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Shop-Floor Bottleneck Prevention and WIP Queue Control

A high-mix, medium-volume industrial equipment manufacturer produces heavy hydraulic manifolds and valve block assemblies for mining and earthmoving machinery. Their plant operates across six discrete production stages: CNC machining, deburring, ultrasonic cleaning, valve spool fitting, high-pressure hydraulic testing, and final painting. The plant handles dozens of concurrent customer orders with strict delivery windows and contract penalties for late delivery.

Runs onAnvaya
floor expediting and unplanned overtime costs drop
85%floor expediting and unplanned overtime costs drop

How the work runs

The pressure that made this worth automating, the steps the system runs, and what came out the other side.

Pressure & Trigger Points

  • A sudden influx of custom valve spool orders causes a severe, silent backlog at the spool fitting workstation.
  • Planners rely on end-of-shift paper traveler sheets and once-daily ERP batch updates, catching bottlenecks only after downstream assembly lines run out of parts.
  • Floor expediters scramble via phone calls and whiteboards to authorize costly overtime without clear visibility into machine capacity or true order priority.

The run · 5 operational steps

Click any step to inspect telemetry signals, model reasoning, and governance gates.

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1

Live Timeline Queue Monitoring

Anvaya continuously visualizes real-time job movement across all workstations on the live Production Timeline, automatically tracking cycle times and queue volumes.

Input Signal:

Real-time operational telemetry & queue

Reasoning Pattern:

MCP grounded vector inference

Governance Gate:

Policy constrained with audit write-back

Verified Business Outcomes

  • Testing and assembly lines operate without interruption, preventing schedule slippage across 14 high-value customer orders.
  • Floor expediting and unplanned overtime costs drop by ~85% through automated queue monitoring and proactive capacity balancing.
  • Zero SLA late-delivery penalty charges are incurred during peak production volume.

Capabilities this relies on

  • system connectors
  • signal ingestion
  • root cause reasoning
  • anomaly detection
  • workflow orchestration
  • evidence audit trail

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