pAud is an operational intelligence platform that helps warehouse teams predict and prevent disruptions before they impact the floor. It reads across WMS, OMS, and TMS systems to identify emerging risks and recommend interventions, giving shift leaders the visibility to act early. The platform uses object-centric process mining to trace shared causes of delays across orders, resources, and equipment.
Funding
Funding not disclosed
Founders
Product
Problem
Warehouse operations generate millions of data points daily across WMS, TMS, and OMS systems, but traditional process mining tools flatten this data into single-case timelines that miss shared causes of delays. When a resource like a picker or a packing lane is delayed, the impact appears as hundreds of unrelated order-level anomalies rather than one systemic issue, leaving teams blind to what is actually about to break until it is too late.
Solution
pAud provides an operational intelligence platform that reads across a warehouse's existing WMS, OMS, TMS, and ERP systems to detect emerging risks and predict failures before they impact the floor. Using object-centric process mining, the platform reconstructs event logs that track multiple objects—orders, pickers, vehicles, and dock slots—as interconnected entities, revealing shared-cause patterns that flattened models miss. The system learns from 6 to 12 months of historical data to build failure patterns, then generates predictions with traced reasoning chains, showing exactly when and where a disruption will occur. For each predicted issue, pAud ranks available interventions with simulated outcomes, allowing the person on shift to approve actions directly from the console without writing to or replacing existing systems.
Target Audience
Primary users are warehouse shift managers and operations leaders who need real-time visibility into emerging risks, as well as network-level executives responsible for monitoring exposure across multiple distribution centres.
Features
- Object-centric process mining that models orders, pickers, stations, and vehicles as separate objects to correctly attribute shared-cause delays
- Read-only integration with WMS, WCS, DCO, OMS, ERP, and TMS systems, requiring no rip-and-replace of existing infrastructure
- Predictive control centre that forecasts failures hours in advance, with each prediction traced back to the specific events that triggered it
- Decision surface that ranks available interventions by simulated outcome, letting operators compare baseline versus intervention scenarios side-by-side
- Process explorer and variant explorer that discover the actual operational flow from data, ranking hundreds of process variants by frequency and duration deviation
- Executive view that aggregates risk across multiple sites, showing which locations are exposed and whether interventions are already underway