Advancements in technology have revolutionized many sectors of the economy, including the material handling industry. Implementing artificial intelligence (AI) technologies in material handling has significantly improved efficiency, productivity and safety.
Material handling technologies cover various equipment and systems, such as conveyors, lift trucks, automated storage and retrieval systems, cranes, sorting systems and more. Integrating AI technologies with these systems and processes largely involves automation, reducing manual labor or input.
Autonomous mobile robots (AMRs) are self-guided robots that can move around a warehouse or factory floor without human intervention. AMRs can transport, sort and store goods, saving time and increasing productivity. Unlike traditional automated guided vehicles — which require physical infrastructure such as wires, magnets or reflectors for navigation — AMRs use sensors, cameras and mapping algorithms to navigate the environment. A core element of their successful navigation and processing is thanks to AI and machine learning.
AMRs can also work around the clock without taking breaks, which means they can operate during non-working hours and weekends, leading to continuous and uninterrupted material handling. AMRs can adapt to changing layouts or environments, making them a more reliable solution for commercial and industrial settings.
More importantly, they improve working conditions for those doing similar work, especially regarding carrying or moving bulky items across great distances. When combined with robotic process automation or intelligent automation, businesses can deploy solutions far more advanced than just one of the technologies alone.
You can find an example of the implementation of AMRs within Amazon's warehouses. Amazon uses thousands of Kiva robots — now known as Amazon Robotics — to automate packaging and picking. The Kiva robots can autonomously navigate to different shelves, pick up inventory, and transport it to human workers for packing and shipping. Implementing these robots has enabled Amazon to process orders faster and at a lower cost. It has also secured AMRs as one of the most valuable material-handling technologies.
Predictive analytics is an AI technology that uses statistical algorithms and machine learning to analyze data and forecast future outcomes. In material handling, predictive analytics optimizes inventory management by forecasting demand, identifying trends and improving order fulfillment. It helps gain a clear understanding of the current landscape, both internally and externally. It is largely a data-driven form of material handling technologies, and may or may not require access to an increasingly large store of digital information, such as an operation’s primary data center.
Predictive analytics can analyze various data sources — such as historical sales data, customer behavior, weather patterns, or social media sentiment — to predict product demand. The predictions can help businesses optimize their inventory levels, reduce waste, and avoid stockouts or overstocking. Furthermore, the information can help plan and prepare for unexpected events, such as a sudden economic downturn, natural disasters and more.
Predictive analytics can also help businesses optimize their order fulfillment processes by identifying bottlenecks or inefficiencies. For example, if the analytics show a particular product is often out of stock, the company can adjust its ordering or production schedules to meet the demand.
The benefits of predictive analytics also extend to equipment and tools within the industry. Heavy machinery — like a forklift — requires a regular maintenance schedule, whether it’s something as simple as changing fuel and fluids, charging the battery or ensuring there is proper lubrication for the internal components.
Preventative or predictive maintenance can be enabled with the help of AI to catch issues before they become costly failures or malfunctions. It might identify when a system is overdue for a checkup or when the next scheduled checkup should occur, and it can keep tabs on regular maintenance tasks. Altogether, this extends the life of the equipment and keeps them in proper working order.
The automotive industry is a massive proponent of predictive analytics. They use the technology to optimize their supply chain and production processes. It helps forecast the demand for each car model, the availability of parts and materials, and the production capacity of each plant. The analytics can also predict the impact of unexpected events — such as natural disasters or labor strikes — on the supply chain. By using predictive analytics, automotive manufacturers can adjust their production and inventory levels to meet changing demands and reduce costs.
Machine vision is an AI technology that enables machines to see and interpret the environment using cameras, sensors and algorithms. In material handling, machine vision is used to identify, track and sort products accurately, reducing the risk of errors and increasing the efficiency of auxiliary technologies. An AMR equipped with machine vision can better serve employees, making the environment safer and helping them organize and process a warehouse, inventory storage location and other areas.
Machine vision can detect various product attributes — such as size, shape, color and texture — or it can read barcodes and RFID tags, using this information to sort them into different categories or destinations. Machine vision can also identify defects or damage in products, which can be flagged for inspection or removal.
A company called Neurala — in partnership with IHI Logistics and Machinery — has announced a tool that can automatically identify a product’s expiration date and validate where the expiration has printed on the packaging. This reduces the potential for errors or misprints in products where expiration is a significant concern and helps improve general workflow.
A high-level example of the implementation of machine vision is in the parcel delivery industry. Delivery companies like FedEx and DHL use machine vision to sort and track packages. As parcels move on conveyor belts or through chutes, cameras capture images of each one and analyze them for their destination, size and weight. The sorting system can then divert each package to the correct chute or container, ensuring accurate delivery. It also speeds up the entire shipping process, kicking back a benefit to customers.
Collaborative robots — or cobots — work with human employees in a shared workspace. As you might have guessed, they’re powered by advanced intelligence solutions like AI and ML. Cobots are equipped with sensors and safety features that enable them to detect and avoid collisions with humans, making them a safe and flexible solution for material handling.
Cobots can perform various tasks — such as lifting heavy objects, handling hazardous materials, or performing repetitive motions — that are difficult or unsafe for human workers. They can also work close to humans, reducing the need for physical barriers or safety zones. Cobots handle many of the dirty or unsafe tasks that humans should not.
But they’re also used in many fields — not just the risky ones — including the food processing industry. Food processors use cobots to handle perishable products like fruits or vegetables that require precise or tempered handling and quality control. Cobots can sort, inspect, and package products without damaging or contaminating them, ensuring food safety and quality.
AI Augments and Improves Material Handling Technologies
In reality, implementing AI technologies in material handling has significantly improved efficiency, productivity and safety. Autonomous mobile robots, machine vision, predictive analytics and collaborative robots are four AI-driven technologies that can enhance material handling.
By implementing these technologies, businesses can optimize their inventory management, reduce costs, and improve the quality and safety of their products. As AI technologies evolve, so will material handling, thanks to its intersection of modern technology, manual processes and standard operations. Companies will always look for ways to be better, wiser and more efficient — AI certainly provides all that and more.
Emily Newton is the Editor-in-Chief of Revolutionized. She regularly covers trends in the industrial sector.