Many organisations operate with asset criticality classifications that were never truly evaluated — inherited through ERP migrations, copied from legacy systems, or assigned without structured methodology. The result is predictable: flawed maintenance prioritisation, reactive planning, and inventory strategies built on assumption rather than evidence.

We faced this challenge at scale. With thousands of assets incorrectly classified in our ERP, minor components were competing with major production-critical equipment for maintenance attention, planner bandwidth, and spare parts investment. The system wasn't just inefficient — it was actively working against us.

In response, we designed and implemented a dynamic, data-driven Equipment Criticality Analysis (ECA) program from the ground up. The work involved cleansing and reassessing over 9,000 asset lines, integrating failure mode modelling, and building a live Power BI visualisation layer — enabling real-time, value-based decision-making at both the planning and execution level.

The outcomes have been significant and measurable. Maintenance teams can now prioritise work with confidence, anchored to production value and quantified downtime risk rather than historical habit. Shutdown planners make faster, better-informed trade-offs when scope conflicts arise. And critically, the ECA outputs have reshaped our inventory strategy — driving targeted investment in high-risk critical spares while identifying and eliminating substantial volumes of obsolete stock, delivering clear financial returns.