AI in Warehouse Management: Impacts and Use Cases

Warehouses have become very exciting places in recent years. As ecommerce keeps growing, it brings with it a race for ever-faster delivery times and, for traditional retailers, pressure to keep their store shelves well stocked to compete. Those pressures have turned finished goods warehouses and distribution centers into the front lines for businesses to gain operational efficiency, enable customer satisfaction, and respond to shifting market dynamics. To keep pace, warehouse management systems (WMS) have seen an influx of intelligent technologies including artificial intelligence that help bring more visibility, accuracy, cost savings, and speed to fulfillment operations.

What Is AI in Warehouse Management?

The primary use of AI in a warehouse management system is at the fulfillment stage—helping optimize product placement and picking patterns, train and control robots, identify at-risk orders, and assist in other fulfillment center workflows to raise output and employee performance. AI also plays a role in helping businesses improve demand forecasting, respond to market fluctuations, optimize inventory levels, and plan warehouse operations more effectively.

Key Takeaways

• Inventory management, order fulfillment, and forecasting are among the top areas where AI can help improve accuracy and efficiency in warehouses.
• AI can help improve productivity and order processing speed by learning customer ordering patterns and recommending that items commonly picked together are stored close to each other in the warehouse.
• Cloud-based applications and data analytics help measure the ROI of AI initiatives and support continuous improvement efforts.

AI in Warehouse Management Explained

Warehouse management software encompasses the functions and capabilities that businesses use to manage inventory, material handling, and fulfillment operations within a warehouse or distribution center. The increasing maturation of artificial intelligence (AI) technologies brings more opportunities to hone warehouse operations. In a warehouse setting, these technologies can help bridge the gap between the physical and digital worlds, supporting significant changes across warehouse operations. According to a 2023 report by Accenture, 96% of executives indicated that the merging of information technology and operational technology will have a transformative impact on their industries over the next 10 years. That IT/OT convergence is already happening in highly efficient fulfillment warehouses, in such forms as sensors on equipment for predictive maintenance and robotic picking systems backed by real-time order and inventory data.

More broadly, the primary business goal when integrating AI into warehouse management processes is to help make those processes more efficient and accurate. AI can process large amounts of data at a rapid speed to perform tasks to help predict shipment lead times, detect equipment anomalies that might signal a pending breakdown, or forecast demand and compare it to inventory and incoming shipments to gauge supply. AI models also can recommend the best routes for product picking and shipping, helping optimize the way employees work and helping improve delivery times.

AI Impacts in Warehouse Management

The proliferation of AI in warehouse management could have far-reaching benefits for organizations that can reimagine their operations to take advantage of this new technological wave.

