pAud  ·  Operational intelligence  ·  London

The copilot for your warehouse war room.

Local systems behave correctly. The network doesn’t.

pAud reads across your WMS, OMS and TMS and tells the person on shift what is about to break, while there is still time to act.

Already running with a major UK retailer.

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Lane 3 · SLA breach11:42→ 15:00InboundRackingPackingDispatch
Built by people from
  • Deloitte
  • IBM
  • Fuse Energy
  • LSE

Your worst day doesn’t start when the alarm goes off.

One incident, from the first trace in the data to the moment the floor feels it.

  1. First detectable signal

    Pick completion begins deviating from normal. No alarm, no threshold crossed, nothing on any dashboard.

  2. pAud detects emerging risk

    Multiple process signals begin moving together — the pattern, not the metric.

  3. Root cause identified

    Replenishment latency in Zone C, traced upstream from where the symptom appeared.

  4. Intervention recommended

    Rebalance labour and release the replenishment wave, while there is still time for it to work.

  5. Expected failure point

    Dispatch congestion. This is the moment the floor would have found out on its own.

How it works.

Reads the operational data your WMS, ERP, TMS and OMS already record. Read-only. Nothing gets replaced.

Your stack
  • WMSPicks, moves and stock positions
  • WCSConveyors, sorters, AMRs
  • DCOReplenishment and cover logic
  • OMSOrders, priorities and promises
  • ERPSAP, Oracle, NetSuite
  • TMSLoads, departures and carrier cutoffs
Read-only
  1. Learn12 months of history
  2. Predict09:47 — 5h 13m out
  3. Actbefore the 15:00 dispatch
  1. Learn

    Reconstructs an event log from the data your systems already record, and builds the failure patterns that repeat, using object-centric process mining. No rip and replace.

    A pattern it learned

    Sale list received with no quantities

    Central releases the list without quantities attached, so replenishment reacts to sale demand instead of planning for it.

    Seen
    8 times in 12 months
    Median disruption
    244 orders per occurrence
  2. Predict

    Flags the breakdown hours ahead, scored against live signal.

    One entry from the reasoning chain
    09:47Prediction

    5h 13m before the floor feels it

    Dispatch congestion forming, from replenishment latency in Zone C

    Process signals moving together — the pattern, not the metric. Pick completion has been deviating since 09:12; traced upstream, it starts in Zone C. Rebalance labour and release the replenishment wave — both are inside existing controls.

    The reasoning chain
    Where that prediction comes from — every detection and prediction traced back to the events it was drawn from, so the shift manager can check it rather than trust it.
  3. Act

    Ships the ranked fix to the person who can authorise it.

    The decision surface
    The interventions that were actually available on the day, ranked, each with its simulated outcome.
See it running →

Back into the operation through the person on shift — pAud never writes to your systems.

OperatorBuilt for the person running the floor.One screen, for the person who has to make the call.Open it runningExecutiveBuilt for the person who owns the network.One altitude up: which sites are exposed, what it costs if it lands, and whether anyone is already on it.Open it running

What did your warehouse know before your last bad day?

Send us 6 to 12 months of operational data from one DC. We come back with the failure patterns that repeat, how early each one was visible in the data, where it started, and what could have been done about it.

What we need from you. A data export from your WMS. No integration, no live system access, no IT project.

Run a warehouse failure diagnostic