middle East); and Africa.

Select Competitors (Total 34 Featured) -

• Autostore AS
• Berkshire Grey
• Beumer Group GmbH & Co. KG
• Dematic Corp.
• Honeywell International, Inc.
• Korber AG
• Locus Robotics
• Modula S.p.A.
• Murata Machinery Ltd.
• Oracle Corporation

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