• Productivity: In a warehouse setting, productivity is the guiding star. It determines the cost and speed of a warehouse operation, which—along with accuracy—is a key measure of operational success. Nearly every technology investment or process improvement is meant to boost throughput in a warehouse, and AI can help power faster, less labor-intensive order processing. For instance, AI can support dynamic slotting and putaway to help reduce picking times and improve productivity. AI-driven slotting algorithms can continuously suggest optimized item placement within warehouses based on demand patterns and item popularity, such as recommending that higher-demand items are placed closer to shipping and packing areas.
• Order accuracy: Order accuracy is essential in fulfillment warehouses and distribution centers. Using computer-vision and image processing systems that scan every item picked, AI-powered systems can help spot mistakes such as mislabeled packages or incorrect product selections and identify orders at risk of being late. This allows warehouse managers to escalate issues and inform customers about potential delays. AI is also beneficial to warehouse automation, with algorithms that can help direct autonomous mobile robots to move items in less time and with better accuracy.
• Forecasting: In fulfillment warehouses, forecasting is a means of maintaining the right amount of inventory, even through seasonal cycles and market demand shifts. Typically referred to as demand forecasting, this process is becoming increasingly linked to AI, which can learn trends and predict spikes in demand to help companies keep the right type and quantity of products in stock. An AI-powered WMS can help detect and notify warehouse managers about out-of-stock items, helping to maintain inventory levels and avoid under- or over-stocking. AI can learn seasonal demand patterns—such as back-to-school, Halloween, and Christmas spikes—to forecast which items will likely be in high demand for those particular periods.
• Processing: Warehouse managers are constantly trying to cut the time it takes to receive an item on a pallet, stow that item on a shelf, and efficiently pick and process that item when it’s bought to fulfil an order. AI can help in this effort by learning customer ordering patterns and providing insight about which items should be placed closer together. Warehouse employees can then assemble bundles of items that are commonly ordered together without traversing from one end of the warehouse to the other. AI can also be used to pull order details from invoices and shipping notices to reduce manual data entry and help speed up order processing.
• Footprint planning: Just as feng shui can help you design a balanced living space, AI can help to optimize warehouse layouts to maximize workflow and picking efficiency. This involves planning warehouse infrastructure to minimize the space to be rented or bought, stocking related items closer together for faster order processing, mapping out optimal picking routes to avoid traffic jams on the warehouse floor, and facilitating the efficient flow of materials and equipment.
• Equipment lifetimes: AI should get smarter the more it’s used and the more data it ingests. In terms of monitoring the health of equipment and machinery in a warehouse, AI can observe variations from the norm in heat, vibration, throughput, or other factors. Detecting such anomalies with AI can make it easier to spot equipment malfunctions or failures before they arise or the situation worsens. As a result, warehouse managers can arrange for preventative maintenance work orders before a machine breaks down, which can help maintain productivity and output while potentially extending equipment lifetimes.
• Safety: Working in a warehouse can be physically demanding, so taking steps to promote employee safety and reduce the risk of injury is a big priority for businesses. AI that’s trained on data from a warehouse’s safety protocols can help determine when and where it's safer for machines to maneuver and manage tasks versus warehouse workers. AI can also help enact ergonomic and safety practices as part of warehouse management, such as recommending that heavy items not be placed high up overhead.
• Visibility: Layering AI on top of business data can expand visibility into operations, which business leaders can then use to drive greater efficiency across processes such as inventory and order tracking. With capabilities for demand forecasting and predictive maintenance, AI can also help support decision-making around production, procurement, and capital expenses. Additionally, AI tools can continuously monitor warehouse operations to help identify inefficiencies, bottlenecks, and areas for improvement.
• Reduce costs: AI can help warehouse managers gain insights to on how to lower costs across several areas, including energy and resource usage, labor utilization, and inventory management. For instance, AI-powered computer vision and sensor-based systems can adjust warehouse lighting and temperature based on activity in a given space. Achieving greater efficiencies in these processes helps minimize waste and ultimately lower operational costs. Automation can also help reduce costs, trusting autonomous robots and sorting machinery to handle repetitive, routine tasks such as picking, packing, and sorting while redirecting employees to more strategic work.
• Sustainability: Operating a warehouse requires a lot of electricity, so many businesses are deploying AI to help them operate more sustainably. AI systems can help manage energy consumption by controlling lighting, heating, and cooling based on real-time activity in different areas of the warehouse. For example, AI can help reduce electricity usage by dimming the lights in spaces that aren’t in use or adjusting temperature settings during off-peak hours. AI-driven efficiency in inventory management can also help reduce energy usage during the picking process.

AI Technologies in the Warehouse

There are many AI-based technologies making an impact on warehouse operations. Here are a few technologies that are already helping enable automation, drive efficiencies, and support better decision-making.

