Failure-probability where the data supports it, integrated into work that actually happens

IBM Maximo Predict applies analytics to failure history and operational signal to estimate the probability that an asset will fail in a given window. The output is not the answer — it is an additional, defensible input into the maintenance plan that the reliability function and planners already own. Done well, it shifts maintenance spend off the wrong assets and onto the right ones, and gives the executive team something to defend the plan with at a budget review.

Where Predict pays off and where it does not

Predict pays off when four things are true at once. Failure history in Manage is rich enough to learn from. The asset population is large enough for a model to generalise. The cost of unplanned failure is high enough to justify the investment. And there is a reliability function that will use the output, not just receive it.

When any of those is missing, the project produces dashboards that nobody acts on. We are explicit about this before scoping. Manage data discipline, work coding, and the right candidate asset class are honest prerequisites. Why predictive maintenance programmes fail sets out our position in detail.

What we deliver

• Readiness assessment on Manage data quality, candidate asset classes and the reliability operating model
• Data engineering: failure code cleanup, work history alignment, feature build from Monitor signal where available
• Model build and validation using the MAS Predict model lifecycle, against historical failure events
• Integration into Manage so output drives PM intervals, inspection scope or work prioritisation, not a separate dashboard
• Operating model for the reliability and planning teams to interpret, retrain and challenge the model over time
• Managed run-state on our cloud, with model lifecycle, retraining and observability owned by the same team that runs Manage

Where Predict sits in the maintenance maturity ladder

The asset-management community has a long-standing way of describing maintenance strategies, and it helps to be honest about where Predict belongs in it. The strategies are, in rough order of maturity: run-to-failure (let it fail, replace it), calendar-based (do it every six months whether it needs it or not), usage-based (do it every 1,000 hours), condition-based (do it when condition data says it needs it), predictive (do it before the predicted failure window), and risk-based / financially-optimised (do the work that maximises return against the operating constraint).

Most asset-intensive operators run several of these in parallel, on different asset classes, and that is the right answer. Predict is the predictive rung. Monitor takes you to condition-based. Health and AIO take you to risk-based and financially-optimised. The mistake is treating any one of them as the universal answer. The right strategy for a light bulb is run-to-failure; the right strategy for a critical safety system is not.

Sequencing matters

Predict is rarely the first MAS suite component to deploy. For most operators it sits naturally after Monitor on the same asset class, and benefits from Health for criticality scoring. The detailed sequencing argument is in sequencing Monitor and Predict after Manage.