Machine Learning

Nexaan APM

Asset performance management for mining and heavy industry

An 11-module governed APM platform that standardizes maintenance strategy engineering, generates CMMS load sheets, and improves strategy using ML on field failure history.

View Nexaan APM

250%

ROI across brownfield ops

Engagement-reported

500%

ROI on operational readiness

Engagement-reported

70%

Reduction in strategy time

Engagement-reported

A$1.12M

Annual cost/risk opportunity

Engagement-reported

Numeric figures are engagement-reported from published project work. Baselines and measurement windows are shared with qualified buyers under NDA.

Business challenge

Mining operations face duplicate work tasks, unoptimized shutdown scopes, and high operational risk. Traditional asset management fails at handoffs: engineering logic gets lost in manual data entry, free-text work order history goes unanalyzed, and local fixes rarely scale across identical fleet assets.

Why AI

Years of unstructured work order text in SAP/Maximo cannot be analyzed manually at scale. ML classification of failure mechanisms plus engineer-trained RCM drafting is the right fit when strategy must improve from field evidence—not static templates.

Business outcomes

Manual data entry and duplicate analysis drop; engineering time shifts to high-value risk decisions. Downtime and availability modeling are backed by auditable evidence; failure logic accumulates in the client tenant—not individual staff notebooks.

Delivery approach

How the solution was implemented.

Teams build asset hierarchies, inherit and tune RCM from master classes, bundle tasks for CMMS export, then ingest field execution data to surface failure trends and push approved optimizations back into strategy.

Solution architecture

Single unified data model across 11 strategy and governance modules
Engineer-trained ML for free-text NLP classification and RAM modeling
3-tier asset classification down to maintainable items and parts
Native integration loaders for SAP PM and IBM Maximo
Audit-first governance logging for strategy edits and engineer sign-off

AI capabilities delivered

AI-drafted RCM specifications from asset libraries
Work order analyser mapping failure mechanism and root cause
PM optimization on failure evidence
Automated CMMS load sheet generation

Outcome detail

Reclaimed engineering hours from eliminated duplicate analysis
Defensible board reporting with auditable RAM and downtime modeling
Enterprise knowledge retention in the client tenant

Technical highlights

What made the delivery work.

Unified data model across 11 APM modules

ML on unstructured work order history

Direct SAP PM / Maximo load sheet export

Engineer sign-off and audit logging on every strategy change

Enterprise technologies used

Python / machine learning engineAWS private cloudSAP PM & IBM Maximo integrationRAM & fault tree analysisRCM automation

Lessons learned

APM value compounds when CMMS execution feeds back into strategy—not one-way exports.
Free-text work orders are signal once NLP classification is engineer-governed.
Single-tenant client environments matter for regulated heavy industry.

Discuss a similar AI initiative.

Thirty minutes with an architect to pressure-test fit, constraints, and a practical first slice, NDA available for qualified opportunities.