• Picking robots and autonomous mobile robots: Autonomous mobile robots (AMRs) are designed to move items around a warehouse in less time, with greater ease, and with higher accuracy compared to warehouse employees, by using AI to recommend the best path rather than a preprogrammed route. These robots can also handle tasks that could be difficult or unsafe for warehouse staff, such as retrieving items from high shelves.
• Predictive maintenance: Predictive maintenance uses AI to help identify machinery issues before they become major problems. Alerting employees to take action before a breakdown occurs can help improve equipment reliability, minimize downtime, and reduce repair costs. Sensors are often used to monitor robots and equipment in real time, collecting and streaming data into a WMS where it can be analyzed against historical data to spot irregularities. The AI’s job is to help determine if a data point counts as an irregularity and whether it signals a potential issue or looming malfunction.
• Smart warehousing: Smart warehousing is a concept that encompasses many of the AI-powered capabilities deployed in a warehouse management system. This includes using AI to optimize warehouse layouts and item placement or identify seasonal demand fluctuations for informed inventory planning. Smart warehousing also covers the use of Internet of Things technologies such as RFID tags and equipment sensors to help streamline item scanning and tagging, among other processes, and to monitor equipment and robot performance to enable predictive maintenance.
• Computer vision: Computer vision is a type of AI that helps autonomous mobile robots to navigate warehouses, avoid obstacles, and transport inventory without human intervention. It can also recognize and classify packages or materials, helping to streamline sorting processes and verify that items are sent to the right place for further order processing. Cameras and image recognition systems can scan barcodes or QR codes on products in real time, allowing for automated inventory updates and can help reduce risk of human error.
• Natural language processing: Natural language processing (NLP) is a type of AI that can be used in a WMS to summarize documents and automate communication and data entry tasks. NLP-powered digital assistants can use shipment data from a warehouse system to help perform some customer service functions, providing real-time order updates and responses to customer queries. NLP can also automate data extraction from shipping notices, invoices, delivery receipts, and other documents to help reduce manual errors and speed up processing times.
• Intelligent forecasting and predictive analytics: The combination of AI, data analytics, and cloud applications enables intelligent demand forecasting and inventory planning in warehousing operations. With help from AI-supported forecasting and predictive analytics, businesses can manage the raw materials and finished goods in their warehouses in line with demand trends and seasonality. Predictive analytics can also help calculate how long it will take to pick, pack, and ship an order, thus providing more accurate time estimates for order completion. These predictions can also help flag orders at risk of being delayed or missing expected service levels and shipping windows, leading to better order fulfillment.

Implementation Challenges of Warehouse AI

Only 16% of organizations say they’re unlikely to adopt AI technologies within the next five years, according to a report published by logistics and supply chain association MHI. While most logistics and supply chain businesses have already started to map out some uses for AI, many are still hesitant to commit due to the various unknowns around this technology. Companies face several notable challenges implementing warehouse AI.

• Data privacy and security: Implementing AI involves handling large amounts of data, which can raise concerns about compliance around data security and privacy—especially within industries that handle sensitive data, such as finance and healthcare. Businesses are responsible for ensuring that their AI tools adhere to acceptable data privacy and security policies. This requires an understanding of encryption, anonymization, access control, and other safeguards, as well as how customer data may be used to further train a provider’s AI models.
• Workforce adoption: Implementing AI in warehouse management will likely challenge longstanding approaches that employees are accustomed to. Employees may fear that AI could take their jobs. The technical knowledge requirements could also pose a concern. As businesses consider whether they have the right workforce to operate alongside AI tools, employees may worry about what skills they’ll need to learn to stay on the job.
• Technical complexity: Integrating AI models with legacy systems and software can be complex and costly, requiring significant IT efforts to update or replace outdated technology. Cloud-based AI tools can help reduce this complexity and bolster an implementation’s success. However, AI systems still require robust data integration strategies to pull data from across an organization into one centralized platform for analysis.
• ROI realization: It can be difficult to quantify the exact return on investment from AI initiatives in the warehouse, especially in the short term. Without clear ROI metrics, businesses may struggle to justify spending on AI technology. Companies may need to develop new performance metrics and goals to demonstrate AI’s value in a warehouse environment.

