These are live, interactive examples — not mockups. Enter your own asset data and see what the AI produces. Each is aligned to ISO 55000 / ISO 55001 and designed for mid-market ANZ operators.
CAPEX decisions in mid-market organisations almost always undercount the full lifecycle cost of the replacement option. This tool takes your asset parameters and uses AI to project a Total Cost of Ownership model across the asset's remaining life — separating acquisition, operation, maintenance, and end-of-life into a comparable view. The goal isn't a precise number; it's a defensible structure for the decision.
Asset type, current age, replacement cost, and operational environment. The AI anchors assumptions to your specific industry and asset class.
Annual OPEX, current maintenance spend, and expected failure frequency. Rough numbers are fine — the structure matters more than false precision.
The AI returns a projected TCO, a cost-component breakdown, the NPV of both options, and a recommended decision with ISO 55001 clause linkage.
Most mid-market asset owners default to time-based or reactive maintenance because developing a proper strategy takes specialist time they don't have. This tool takes your asset class, criticality, and operating context — and generates a structured maintenance strategy recommendation across the four main approaches, with RCM-aligned failure mode analysis and inspection interval guidance.
Asset type, criticality, current strategy, and consequence of failure. The more specific you are, the more targeted the output.
Whether the asset is condition-monitorable shapes which strategies are viable. The AI adjusts recommendations based on what's feasible for your context.
The output maps failure modes to maintenance tasks, specifies inspection intervals, and flags where a Digital Twin could improve the strategy.
Most Digital Twin projects in mid-market organisations fail at implementation — not because the technology is wrong, but because the foundational data and process conditions aren't ready. This diagnostic assesses your organisation's readiness across six dimensions, scores each one, and produces a prioritised roadmap for closing the gaps before you invest in DT technology.
Asset register quality, sensor coverage, CMMS maturity, and data governance. Be honest — the diagnostic value comes from accurate inputs.
Process maturity, change capability, and strategic alignment. A technically-ready organisation with low change capability will struggle to extract value from a DT.
The AI scores six dimensions, identifies the biggest gaps, and produces a phased approach — starting with what will have the most impact on readiness.