Why Picking One Store at a Time Is Costing You…and How AI Finds a Better Way

By Ben Smeland, Senior Software Engineer, Lucas Systems

In many distribution operations, pallets are still planned around a familiar objective: making delivery and store stocking as easy as possible. Store-ready pallets help teams unload, replenish shelves, and keep downstream processes moving. But when pallet planning is optimized almost entirely for the destination, it can create a significant hidden cost inside the warehouse.

The challenge is that store-ready does not always mean warehouse-efficient. In many operations, picking work is still assigned one store at a time. Even when workers use double or triple pallet jacks, each assignment is often tied to a single destination. As a result, multiple workers may travel the same aisles, pass the same pick faces, and repeat similar routes throughout the shift.

The impact adds up quickly. Redundant travel reduces productivity, increases labor cost, and makes already demanding work more physically taxing than it needs to be. In facilities with limited automation, travel can account for half or more of total picking time. That means a large share of labor effort is spent not on selecting product, building accurate pallets, or serving customers, but simply moving between picks.

From Moving People Faster to Matching Work Smarter

The traditional response to excessive warehouse travel has often been mechanical: add conveyors, install goods-to-person systems, or deploy mobile automation to reduce walking. For the right operation, those investments can make sense. But many distribution centers run mixed workflows, handle variable order profiles, and rely on people working alongside partial automation. In those environments, the more practical opportunity is not always to replace movement with infrastructure. It is to make the work itself more intelligent.

That is where the idea of “best pallet matching” becomes powerful. Instead of asking how to move a picker through the warehouse faster on a single-store assignment, it asks a different question: which pallets should be picked together so that one trip through the warehouse accomplishes more?

In practical terms, a worker using a double or triple pallet jack may be able to pick for multiple stores at the same time while preserving store-ready integrity. The opportunity is not simply batching for the sake of batching. It is finding the right combination of pallets, at the right moment, for the right equipment, in a way that reduces travel without creating accuracy, safety, or pallet-build problems.

Seeing the Work as a Network of Possibilities

At its core, best pallet matching is an optimization concept. It uses data about orders, locations, products, equipment, priorities, and warehouse constraints to identify which pallets should travel together through the facility. Rather than following a first-in, first-out queue or a simple route sequence, a more advanced approach evaluates many possible combinations and selects the groupings that can produce the most efficient work assignment.

This matters because pallet matching is a combinatorial problem. Two pallets may look compatible because they share a few aisles, but that does not necessarily make them the best match. Another combination may reduce travel more, balance cube more effectively, avoid product-handling issues, or better align with order priority. When multiple stores, pallet sizes, pick zones, and equipment types are involved, the number of possible combinations quickly grows beyond what manual planning or basic sequencing can reasonably evaluate.

When the right pallets are matched, the worker receives a single optimized assignment that may include two or three store-ready pallets. Each pallet remains tied to its destination, but the work is organized so the picker can collect items for multiple stores on one efficient pass through the warehouse. Instead of sending several people down overlapping routes, the operation concentrates compatible work into fewer, smarter trips.

Rethinking the Pick Path

Once compatible pallets are grouped, the next opportunity is routing. Traditional picking paths often follow a fixed sequence through the warehouse. That may be simple to manage, but it is not always the shortest or most efficient route for a specific batch of work. A smarter approach uses a digital understanding of the facility to calculate paths based on the assignment, the worker’s starting point, aisle restrictions, cut-throughs, product sequencing needs, and even congestion created by other in-progress work.

Instead of assuming that the best path is always linear or serpentine, advanced routing can test different approaches and select the one that minimizes unnecessary movement. In some cases, the best route may prioritize heavy or stable products early so the pallet can be built correctly. In others, it may avoid congestion or change the starting aisle to spread workers more evenly across the floor. The result is a route built for the actual work in front of the picker, not a generic path through the building.

What the Results Can Look Like

The potential impact is significant because this approach targets one of the largest sources of wasted time in manual and semi-automated picking environments. In operations where pallet matching has been applied effectively, travel savings from batch optimization have reached the 15–30% range, with additional reductions possible when optimized pick paths are layered in.

One large wholesale grocery operation provides a clear example. Because its stores varied widely in size, traditional assignment logic struggled to combine orders efficiently. Larger stores generated substantial pallet volume, while smaller stores created opportunities for complementary pairing. By matching pallets more intelligently, the operation was able to group work in ways that reduced travel while preserving the store-ready structure required downstream.

Just as important, this type of improvement does not necessarily require a major redesign of the building. For many operators, the appeal of best pallet matching is that it works within the physical realities they already have: existing aisles, existing equipment, existing labor, and existing store-service requirements.

Augmenting the Workforce Instead of Replacing It

Best pallet matching is not automation in the traditional sense. The worker still picks the product, builds the pallet, validates the work, and applies judgment on the floor. What changes is the intelligence behind the assignment.

Instead of expecting people to solve a complex optimization problem while they are also performing physical work, the system handles the calculations that are difficult to do manually: which pallets belong together, which route makes the most sense, where congestion may occur, and how to balance competing priorities. The worker then executes a better plan.

The Economics of Intelligence Before Infrastructure

Fixed automation can deliver strong value, but it usually requires significant capital investment, long implementation timelines, and a level of process stability that not every operation has. Conveyors, AS/RS, and goods-to-person systems are powerful tools, but they are also physical commitments built around assumptions about flow, volume, layout, and future demand that may change over time.

A software-first optimization approach follows a different investment profile. It can often be deployed within existing workflows, integrated with existing warehouse systems, and adapted through configuration rather than construction. That flexibility matters in environments where customer requirements, order patterns, labor availability, and store formats are constantly changing.

For operations where capital budgets are constrained, or where the business changes faster than fixed infrastructure can be reconfigured, intelligence can be a more resilient first step. It does not eliminate the need for automation in every case. Rather, it helps operators extract more value from the people, equipment, and space they already have.

Smarter Assignments, Not Harder Work

Store-ready pallets will continue to matter because retailers still need efficient receiving and replenishment. But warehouse leaders also need to ask whether the path to building those pallets is as efficient as it could be.

Best pallet matching reframes the problem. Instead of treating each store order as an isolated assignment, it looks across the work to find combinations that reduce travel, balance loads, preserve accuracy, and make better use of existing labor and equipment. That is where the savings often hide: not in asking workers to do more, but in giving them better work to do.

The question is not whether pickers are working hard enough. In most operations, they are. The better question is whether the systems around them are doing enough to make every trip, every pick, and every pallet count. For many warehouses, the next major productivity gain may not come from more infrastructure. It may come from smarter matching.

Ben Smeland serves as a Senior Software Developer with Lucas Systems, leveraging more than 19 years of software development experience to challenge and innovate against software architectures in order to promote clarity, performance and sustainability.

With experience as a full-stack developer, software architect, and project manager, Ben has served in almost every capacity in the software industry, engaging with internal teams and customers to bring inventive, sustainable solutions to complicated business problems

Having earned a Bachelor of Science degree in Computer Science, as well as Master of Science in Computer Information Technology, Ben excels at critical, out-of-the box thinking and solving complexity through simplicity.