WALI · AI-ASSISTED WAREHOUSE EXECUTION
Your warehouse workload moves.
Your release plan should too.
WALI WES reads open orders alongside the people and stations actually working. It sizes micro-waves to real task speed and recommends practical support to supervisors when a queue starts to grow.
Works with the existing WMS and RF flow. Supervisors approve floor resource changes; results are measured in a pilot.
RECOMMENDED MOVE
Reduce next micro-wave · move available support to packing
THE PROBLEM
The bottleneck moves during the shift.
Work changes
The order mix and actual picking speed move away from the morning plan.
Capacity stays in the wrong place
Packing queues grow while suitable people may be idle elsewhere.
Local choices slow the flow
The first task on a terminal may miss a dispatch deadline or packing constraint.
THE WES DECISION LOOP
Choose the work. Size the wave. Learn from the result.
WALI reads orders, stock, deadlines, skills and queues. It groups common SKUs and nearby locations to select the right work; the WMS sends tasks to existing RF terminals. Actual duration updates the next wave decision.
Prioritise
Deadline, common SKU, location and remaining work
Release a micro-wave
Match free pickers and packing capacity
Recommend support
Suggest a qualified worker who has finished a task
Recalculate
Use actual WMS/RF speed and waiting time
ONE OPERATIONAL VIEW
People and automation in the same flow.
Shifts, skills, availability and each critical station's minimum crew are considered together. When connected, AMR/AGV location and battery, robot-cell capacity and conveyor blockages join the flow. Supervisors approve temporary people moves.
SAMPLE FLOOR DECISION
10 pickers, 2 packers, 1 loader. Which work should be released now?
WALI weighs common SKUs and nearby locations against the dispatch promise, available crew and packing load. If packing is full, it reduces the next wave. It suggests a qualified worker who has completed a task for temporary support, while protecting minimum staffing at critical stations.
FIELD RESULTS REPORTED IN THE WES DECK
Faster flow. Less waiting.
Picking time
Wait before packing
First pick to last pack
Source: Datanomous_WES.pdf, 'Field Results'. These are reported outcomes, not a guarantee for every site. We compare baseline and pilot periods with similar order mix and staffing to measure your result.
The WMS records. WES manages the flow.
WALI adds a decision layer to the existing WMS/RF flow: it calculates what to release and when, estimates completion, and passes approved decisions to a supported WMS process. Operators keep using their terminals. Connected yard and shipment status can inform priority too.
CONNECTED OPERATION
The warehouse is one part of the promise.
WES adjusts floor tasks against real capacity, while upstream orders and downstream dispatch stay connected.
Measure lost time. Prove WALI in a pilot.
Connect order, task and shift data to measure waiting and idle capacity. Then compare time, labour hours and warehouse output in a limited live pilot with similar work.
Review your warehouse flow