Artificial intelligence (AI) is increasingly playing a crucial role in warehouse management, specifically after the post-pandemic period, with AI-powered solutions being used to achieve higher levels of optimization, accuracy, and efficiency in warehouse operations. This article discusses the major benefits and applications of AI technology in warehouse management.
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Benefits of AI in Warehouse Management
AI techniques, such as machine learning (ML), neural networks (NNs), deep learning (DL), computer vision, and speech and visual recognition, can be used to increase the effectiveness and consistency of warehouse management, which primarily involves demand planning, processing and packaging, material handling, and inventory management. Thus, integrating AI into warehouse management can lead to several benefits for organizations.
Improved Operational Efficiency and Inventory Management: Human error is one of the major reasons for equipment failure and product damage. Additionally, a lack of coordination from a human employee can hinder one or several warehouse operational workflows.
AI technology can be utilized for warehouse automation to address these issues as advanced algorithms used in AI-powered solutions can coordinate various tasks in a seamless manner. Moreover, AI solutions can assist businesses in improving resource and operational planning by standardizing client, staff, and asset management.
AI-powered custom inventory management software can be used for efficient warehouse management as the software can calculate large inventory datasets. Additionally, AI-operated systems can easily maintain an adequate supply-demand ratio without supply chain disruptions, identify customer preferences and purchase patterns, and positively impact the operational workflow.
Moreover, AI-powered robots can be used for better inventory management within warehouse facilities, with several robots being programmed to scan, tag, and sort products for distribution and storage, leading to improved stock control as the systems update and count inventory records automatically.
Enhanced Productivity: AI-based solutions can be employed to monitor warehouse productivity and realize productivity gains. Custom AI solutions operating 24/7 can improve pick-and-pack procedures, allow the scanning of digital tags for thorough inventory management, and identify the quickest routes for product transportation.
AI technology can also proactively predict performance, identify supply chain challenges, reduce turnaround times to improve customer order fulfillment, and provide tools to ease the interaction between employees and warehouse equipment to increase productivity.
Additionally, AI-powered robots can be utilized for material handling, including transporting, packing, and picking different loads within the warehouse, handling repetitive tasks without fatigue following a specific set of routes/instructions, and performing all operations with similar speed and efficiency. Automated material handling can significantly increase productivity as robots can handle more loads than humans, enabling companies to establish a robust supply chain.
Faster Shipping and Storage Optimization: In a conventional warehouse, order processing is an extremely time-consuming task as associates spend a long duration moving the warehouse floor to fill orders and pick products. Smart AI-based warehouse management software can eliminate picking errors.
Specifically, Interactive robots and automated picking methods can reduce the order picking time and expedite order shipping, which is crucial in the era of e-commerce as shipping time is a critical factor for customers along with the product price. Thus, companies can gain a competitive edge and realize greater customer satisfaction through faster shipping.
Storage optimization is crucial for curtailing costs in scale-driven businesses. AI models can be effectively used for storage optimization as AI-driven automated processes can improve storage usage. These models make judgments on lean storage using data about the shipping fleet, containers, storage units, and other assets.
Higher Safety: Ensuring safety is the most critical requirement in warehousing. Warehouse activities, such as handling hazardous products by human employees in confined spaces, unnecessary human movement leading to workplace injuries/collisions, and routine handling of heavy loads, can be categorized as high-risk, medium-risk, and low-risk.
NNs and ML can be used to improve the technical and cognitive capabilities of robots and cobots that can be customized for use in hard-to-reach locations within the facilities, programmed to transport fragile or hazardous products with high levels of accuracy, and leveraged to decrease the material damage risk or collision-related accidents.
Additionally, advanced image processing and computer vision techniques can facilitate the utilization and development of autonomous mobile robots (AMRs) and autonomous guided vehicles (AGVs) for warehouses. These systems do not require human oversight and can independently move within the warehouse, significantly reducing risks.
