Connect Manhattan Active Warehouse Management to WareBee.

How the connection works.

1. 1

   Get the data out

   Connect via REST API, scheduled exports, or a one-off CSV — whatever Manhattan Active Warehouse Management supports in your setup. No changes to your WMS configuration.

2. 2

   WareBee builds the twin

   The Universal WMS API maps your feeds onto the five core data types below and builds a live 3D digital twin of your warehouse.

3. 3

   Optimise and execute

   Analyse performance, simulate changes, and send approved recommendations — slotting, batching, labour plans — back to Manhattan Active Warehouse Management for execution.

What data WareBee reads.

Five data types cover everything the twin needs. Manhattan Active Warehouse Management already produces all of them.

• Events

  Picking, replenishment and putaway events with timestamps, users, locations and quantities.

• Orders & picklists

  Order lines, dates, quantities and routing metadata — waves, docks, priorities.

• Items

  SKU master data: dimensions, weight, unit-of-measure hierarchy, groups and stackability.

• Inventory & assignments

  Which SKU sits where, and in what quantity — the twin’s current state.

• Locations

  Aisles, bays, levels and positions with dimensions — the physical map of your warehouse.

Products & editions.

WareBee works with every edition below — the Universal WMS API only cares about the data, not the version.

• Manhattan Active WM

  Cloud-native, versionless platform — the current flagship.

• Manhattan WMOS

  Warehouse Management for Open Systems — the widely deployed on-premise generation; still runs many large DCs.

Example data mapping.

A pick-event extract mapped onto WareBee’s Events feed. Column names vary per site — the mapping is configured once during onboarding and reused for every refresh.

event_id,job_id,process_type,event_start,event_end,user_id,location_id,sku_id,quantity
E-583921,PCK-11027,PICKING,2026-05-04T08:12:03Z,2026-05-04T08:12:41Z,U-217,A-012-03-1,SKU-44712,6
E-583922,PCK-11027,TRAVELLING,2026-05-04T08:12:41Z,2026-05-04T08:13:20Z,U-217,A-014-01-2,,
E-583923,RPL-04481,REPLENISHMENT,2026-05-04T08:14:02Z,2026-05-04T08:15:10Z,U-098,R-201-11-4,SKU-09113,48

Ask AI about your Manhattan Active Warehouse Management data.

Anyone on the team can ask, in plain language — answers come back as charts, tables and KPIs, grounded in the twin built from your Manhattan Active Warehouse Management data.

• “What are our picking rates by zone?” → Ask AI
• “Which SKUs should move before peak season?” → Ask AI
• “Where is travel time leaking in Manhattan Active Warehouse Management?” → Ask AI

Value for every slotting model.

However Manhattan Active Warehouse Management runs your warehouse — fixed slots, dynamic putaway or a mix — the twin optimises the part that matters.

• Static slotting

  Better slotting

  Every SKU gets the fixed slot that minimises travel and congestion, with re-slotting plans your team can apply week by week.

• Dynamic slotting

  Zone configuration

  WareBee tunes the zones themselves — velocity boundaries, putaway rules and zone shapes that direct stock to the right areas.

• Hybrid

  Both, together

  Optimised fixed pick faces where stability pays, optimised zone rules where flexibility wins — one model covering both.

What you unlock.

• Improve WMS slotting

  Optimal slot assignments, simulated in the twin and sent back to Manhattan Active Warehouse Management for execution.

• Batching & clustering

  Group orders into efficient pick waves that cut travel and picking stops.

• BI tools

  Warehouse analytics over your events — cost to serve, utilisation, CO₂, velocity.

• AI dashboard

  KPIs and trends assembled by AI, surfacing what changed and why it matters.

• Ask AI

  Plain-language questions about your data and processes, answered as charts and tables.

• Root-cause analysis

  Trace a missed SLA or a slow shift back to the events that caused it.

• Process mining

  Reconstruct how work actually flows — detours, rework and bottlenecks included.

• Master-data analysis & audit

  Find dimension gaps, orphan locations and data issues — then fix them in the WMS.