While Artificial Intelligence (AI) and digital twins have made headlines in recent months, the actual applications of these technologies might leave supply chain professionals scratching their heads about whether they have what they need to employ the technologies and even how to apply them in real-life.  The good news is that most facilities already have the required data they need — it’s just buried in their warehouse management system (WMS). And integrating AI and digital twins is actually quite useful and simple. Here are five ways these technologies can revolutionize warehouse operations in 2024. 

1 Optimizing Warehouse Layout and Design 

Advanced technologies like digital twins and AI make it much easier to optimize warehouse layout and design. It’s next to impossible for anyone to attempt such tasks by trying to visualize a warehouse layout in their head. Calculations based on regression or optimization models are also difficult for warehouse managers to use to optimize operations. 

But put the visualization of a 3D digital twin at their disposal, and warehouse managers can easily understand the implications of moving racks around and changing slotting. Showing users warehouse hotspots — areas where picking activity is heightened — makes decisions about changing facility configurations and the deployment of labor to optimize warehouse operations quicker and easier. Users only have to look at a dashboard to see the results. 

In the past, these functions were performed by warehouse consultants and engineers. With the advancement of technologies such as digital twins, AI, advanced analytics and natural language processing, decision-making tools are put in the hands of warehouse managers. 

Visualizations allow managers to experiment with diverse warehouse layouts and different numbers of workers in different zones to understand how changes impact productivity. Natural language generation adds narrative information, to provide greater understanding to users by highlighting issues that aren't obvious from the picture.  

2 Optimizing Picking and Packing Operations 

In the past, when it came to grappling with optimizing warehouse picking and packing, all of the data that was available for comparison was historical. That was helpful up to a point, but it became less relevant when current reality diverged from historical averages. 

Digital twins, powered by AI, allow warehouse managers to make decisions based on real-time data, which has implications for many areas of warehouse operations. For example, managers may find that electric forklifts need to be charged sooner than expected. That may happen because the equipment routing wasn’t optimized, or because workers are deviating from their assigned routes. Access to real-time data provides insights into these types of issues. 

Conveyor speeds are other factors that can be optimized with real-time data. The digital twin provides a connected environment in which the sorter knows what the conveyor is doing, what the forklift drivers are up to, and where the pickers are. Access to that kind of data and analysis, and the resulting ability to synchronize different areas of warehouse activity, empower managers to make the changes that can boost warehouse productivity by 20%, 30% or even 40%.  

3 Predicting and Preventing Equipment Failures 

WMSs collect a wealth of data, such as transit times for forklifts, carts and robots. With the analytical capabilities of AI, WMS data can be extrapolated to create a predictive model to inform warehouse managers of the need for equipment maintenance and repairs, thereby going a long way to prevent equipment failures.  

That capability can come in handy if, for example, forklifts are working longer hours than expected. Warehouse managers may have expected their forklifts to work seven hours a day. But if data analysis reveals they are actually working eight hours a day, the equipment is going to need maintenance more often than originally predicted. Knowing that information, and adjusting schedules accordingly, can avert disruptions.  

Operational plans, in warehouses and elsewhere, are always based on assumptions. But when real-time data analysis challenges those assumptions, plans can be revised to optimize operations. 

4 Improving Workforce Planning and Scheduling 

Picking and replenishment performance is intimately tied to worker productivity. Digital twins and AI enable warehouse managers to drill down into how employee behavior is impacting facility efficiency.  

For example, a warehouse may have an average pick rate of 220 items per hour per employee. If a specific worker is at the 160 level, the shift supervisor and the management team are going to want to figure out why.  

Sometimes the employee has a problem — perhaps understanding the task at hand — which can be addressed with training, education or counselling. A worker productivity issue could also derive from suboptimal workflows. 

The visualization of the warehouse and its activities provided by digital twins allows managers to get to the bottom of these kinds of activities quicky and efficiently. If employees are not following instructions, they can be taught why they need to do X before they do Y. 

Workflow fixes come about from viewing the big picture that digital twins provide. Revising workflows, and understanding their implications in advance, gives managers the confidence that they are on the right track toward improving worker productivity.  

5 Improving Inventory Management 

WMSs have always produced a wealth of data relating to inventory management, but the visualization provided by digital twins goes a long way toward making that information actionable. 

At its core, the digital twin is all about visualizing product locations within the warehouse. Identifying picking hot spots shows where inventory is moving fast and that's going to drive decisions on the best places for locating specific products within the warehouse.  

Digital twins and AI can identify potential problem areas before they develop. For example, many products have expiration dates, after which they must be discarded. Knowing that information allows logistics managers to communicate with sales teams on items that need to be moved out the door quickly. 

Building a three-dimensional model of the warehouse and taking an analytical view of what the data reveals helps to optimize product locations based on the facility cube and product velocity to reduce missed picks. Analytics capabilities provide answers as to whether inventory can become more productive if it's slotted in a different location. Getting the most out of warehouse locations is important for effective inventory management. 

Tecsys Provides an Out-of-the-Box Digital Twin Solution 

Tecsys, a supply-chain technology company, provides an out-of-the-box digital twin solution that helps users track and monitor supply-chain trends in real time and over time.  

“It’s essentially a 3D heat map,” says Chris McPherson, director of business intelligence and analytics at Tecsys, “which allows users to navigate the warehouse as if they were walking the floor to identify areas of high pick and replenishment activity and to optimize the location of products. That would be extremely difficult to do looking at a spreadsheet or at tabular data.” 

“Out-of-the-box” means that there are no services engagements or customization required to get up and running. “We think of our solution as democratizing the digital twin,” says Bill Denbigh, a Tecsys vice president. “It makes the technology more accessible to mid-level companies that might not be ready to fully automate their warehouse.” 

The benefits of the solution do not end at the warehouse door, according to Joe Vernon, a principal at EPAM Systems, who consults with Tecsys. “Virtual simulations connect across supply chains,” he explains. “After implementing the solution in the warehouse, you can bring it upstream to transportation and to supply and demand planning. When these virtual environments connect and talk to one another, they can really transform the supply chain.” 

Resource Link: www.tecsys.com