In warehouse operations, managers are on a never-ending quest to drive greater productivity and throughput in the most efficient manner possible. Especially in lower-margin e-commerce categories such as fast-fashion apparel, electronics and books or media, the need to continually achieve process optimization is paramount.

Increasing labor costs and low unemployment, fluctuating demand, rising customer expectations and supply chain disruptions have created challenging conditions that tax the limits of traditional warehouse management system (WMS) technology. A lack of real-time data analytics constrains adaptability in order processing and tasking as conditions change, often throughout the day.

Companies need technology that not only improves order fulfillment metrics, but also helps them retain workers by making it easier for those workers to hit productivity targets, be accurate while doing so, and stay engaged with the task at hand.

The WMS “capability gap” has created a growing need to evolve traditional warehouse operations into self-optimizing, intelligent systems. Using artificial intelligence and machine learning, innovative warehouse optimization augments the performance of a legacy WMS, putting it on steroids. Companies can boost fulfillment efficiency through things like voice-directed processes, improved labor utilization, smarter tasking and mobile work execution.

Warehouse optimization technology also allows managers to create a dynamic fulfillment environment that automatically flexes as demand and priorities shift and change. For instance, associates can be automatically redirected by voice command from lower-priority to higher-priority orders — based on preset rules — along optimized pick paths. The system can also assign other tasks along the route, such as moving items to a more productive pick location.

This approach is bringing companies closer to the concept of the self-optimizing warehouse. It’s a world in which automated systems are continually learning and presenting new ways to improve productivity, reduce costs and keep workers happy — a trifecta and a flywheel effect at the same time.

Static vs. Dynamic Environments

In a standard WMS without warehouse optimization software, processes are much more static and inflexible. For instance, hard allocation of inventory requires constant manual oversight and intervention to adjust as priorities shift. This keeps managers shackled to their screens instead of doing what they were hired to do: managing workers out on the floor.

“When we go into a warehouse operation, we see a lot of supervisors doing administrative tasks instead of managing people, actually engaging with them,” says Simon Dunlop, head of solution consulting in the U.S. and U.K. for Lucas Systems. “They're bogged down in having to constantly look at the WMS, at the order well, and when things are dropping, so they can change priorities manually. So you have these highly paid managers sitting in control towers, moving stuff around in the WMS.”

In a self-optimized environment using an advanced warehouse execution system (WES) using AI and machine learning, the system sees orders dropping and priorities shifting through the course of the day, and dynamically adjusts to track against goal.  

Batching is another area where a static WMS process gets bogged down. Static batches are generated in the morning, an approach which fails to account for shifting priorities or SKU-level inventory demands as the day progresses. As a result, early batches may group work inefficiently, leading to underutilized resources, throughput bottlenecks and delays. Tasks remain rigidly assigned even when conditions evolve, which prevents operations from adapting in real time, reducing agility and productivity.

In a dynamic WES environment, the batching of orders is done in real time at the point an associate becomes available and requests work.  

Synchronous integrations between the WMS and WES environments means the hard allocation of orders are mirrored in WES.  At the point of integration, the WES environment will consider any pre-defined priority-based rules for the order and calculate the point-to-point travel distance to fulfill the order using a digital to-scale map of the warehouse.  In addition, the WES will consider volumetrics and cartonization of products into pre-defined shipping containers to build “units of work.”  

It’s only at the point an associate becomes available and requests work that the WES will look to dynamically build a batch of work for the individual. In milliseconds, the system will factor in the constraints of the equipment the associate is using, intelligently considering the highest priority order and building a batch of work, complemented by other orders to ensure high pick density and the most optimal travel path around the warehouse, to execute the picking of these dynamically built batches.  The orchestration, assigning and execution of these tasks for the associate is performed through voice or visual commands on devices.          

Workforce Management Gains

The labor utilization rate is a critical metric in warehouse operations, a gauge of worker productivity expressed as a percentage of time on task versus standing idle. The lower the utilization rate, the more resources are wasted, productivity declines, and operating costs rise. The result is greater inefficiency in meeting fulfillment demands.

Static processes in a legacy WMS limit labor utilization by locking tasks into predefined waves and hard allocation, often based on old data. Inflexible workflows result in workers waiting for assignments, inefficient batching and excessive travel. Without real-time task adjustments or dynamic prioritization, companies struggle to respond to shifts in demand or order priorities, resulting in underutilized labor and higher operating costs.

A modern WES process with smart warehouse optimization, on the other hand, analyzes real-time data on run rates and workforce capabilities. Based on that, it can flex the workforce by adding hours to meet end-of-day targets, or automatically reassign workers to higher-priority areas and tasks better aligned with strategy. “The system does that based on AI and not necessarily driven by supervisors sitting in control towers, manually moving stuff around in the WMS,” says Dunlop.

