Artificial intelligence has become widely used in warehouses as decision-makers rely on technology to improve everything from worker safety to inventory management. These leaders also recognize the potential of automated material flow applications. Persistent backups can cause widespread, time-consuming bottlenecks, but efficient, uninterrupted operations increase productivity and customer satisfaction.
Read also: How to Find the Best Warehouse Automation System for Your Budget in 2025
Improving Routing and Tracking
Although many executives want to expand their operations and warehouse footprints, such goals can require workers to spend too much time on manual processes. Such was the case for employees at SuperIndo, Indonesia’s largest supermarket chain. The company has almost 200 large and compact stores spread throughout the country, and warehouses play important roles in getting goods to the right destinations at the appropriate times.
However, operating them became increasingly challenging. SuperIndo’s network includes warehouses, traditional distribution centers, satellite distribution centers and stores. During the company’s ongoing expansion, managers would spend up to two hours daily manually planning the routes to and from company locations. Besides being time-consuming, this approach was costly, prompting leaders to find an alternative.
They eventually implemented an artificial intelligence-based automated routing tool to optimize the movement of goods from warehouses to stores. The tool provided instant, digital proof of delivery and the information managers required to follow up if needed. Once the supermarket brand implemented this technology, managers regained 30% of the time formerly spent on manual routing. Additionally, vehicle utilization improved, allowing drivers to make more trips and achieve faster turnaround times without increasing fuel usage.
This case study is an excellent example of how AI in warehouses can support companies’ growth plans while streamlining internal and external operations. Other executives can anticipate similar results by pinpointing the slowest or most error-prone processes and investigating whether technologies can enhance results.
Simulating Automated Material Flow Successes
Warehouses are hotbeds of activity. Keeping goods and people moving through them requires making crucial decisions ranging from aisle width to rack type. Cantilever racks are ideal for keeping products off the floor and accommodating heavy goods, such as furniture. Alternatively, carton flow racking relies on gravity and an inclined roller to push the next item forward once a worker removes the one closest to them. Whether executives want to improve automated material flow in an existing warehouse or start strong in a new location, being able to test multiple possibilities can enable effective processes.
Participants in a three-company collaboration spent half a year training an AI algorithm on 9 trillion parameters and making it interpret physical environments. The project revolved around highly accurate digital twins of warehouses stored in the cloud.
The resulting technology showed users numerous 3D video simulations of the best warehouse layouts for throughput, labor efficiency and other key performance indicators. One of the executives believes this tool could halve planning time frames for warehouses in development while causing similar reductions in manual labor and operating expenses in current facilities.
This application of AI in warehouses also removes the guesswork that can accompany high-level decision-making. Additionally, conditions can shift significantly when the warehouse goes through a holiday rush period versus its slowest season. Adapting the facilities to meet those changing needs can improve material flow patterns by reducing backups, poorly organized workflows or potentially dangerous designs.
Relieving Employees of Repetitive Tasks
People are interested in using AI to free up more time so they can spend it on more rewarding tasks. That is why options such as generative AI have become so popular for warehouse management and similar needs. It allows people to reduce costs while making their supply chains more efficient and competitive. They can use chatbots to simulate different circumstances and determine how they would handle them if they arise. Since most of these tools give answers in seconds, they save people calculation time.
Advanced robots can also streamline operations, but they typically improve material flows. Automated mobile robots can move goods to and from specific locations, saving workers time while reducing injury risks. Other models directly handle the various products that move through warehouses, preparing them to leave those facilities via trucks to eventually reach customers’ doorsteps.
One example is Sparrow, a material-handling machine used by Amazon. It uses machine-vision technologies and AI to handle millions of products, supporting employees who collectively pack more than 13 million parcels daily.
Amazon’s product inventory is incredibly diverse. However, that massive assortment poses challenges for some robots because the items come in various shapes and sizes. Executives knew they had to create innovations that could work with those products at scale. Options like robotic grippers can grab things within the same product category, such as pharmaceutical bottles. However, expecting such machines to work with goods of a drastically different texture or type may lead to disappointment.
Assessing Automated Material Flow Requirements in Warehouses
There are many enticing reasons to begin using AI in warehouses or scale up their existing applications. However, leaders will get the best results by examining current operations and deciding how artificial intelligence could help. If items move in predictable ways through warehouses — such as by passing through specific stations — robots may be able to assist humans with their tasks at each stage.
Perhaps executives want decision-making support before changing the layout of an existing warehouse or building a new facility. Digital twins and other simulation tools could help people determine everything from the best storage systems to the spacing of aisles to enable smooth vehicular and pedestrian traffic flows. Employees can also give valuable input. They will have direct experience of challenges that leaders may be unaware of.
Measuring the Impacts of Automated Material Flow Changes
Once supply chain professionals have decided how to enhance the movement of goods through their warehouses, they should choose metrics to track. Monitoring those specifics will help them gauge how things are going and whether the tweaked processes bring the desired results. Better operational visibility will help people stay more aware of their operations, urging them to continually roll out new AI technologies as needed.
