Use case
Use case3 min readBy The Data Workers Team

Warehouse operations data, run by agents

WMS, labor and automation data sit in separate systems, so throughput and labor productivity are reported a day late. Here is how Data Workers runs it instead.

Key takeaways

  • •The problem today. WMS, labor and automation data sit in separate systems, so throughput and labor productivity are reported a day late.
  • •What changes. Agents stream WMS and labor events, reconcile them and keep productivity metrics live.
  • •What stays with your people. Labor data tied to pay: people's pay depends on it.
  • •The result. Supervisors see throughput by the hour.

The problem today

WMS, labor and automation data sit in separate systems, so throughput and labor productivity are reported a day late.

What changes with Data Workers

Agents stream WMS and labor events, reconcile them and keep productivity metrics live.

The workflow: Real-Time Streaming agent (register_stream_schema) → Pipeline Building agent (generate_pipeline) → Quality Monitoring agent (get_anomalies) → Data Context & Catalog agent (define_business_rule).
Fig. 1:The workflow: Real-Time Streaming agent (register_stream_schema) → Pipeline Building agent (generate_pipeline) → Quality Monitoring agent (get_anomalies) → Data Context & Catalog agent (define_business_rule).

The workflow, step by step

  1. 1.The Real-Time Streaming agent calls register_stream_schema and streams WMS events.
  2. 2.The Pipeline Building agent calls generate_pipeline and joins labor and throughput.
  3. 3.The Quality Monitoring agent calls get_anomalies and catches gaps.
  4. 4.The Data Context & Catalog agent calls define_business_rule and governs productivity.

The impact metrics we move

  • •Throughput report: from about a day to 15-20 min
  • •WMS gap caught: from about 4 hours to minutes
  • •Productivity metric: from about a week to 4 h
Time to complete, current practice (black) vs. with Data Workers (green). Illustrative.
Fig. 2:Time to complete, current practice (black) vs. with Data Workers (green). Illustrative.

What goes on autopilot

  • •WMS events stream continuously, so throughput is live.
  • •Productivity is governed, so sites compare fairly.

Where your people stay in charge

Labor data tied to pay always go to a named approver, because people's pay depends on it. Everything starts at L1, observing only, and autonomy rises only on evidence.

The pilot

  • •Scope. Live throughput at one site.
  • •First week. Agents run at L1, observing only, and show the fixes they would have made, with receipts.
  • •By week two. The routine work above runs on its own; anything sensitive stays with a named approver.
  • •At day 30. We review the impact metrics against your baseline:
  • •Throughput report: from about a day to 15-20 min
  • •WMS gap caught: from about 4 hours to minutes
  • •Productivity metric: from about a week to 4 h
  • •Cost. Start with a pilot ($7,500 one-time, credited in full against the first year). Scale is from $1,000 a month and Enterprise from $3,000 a month, billed annually, with unlimited seats, no usage meter and no markup on model spend. See pricing.

Related: The logistics and transportation hub · The full team playbook

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