Using advanced speech recognition algorithms within voice picking, the WES learns users’ voice templates and modeling to make them more efficient. By making this specific to each worker, the system improves accuracy and response speed on tasks. This personalized adaptation reduces miscommunication, increases picking speed, and minimizes downtime caused by repeated commands or errors. Ultimately, workers are more efficient, and productivity is enhanced.Workforce churn is a major issue in warehouse — a whopping 46.1% in 2024 compared to the national average of 12% to 15%, according to the Bureau of Labor Statistics. This high rate is due to the physically demanding work, irregular schedules, and relatively low pay. Retention, therefore, is a critical goal.

One key avenue is technology. In a 2023 “Voice of the Warehouse Worker” study by Lucas Systems, 90% of respondents said investing in new technology helps attract and retain workers, while 88% said it equates to investing in the workforce. The top-ranked benefit was “increased ability to meet performance goals,” followed by “improved accuracy and minimized mistakes,” and “making the job less physically demanding.”

Companies are finding that warehouse workers are willing to stay longer and even take less pay to have good technology that they feel comfortable working with. “People really want adaptive tools that are closer to what they're using every day, a mobile device that looks a lot like a smartphone instead of a big brick RF gun,” says Kyle Franklin, a senior solution consultant with Lucas Systems. “They can pick things up a lot faster, and that's providing a lot of benefit as well.”

Wes Coleman, industry principal for warehousing and distribution with Zebra Systems, agrees. “They want the same thing at work that they have when they're walking through an airport — they don’t have a wire going to their phone,” he says. “They want something wearable, with a large enough screen and a comfortable user interface. And having the ability to get immediate [performance] feedback right on your arm or in your headset, depending on the workflow, is a huge advantage.”

Task-Based Work and Prioritization

Another key aspect of the self-optimizing warehouse concept is the ability to implement tasking, such as task-based workflows. In this scenario, the system automatically assigns specific tasks to individual workers based on shifting operational priorities throughout the day. Workflows are organized in a manner designed to maximize efficiency and minimize delays. Tasking can cover every conceivable function, from picking and packing to replenishment, cycle counting, putaway and loading/unloading.

Task-based workflows are optimized for travel time and labor efficiency, and bring several benefits:

•    Clear assignments through voice or visual prompts, reducing errors in inventory handling and order fulfillment.

•    Task priorities that can be dynamically adjusted based on shifting demands (such as rush orders and delayed shipments).

•    Performance metrics such as completion times and order accuracy, tracked in real time at a granular level, highlighting inefficiencies and optimizing labor allocation.

•    The ability to implement interleaving, combining multiple tasks into a single route, further enhancing productivity.While task-based workflows can be much more efficient than traditional waving, where orders are grouped and timed based on deadlines, priority levels, or order types, the two methods aren’t necessarily antithetical. Using a hybrid approach, wave picking is used for pre-planning large order batches, while AI and machine learning-driven tasking dynamically adjusts worker assignments based on real-time operational data.

“From our perspective, warehouse managers shouldn’t be too bothered about the wave strategy,” Dunlop says. “But the reality is, if the orders are there, the system will determine based on schedules, resources and shift patterns when individuals become available, and they can demand work dynamically. At that point, prioritization can be taken into consideration.”

The Power of Managing by Exception

In warehouse operations, “managing by exception” refers to focusing effort on deviations from standard processes, goals or metrics, instead of actively managing each individual task that is in error. This approach relies on systems that monitor operations and alert managers only when issues arise that require intervention.

Using an AI-powered WES, warehouse managers can set up specific key performance indicators or limits based on things like order accuracy, pick rate, inventory levels or productivity voids, to trigger exception alerts. An exception dashboard displays where issues are popping up. “Are there shorts in an area, or insufficient inventory to complete orders? Are workers going slow and breaking the ‘speedometer’ that tracks completion rates? Is there data on import or export failures? All these and many other scenarios can be preset alert triggers in a WES, providing areas for ‘health checks’ during the day in terms of goal versus actual performance,” Franklin says.

For instance, the system can message an individual via headset if they’re lagging, but if exceptions exceed a tolerance level it can be escalated to a supervisor. Companies have complete flexibility in terms of how they set up exceptions management.

“The ability to proactively manage operations is the goal,” Dunlop says. “There are many times when managers aren’t aware of individual performance because they’re seeing it the following day, and it's very difficult to manage individuals that way. A next-generation WES gives them the opportunity to deal with something in real time, whether it's a discrete conversation with an individual or handled another way. They also have the freedom to do so when the system is automatically directing work in the background, and they’re not bound to order flow in the WMS.”

From the Realm of the Possible to Reality

The demands of e-commerce and omnichannel operations today make it impractical to rely on outdated technology and processes for orchestrating fulfillment. Especially with labor perpetually in short supply, human capital resources must be managed carefully to maximize throughput and order demand while keeping margins in view.As it has in so many other spheres, AI and machine learning are making possible previously unheard-of levels of warehouse optimization via smart, automated processes. Real-time data analytics provide key insights that drive dynamic resource allocation. The results speak for themselves, with many organizations realizing average productivity gains in the range of 15% to 30%, while some have even greater success.

Resource Link: lucasware.com