Asset Performance & AI
If you’re an asset manager, Secora now gives you multiple AI-powered ways to make faster, better asset management decisions to increase asset lifecycle performance.
WHO WE ARE
Secora is a specialist asset management and performance improvement firm with over 20 years of experience working with Australia’s major resource and industrial operators. We combine deep operational expertise with advanced AI and analytics capability — built not as a technology overlay, but as a natural extension of how we solve complex maintenance and reliability problems.
Our roots are in the European automotive industry, and we bring that manufacturing mindset — zero-defect thinking, relentless efficiency, and intolerance for waste — to our clients across resources and oil & gas, infrastructure and utilities, and government and defence.
OUR WORK
Secora has delivered AI-enabled asset management and performance improvement programmes across mining, energy, utilities, and defence. Our work spans AI-embedded analytics, asset strategy, and capability development:
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Asset Health Metric
An AI-embedded asset risk and criticality metric supporting sustaining capital allocation decisions. Built on the ISO55000/55001 framework, it automates complex manual assessment — providing a propensity metric for critical fleet risk and identifying where the next significant capital surprise may emerge.
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One-Page Asset Management Plans
Concise, structured and completely customisable asset management plans aligned to the ISO 55000/55001 framework, designed to make strategy visible and actionable at the asset level.
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AI Opportunity Identification & Analysis
Automated identification and analysis of asset performance improvement opportunities using Python-based AI tools, surfacing insights from large maintenance datasets that would be impractical to detect through manual analysis.
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Generative AI for Work Instructions
AI-generated, context-aware maintenance work instructions across utilities and Defence — improving consistency, reducing authoring time, and supporting standardisation at scale.
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AI-enabled Maintenance Optimisation
End-to-end AI programme for a major tier 1 asset operator targeting PM reduction and corrective maintenance optimisation (across multiple sectors). Using AI to identify duplicate and redundant tasks, correlate asset performance data with maintenance routines, and validate opportunities for deferral or scope reduction.
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AI Upskilling
Structured sessions to build internal client capability —enabling teams to work with, interrogate, and draw value from AI-generated insights in their day-to-day roles.
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HOW WE WORK
We approach every engagement as operators and problem-solvers first. Our AI and analytics capability is a tool in service of real outcomes — not a product to be sold. In practice, this means:
We start with discovery — understanding the actual problem before proposing a solution
We favour targeted pilots and test-and-learn approaches over large, slow programmes
We are honest about what analytics can and cannot do — including the change governance effort required to translate insights into operational impact
We work collaboratively with internal teams and complement, rather than displace, existing improvement programmes
We develop solutions within client environments — not proprietary platforms — so value stays with the client
OUR APPROACH –
RISK AND RELIABILITY CENTRED MAINTENANCE
Most organisations measure Schedule Adherence as their primary maintenance KPI. It tells you whether planned work was completed on time. It does not tell you whether that work reduced risk, improved reliability, or was even worth doing at all.
Is the right work being done, at the right frequency, on the right assets?
Secora’s programmes are structured around this question. We apply a three-tier KPI architecture aligned to RRCM doctrine — all derivable from data the organisation already holds:
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Tier 1: Equipment outcomes
Defect rate per asset stratified by criticality class; repeat failure rate within 90 days of maintenance action; time since last defect as a standing trend signal.
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Tier 2: Programme effectiveness
PM-to-CM ratio by criticality tier; overdue critical PM rate with auto-escalation; backlog delay ratio —target <5% of active backlog at delay ratio ≥2.0.
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Tier 3: Strategic maturity
Evidence-triggered strategy revision rate; condition-based monitoring coverage of high-criticality assets; Maintenance-Induced Risk Exposure (MIRE) index — deferred PM risk weighted by consequence.
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Secora’s programmes are structured around this one question:
Is the right work being done, at the right frequency, on the right assets?
Secora is currently delivering two AI pilots for a major global energy operator as part of a broader maintenance optimisation programme. Both are live within the client’s enterprise data environment:<hr>
Pilot 1: Dynamic Criticality & Priority Tool
A real-time work order prioritisation overlay that integrates backlog ageing, redundancy status, and asset condition signals to reorder the maintenance queue dynamically. Replicates experienced human judgement at scale across thousands of open work orders — surfacing what needs to be done now based on current risk, not last month’s plan.
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Pilot 2: Work Order Quality Tool
An AI-assisted work order assessment tool integrated directly with the client’s CMMS. Evaluates each work order before it reaches the planning stage — surfacing scope gaps, standardisation failures, duplicate tasks, and defects requiring reliability escalation. Improves the quality of work entering the system, not just the work that comes out.
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Both pilots are built on a prototype AI architecture demonstrating autonomous query of the client’s maintenance data environment — formulating database queries, retrieving structured data, and surfacing insights without manual analyst intervention. The capability is designed to scale across additional asset classes and maintenance programmes as the pilots validate.
OUR CURRENT PILOT PROGRAMMES
CONTACT
Jonathon Gregor | ED Strategy & Business Development
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