The problem
Discrepancies are discovered when it is already too late.
Recurring inventory adjustments, anomalies in SAP movements, gaps between physical and accounting stock: today these are handled after the fact, through annual audits, manual reconciliations and conversations that arrive months after the event. In the meantime the process keeps producing the same errors.
Late discovery
Discrepancies surface at close or audit, too late to act on the process that produced them.
Background noise
Thousands of daily movements saturate traditional parametric controls, producing alerts that are indistinguishable from noise.
Unstructured root cause
Cause analysis is left to experts who repeat the same investigative work each time, with no cumulative memory.
How it works
Three control layers working together.
Parametric controls
A library of deterministic controls applied to SAP MM movements: thresholds, recurring patterns, quantitative anomalies. It covers known cases with explicit, maintainable rules.
ML normality engine
Machine learning models that learn each plant's normal patterns and flag statistically significant deviations. They catch cases rules miss, keeping the noise floor low.
Investigative copilot
An AI agent that guides the analyst through root cause analysis: it reconstructs the context of the anomaly, queries related data, proposes hypotheses and records conclusions to reuse on future cases.
Results
From reaction to prevention.
Proactive interception
Discrepancies are detected as they are generated, not at the end of the fiscal year.
Signal-to-noise ratio
Alerts prioritised by impact and context: the analyst sees what matters first.
Investigative memory
Every analysis feeds the knowledge base: recurring cases are recognised automatically.
Cross-plant benchmarks
Comparison across plants to identify best practices and processes to align.
Your warehouse produces signals every day.
Are you listening?
We start with an assessment over your SAP MM movements and show you, on your own history, which discrepancies could have been caught in real time.
Book an assessment →