Understand the distinction: batch picking groups multiple orders into a single picker trip (one picker, many orders); wave picking releases groups of orders to the floor in scheduled waves to coordinate picking, packing, and shipping.
Use your WMS API to query open orders filtered by criteria such as shipping deadline, carrier cut-off time, zone, or product location; these attributes drive wave or batch grouping logic.
Create a wave by POSTing a wave definition to the WMS wave endpoint with the selected order IDs, the target release time, and the picking method (batch, zone, or cluster); the WMS returns a wave ID and generates pick tasks for warehouse staff.
Retrieve pick tasks assigned to each wave via the pick task query endpoint; distribute tasks to mobile scanning devices or automated picking systems via the task assignment API.
As picks are completed, the WMS updates pick task status in real time; subscribe to pick-complete webhooks or poll the wave status endpoint to monitor wave progress and trigger packing station prep.
Release subsequent waves only when capacity at packing and shipping stations allows, using wave status data to pace throughput and avoid downstream bottlenecks.
Known gotchas
Wave size must balance pick efficiency (larger waves = fewer trips) against shipping deadline risk (too-large waves may miss cut-off times for some orders within the wave).
Not all WMS platforms expose wave and batch configuration via API — confirm your WMS vendor's API support for wave management before designing a programmatic workflow; some require manual wave release in the WMS UI.
Batch picking is most effective when SKU density is high (many orders share the same SKU); for highly diverse order profiles, zone or cluster picking typically outperforms pure batching.
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