AGVs navigate following predetermined paths or tracks, preventing collision with different obstacles in the warehouse. The route and operation planning of AMRs depends on several AI technologies and sensors for collision avoidance and detection. AMRs do not need a predetermined track for navigation as AGVs.
Thus, companies increasingly use AMRs and AGVs to optimize warehouse spaces and increase warehouse safety. Moreover, AI-based warehousing equipment can be utilized to perform autonomous risk assessments in the facility by collecting the required data from the installed sensors, analyzing warehousing work patterns and activities, and assigning risk scores for all warehousing activities.
Such safety assessment is crucial for workforce planning as the company understands the risk levels for various activities. AI can also facilitate dynamic slotting to effectively distribute high and medium-risk work among robots and low-risk work among human employees.
Modern virtual reality (VR) and augmented reality (AR) devices using AI technology can be used to improve the safety training provided to warehouse employees. Safety training is crucial to minimize injuries and risks in warehouse facilities. Companies can develop and distribute scenario-based standard operating procedures and instructional videos using these platforms.
Major Applications
In-warehouse Travel Optimization: Reducing the traveling by warehouse workers within the facility using AI-based travel reduction can increase productivity in pick-to-pallet operations and piece-picking applications.
AI and ML systems use substantial warehouse process data to learn to reduce travel through intelligent pick sequencing and order batching and balance priorities considering the slow-moving routes and common congestion areas.
Performance Management: Labor management systems based on standards, such as Engineered Labor Standards (ELS), are conventionally used in warehouses. AI can eliminate a major part of the labor-intensive data collection process in ELS-based performance management using learning algorithms to predict the required time to complete tasks.
AI algorithms can learn using real-world performance data, including several variables, such as work type and area, user, starting travel location, ending travel location, and product and quantity to be handled, obtained from warehouse operations. The predicted expectations and results are more accurate than conventional systems.
Dynamic Slotting: Although proper product slotting can positively impact labor productivity, accuracy, and productivity, achieving it is a challenging task as slotting is both a combinatorial optimization problem and a multiple-objective optimization problem.
Traditional slotting solutions need the installation and maintenance of customized models and extensive measurement, data collection, and engineering. AI can eliminate a significant part of manual warehouse mapping, data inputs, and engineering work required for conventional slotting systems.
AI-based software/solutions can learn the travel time predictions and spatial characteristics needed for a slotting model based on activity-level data obtained in the warehouse, and the learned model can adapt to changing conditions to ensure continuous optimization.
Predictive Picking: Predictive picking is one of the major potential applications of AI in warehouses that use data to predict the items that will be ordered next. These predictions can then optimize the picking process to ensure that the commonly ordered products are picked and packed first.
Predictive picking can shorten the request fulfillment time by selecting the commonly ordered products first, leading to faster deliveries and greater customer satisfaction. Predictive picking can also reduce costs significantly, specifically in large warehouses, by decreasing the resources and manpower required to improve the picking process.
Moreover, anticipatory picking can improve accuracy as warehouses can reduce the possibility of picking the wrong product/quantity by picking the commonly ordered products first, allowing companies to avoid expensive mistakes and ensure the delivery of correct products to the clients.
Reference and Further Reading
Tu, X. (2023). Artificial Intelligence in Warehouse Management: How Predictive Picking is Transforming the Industry. [Online] (Accessed on 03 November 2023)
5 applications for artificial intelligence in the warehouse and distribution center [Online] (Accessed on 03 November 2023)
How Artificial Intelligence is Changing Warehouse Operations [Online] (Accessed on 03 November 2023)
Increased Productivity And Other Benefits Of Using AI In Warehouse Automation [Online] (Accessed on 03 November 2023)
9 Ways AI Can Modernize Warehouse Management [Online] (Accessed on 03 November 2023)
Andiyappillai, N. (2020). Digital transformation in warehouse management systems (WMS) implementations. International Journal of Computer Applications, 177(45), 34-37. https://www.researchgate.net/publication/339986803