For years, warehouse pallet picking has followed a familiar operating model: Plan work by store, release it in waves, and focus on execution efficiency once tasks hit the floor. It is a structured approach, but one that is becoming increasingly difficult to sustain as fulfillment requirements grow more variable, service expectations rise, and operations are asked to do more with tighter labor resources.
As order profiles become more complex and workforce constraints remain persistent, static, store-by-store planning is showing clear operational limits. The next phase of improvement is not simply about executing traditional processes more efficiently. It is about redesigning how work is created so operations can respond with greater speed, precision, and flexibility.
That shift aligns with a broader trend across manufacturing and distribution: they are moving from fixed, sequential processes toward more adaptive, data-driven operations. Whether the objective is improving throughput, strengthening fulfillment responsiveness, managing inventory flow more effectively, or building resilience into the movement of materials, leaders are increasingly looking for ways to make execution decisions in real time rather than relying solely on static plans built hours earlier.
Pallet picking shifts to continuous optimization
Imagine a warehouse where pallet picking is not pre-defined in rigid batches, but instead continuously optimized in real time. Work is no longer locked into a single-store sequence. Instead, orders across multiple stores are dynamically grouped based on the most efficient way to pick, build, and move product through the facility.
This is the core idea behind emerging approaches like “best pallet matching,” a concept that pushes optimization upstream. Rather than simply improving execution after work is released, it fundamentally rethinks how work is created in the first place.
By using real-time data and AI-driven orchestration, warehouses can intelligently batch orders across stores, aligning them based on shared characteristics like product location, cube, weight, and route compatibility. The result is a more fluid, responsive system that adapts continuously as conditions change.
What AI changes on the floor
The impact of this shift is immediately visible in how work gets executed. Instead of sending a picker out to build a pallet for one store at a time, the system can direct them to build multiple pallets simultaneously, often using double or triple pallet jacks. This seemingly simple change unlocks significant efficiency gains.
Travel time, one of the largest sources of waste in warehouse operations, is dramatically reduced. Pick paths become more consolidated. Workers spend less time walking and more time picking.
At the same time, pallet quality improves. Because orders are grouped more intelligently, there’s greater control over how pallets are built, balancing weight, stability, and sequencing in ways that are difficult to achieve with static planning.
These gains extend beyond labor efficiency alone. More intelligent batching can help increase throughput, reduce touches, and improve the consistency of outbound loads, all of which matter in high-volume environments where small process improvements compound quickly.
In facilities balancing cost pressure with service commitments, that kind of operational control can translate into better asset utilization and a more predictable path to meeting daily performance targets.
Why smarter operations matter now
The timing of this shift is not accidental. Warehouses today are under pressure from every direction: rising customer expectations, ongoing labor challenges, and the need to handle greater variability without sacrificing performance.
Traditional optimization methods, while still valuable, are inherently limited by when they occur. If optimization only happens after work is released, much of the opportunity has already been lost.
By moving optimization earlier in the fulfillment process, and making it continuous rather than periodic, operations gain a new level of control. They can respond in real time to changes in order volume, labor availability, and operational constraints.
It’s no surprise that approaches like this are gaining industry recognition. More importantly, they reflect a broader shift in thinking: from static execution to dynamic orchestration.
For manufacturing and supply chain leaders, this is an important distinction. The discussion is no longer just about picking faster inside the four walls. It is about creating a more responsive operating model that supports downstream transportation, store readiness, stronger fulfillment performance, and better synchronization with inventory availability and replenishment decisions across the network.
As digital transformation initiatives continue to expand, capabilities that enable continuous optimization are becoming increasingly relevant to the competitiveness of the broader enterprise.
The bigger picture: Orchestrating work, not just managing pallet picking
At its core, this evolution is about more than pallet picking. It represents a fundamental change in how warehouse performance is managed. Instead of viewing the warehouse as a series of discrete tasks to be optimized individually, leading operations are starting to treat it as a continuously orchestrated system, where decisions are made in real time, based on the full context of the operation.
This approach doesn’t require ripping and replacing existing systems. In many cases, it can be layered on top of current infrastructure, enhancing what’s already in place while unlocking new levels of efficiency.
The future of warehouse execution will belong to operations that can adapt, quickly, intelligently, and continuously. Real-time, AI-driven pallet optimization is one example of how that future is already taking shape. For organizations still relying on static picking strategies, the question is no longer whether change is needed, but how soon they can begin making the shift.
Because in a world where variability is the norm, the ability to dynamically create and execute work isn’t just an advantage; it’s becoming a requirement.
About the author
Bill Erdely is the senior director of solution consulting and project management at Lucas Systems. He has more than 25 years of experience in supply chain execution and technology.
Erdely’s expertise spans warehousing and distribution, transportation, automation, and labor management systems, with a deep background in serialization, RFID, and software validation.
Throughout his career, Erdely has led solution design and delivery for Tier 1 organizations across industries, holding leadership roles at companies such as Körber, DM Logic, and RedPrairie. He has extensive hands-on experience with a significant number of distribution and supply chain systems and platforms. Bill holds a B.S. in industrial management from Carnegie Mellon University.