Recently, SupplyChainBrain posted a blog, “Is There a Solution to the Supply Chain Worker Shortage?” The article talks about why there’s a shrinking workforce this year. In theory, many employers felt that people would start looking for a job once the federal unemployment benefits ran out in September. But that didn't happen.

Other reasons cited in the article for the labor shortage include the fallout from the pandemic, causing people to fear returning to the workplace. Meanwhile, as workers either decided or were required to stay home, they bought more online, severely taxing transportation and warehouse operations.

The increase in orders has created the need for more workers to fill them. Carriers are having an increasingly hard time finding truck drivers to deliver goods. Warehouse owners are raising hourly pay, adding benefits, and updating rest areas. But there isn't enough labor out there to hire.

Some companies think adding automation to their warehouse or distribution operations will speed fulfillment and reduce labor demand. Software vendors promise technologies to boost efficiency in the warehouse, including warehouse management systems (WMS), yard management systems, data replicas, digital twins, augmented reality, artificial intelligence, robotic automation and blockchain.

Many organizations have already begun to invest in some of these applications, to make operations more efficient and deliver better customer service. But each of these technologies entails a unique set of complexities.

A WMS, which provides the foundation for nearly every other innovative system, generates critical data throughout the warehouse. Many companies are adopting the new breed of technology known as a "data replica," which creates an easy-to-access database that pulls in real-time WMS data for querying, visualization, and alerting.

Data needs to contribute to an understanding of the future state of the distribution center. Often called "what-if" planning, this leverages digital twin technology. A digital twin is a mathematical model of the warehouse that analyzes all future-facing activities to predict what’s likely to happen in the future. An excellent digital twin will account for labor, shipments, inventory availability, tasking, space and other resources.

AI-based constraint-based optimization can be used to optimize different activity systems. When paired with a digital twin, this technology can prescribe a sequence of events, create a feasible schedule, and minimize touches and labor.

The final stage in the technology roadmap is to automate many human processes in the warehouse. The challenge with automation such as robotics is that it involves expensive devices, and rollouts are challenging and take a long time.

Many organizations today throw automation at their supply chains without considering the surrounding warehouse micro-flows. A consumer goods shipper, for example, might use an automated forklift to retrieve a pallet for staging, but that pallet might sit in the staging area for 12 hours before it's loaded onto a trailer. The pallet consumes valuable staging space for longer than necessary, and could cost the site the opportunity to get a different order out on time and in full.

Warehouses also face the challenge of optimizing existing labor without sacrificing customer fulfillment. What happens if someone doesn’t show up for work? If things don’t happen according to plan? Or if you still have unfilled floor staff positions, and aren’t sure of the best way to optimize the capacity that you do have?

A new approach called a WMS accelerator adapts and rebalances activities based on what happens inside the warehouse every few minutes. These systems pull in data from warehouse management, yard management, order management, visibility and production systems to provide a single, unified view of operations. Using digital twin technology, the data can then be used to predict what will happen. The technology brings in the current state from all systems and all known shipment and order information, and plays the future forward based on known constraints such as labor, inventory, and task times.

For example, at a single Procter & Gamble distribution center, a WMS accelerator is used to reduce daily planning time by 97%, cutting the planning of daily labor activities from eight hours to 15 minutes. Other businesses that use WMS accelerators can make unplanned temporary labor reductions permanent without any loss in site productivity. Reducing one worker per shift equates to over $100,000 in savings annually. At larger sites, such reductions can add up quickly.

Keith Moore is chief product officer with AutoScheduler.AI.