Unlock predictive intelligence from your SAP investment

Unplanned downtime costs 3–5x more than planned maintenance. These five models convert reactive SAP PM into a predictive system — with each model feeding the next in a dependency chain that pre-positions spare parts, ranks work orders by real criticality, and identifies root causes before engineers start guessing.

The five models work as an interconnected system. The failure prediction model feeds the spare parts model — when an asset is flagged at risk, parts are pre-positioned before a work order exists. The RUL model feeds work order prioritisation — an asset with 4 days of useful life ranks higher than one with 30 days, automatically. RCA closes the loop — every breakdown investigation makes failure prediction smarter for next time.

Finance ML works across three time horizons: real-time fraud detection at the point of each posting, forward-looking 30/60/90-day treasury intelligence, and monthly close acceleration — together giving your CFO a finance function that acts on information rather than on 8-day-old data.

The combination of real-time fraud detection and financial close automation means your finance team stops being a reporting function and becomes a decision-support function. When close takes 3 days instead of 8, the CFO has 5 additional days per month to act on the numbers rather than produce them.

In EHS, a false negative is not a metric — it is a life. Every model in this domain is tuned for maximum recall, and cross-plant knowledge graph traversal is the capability that makes EHS ML genuinely life-saving rather than just operationally useful.

Free text is the richest untapped EHS data source. Structured SAP damage codes and cause codes capture what your data entry process allows — which is rarely the full story. The near-miss model reads what workers actually wrote in the long text fields. That narrative contains the mechanism of the hazard in a way that a dropdown code never can.