Strategies for Successful Implementation of Warehouse AI

A successful AI implementation in warehouse management starts with support from company leadership, with executive sponsors championing the change. From there, it’s time to map out what the implementation will actually look like, including specific goals, technologies, and metrics to measure success.

1. Establish goals: What concrete benefits do you hope to achieve with your AI implementation? Identify optimal, achievable outcomes that you can quantify, such as faster order fulfillment and lower costs. Figure out what the most pressing business problems are and whether AI can help solve them.
2. Identify the right technologies: AI encompasses a broad range of capabilities, with different use cases and outcomes achievable within each subset of this technology. For example, there’s machine learning for predictive analytics, natural language processing for chatbots and text analysis, and computer vision for image recognition. To accomplish your desired outcomes, consider matching your established business goals with the right functionality.
3. Run a pilot test: Once you’ve adopted an AI tool, minimizing operational disruption may include testing it in a controlled environment or single warehouse before scaling to high-volume deployment. This pilot test enables, you to identify potential issues and make necessary adjustments without a ton of risk. It’s also an opportunity to gather feedback from users and other stakeholders to determine whether the system can actually meet your business needs. A caveat: Consider only testing systems that you’re confident can scale quickly if they pass.
4. Develop a roadmap: An AI warehouse implementation is not a one-time project with a hard ending. Rather, it’s an ongoing effort that requires a long-term strategy. Establishing a roadmap can help your business visualize how the implementation will evolve to meet future objectives, including additional technology investments and strategies for scaling across the organization.
5. Assess and measure improvements: To measure the success—including the small victories—of an AI implementation, clear metrics that track progress around key business goals such as faster order processing, cost savings, higher accuracy, and total order throughput are beneficial. As well as helping you capture short-term gains, these metrics can also help you assess the long-term strategic value of AI such as improved customer service or faster rollouts for new products.

AI and Warehouse Management Future Trends

The future of AI in warehouse management will likely remain focused on increasing automation, improving productivity, lowering costs, increasing accuracy, and enhancing decision-making. Robotics, material handling equipment, and warehouse automation systems should continue to evolve with more advanced capabilities, such as AMRs that can adapt to changing environments in real time. AI may also play an increasingly vital role in inventory management and demand forecasting within warehouses, helping to detect stock levels, identify trends, and predict future needs with more accuracy. The use of AI to support sustainability initiatives could be an ongoing focus as well, such as warehouse strategies to help reduce product spoilage or lower transportation emissions. Finding ways to continue improving warehouse task efficiency with wearable technology is also a likely innovation driver.

Optimize Warehouse Productivity with Oracle

AI is evolving fast, but there are real and executable warehouse management AI options available to implement today. Gains in productivity and throughput are just the start of the improvements that AI may bring to warehouse and fulfillment operations.

Oracle Fusion Cloud Supply Chain & Manufacturing (SCM) offers an integrated suite of applications that cover the supply chain, including planning, logistics, and procurement. Oracle Fusion Cloud Warehouse Management, part of Oracle Fusion Cloud SCM, provides cloud-based inventory management, warehouse optimization, and labor management. It also supports advanced fulfillment processes including robotics and automation integration, predictive analytics, and automated storage and retrieval. Leveraging AI and machine learning, Oracle Warehouse Management offers predictive analytics to help forecast demand, inventory levels, and labor requirements for proactive decision-making. As a cloud-based solution, Oracle Fusion Cloud SCM is scalable across an organization, with regular updates that grant access to continuous innovation, such as AI and GenAI capabilities.

AI in Warehouse Management FAQs

How is AI being used in warehouses?
The top use cases for AI in warehouses are to support order fulfillment and inventory management, two areas that include the most important workflows in warehouse operations.

Can AI be used for inventory management?
Yes, AI is used for inventory management in many warehouse management systems, providing automation, real-time visibility, and optimization capabilities.

What is a smart warehouse management system?
A smart warehouse management system is a software application that combines technologies such as AI, robotics, Internet of Things, and automation to help improve the operations of a warehouse or fulfillment center.