We applied the same diagnostic approach across five client engagements and found the same pattern every time: the WMS was directing pickers through unnecessary travel, fast movers sat in inefficient locations, and wave logic scattered work across the floor. The fixes were configuration changes, not capital projects.
Across these five networks, the combined annual labor savings exceeded $8 million. In two cases, the savings crossed eight figures when the approach was deployed network-wide. Generalized for confidentiality.
Pick rates were inconsistent. Late waves piled up near carrier cutoff. Managers added headcount to close gaps that never closed. But when we pulled the pick task history, location master, SKU velocity data, and wave configuration parameters, the problem was never the pickers. It was the path they were asked to walk.
Across all five engagements, we found the same four issues. Fast-moving SKUs slotted deep in the building because nobody had cross-referenced velocity against bin locations in over a year. Location priority numbers assigned linearly, up one aisle face then restart at the bottom of the next, forcing a backtrack on every pass. Waves released by order arrival time rather than pick-location proximity. Zone boundaries that predated the current order profile by years.
The diagram below is a real job pulled from a production WMS. Numbered circles show the actual pick sequence the WMS directed. The gold route line shows the path one picker walked. It has been generalized to protect confidential operating data while preserving the diagnostic pattern we found across multiple facilities.
Numbered circles show pick sequence. Route lines show picker travel.
This picker finishes Zone 3, then the sequence forces a cross-floor backtrack all the way to Zone 1 before continuing to Zone 2. Later in the same job, it directs the picker back through the same aisle face that was already completed, walking identical ground twice within a single job. Neither appeared on any productivity report.
The backtrack between picks 9 and 10 added roughly 140 feet of empty travel to a single job. Multiplied across 200-plus jobs per shift, across three shifts, across 250 operating days, that single sequencing failure was generating over 20 million feet of wasted walking per year in one facility. The redundant aisle pass between picks 25–30 added another layer on top. This is the pattern we found across all five client networks.
Each one compounds the next. Slotting determines where SKUs live. Sequencing determines the route. Clustering determines how picks are grouped. Zone allocation ties the physical travel together on the floor. We addressed all four, in that order, at every client.
We assigned every SKU a velocity tier from pick history, then cross-referenced order data to identify affinity groups: items consistently sold together. Those groups were re-slotted into adjacent bins so single-order picks completed within a compact zone. For multi-client 3PLs, we clustered by client. Across engagements, the typical result was 200 to 500 fast-mover SKUs moved closer to packout. At one network, that single change eliminated over 18 miles of cumulative daily walking.
The most common failure we found: location priority numbers assigned linearly, walking up one face then restarting at the bottom of the adjacent face rather than a natural zig-zag that lets the picker cross at the top and work back down. We re-sequenced location priorities to follow the actual walking path a picker would take. At one 3PL, this single change reduced average travel per job by 22 percent before any other fix was applied.
We reconfigured wave release logic so orders were clustered by pick-location proximity rather than arrival time or order number alone. Orders tied to earlier carrier cutoffs were released and grouped differently from lower-priority work due later. At one B2C fulfillment network, this change reduced average daily criss-cross travel enough to recover the equivalent of two full-time pickers per shift without hiring anyone.
We assessed how space and travel were allocated across each building: zone density, backtracking patterns, aisle congestion during peak waves, and cross-aisle cut-through efficiency. At several facilities, zone boundaries had not been adjusted since the original WMS implementation. We realigned them with the current order profile so the physical layout supported the sequence the WMS was trying to execute.
Every client implemented these changes inside their existing WMS. No new systems. No building modifications. No additional headcount. The configuration changes took three to six weeks from diagnosis to first measurable impact, depending on the WMS platform and the client's change-management cadence.
Re-sequenced location priorities eliminated forced backtracking across all pick paths. Average travel per job dropped 18–32%.
200–500 fast-mover SKUs re-slotted per facility into locations closer to packout, reducing high-frequency travel by thousands of daily steps.
Wave logic reconfigured to cluster orders by pick-location proximity and carrier cutoff windows. Smoother outbound flow, fewer late-wave escalations.
Directed put-away rules updated to preserve slotting gains across replenishment cycles, preventing velocity drift within weeks of the initial re-slot.
Zone boundaries realigned with current order profiles. Cross-aisle travel reduced and peak-wave congestion measurably lower.
Recurring 90-day SKU velocity review cadence established at every client to prevent slotting drift and sustain the gains.
See the same pattern in your operation?
Request a Pick Path Review →Annual labor savings from configuration changes alone. Same WMS, same headcount, same buildings.
In two cases, savings crossed eight figures when the approach was deployed network-wide. Not one of these outcomes required a new WMS, additional headcount, or a building expansion.
This approach has been applied successfully across multi-client 3PL operations, high-SKU B2C fulfillment, and wholesale distribution. The common thread: WMS-directed picking was in place, but pick rates were inconsistent, walking distance was excessive, late waves were routine, and slotting rules had not been reviewed against recent order history. The approach is especially effective when the WMS has the configuration flexibility to support location priority changes, wave rule updates, and directed put-away logic, which nearly every major WMS platform does.
Pull pick task history, location master, SKU velocity data, wave configuration, and physical layout. Map every instance of backtracking, cross-zone travel, and redundant passes. Quantify the annualized labor cost of each pattern.
Deliver the re-slotting plan, re-sequenced location priorities, wave rule updates, zone adjustments, and directed put-away changes. Separate quick configuration fixes from changes requiring operational testing.
Establish the 90-day velocity review cadence. Train the local team to run it themselves. Hand off the playbook so slotting and sequencing stay current as order profiles shift.
Start with the exports that show how people, orders, and locations move today. There is no slide deck or pre-analysis required. We use the raw operating evidence to find the waste.
Two connected data sets give us the operating behavior and the WMS configuration behind it.
This shows actual task flow, release behavior, and the current productivity baseline.
This connects travel patterns back to locations, facility layout, and WMS sequencing rules.
Optichain Advisors has helped warehouse teams across five networks find millions in hidden travel waste inside WMS sequencing, slotting, wave logic, and physical flow. The first conversation is about understanding your operation, not selling a scope.