Priorities for a reliability engineer

The job starts with data

A reliability engineer's core job is to identify and fix the maintenance problems driving cost across a site with many assets — but that starts with transactional data, primarily the maintenance notifications and work orders raised by frontline crews. That data captures both scheduled and unplanned work, records which parts were replaced, and carries the cost associated with each job. A single failure event can also trigger the replacement of other, related parts on the same machine, which has to be accounted for too.

Cleaning up the record before analysing it

Getting an accurate part-replacement history right requires care on several fronts. Parts that were removed but hadn't actually failed need to be flagged and treated differently in the analysis, rather than being lumped in with genuine failures. Parts that are functionally identical but carry different manufacturer codes need to be reconciled as the same part. And the underlying records themselves are sometimes incomplete or wrong — often the result of time pressure at the frontline, or urgent orders placed with multiple suppliers under deadline.

Getting that history right depends heavily on combining data with process expertise and maintenance context — internal knowledge of how the site actually operates, and external best practice where it's available.

What the clean data is actually used for

Once the historical picture is solid, it feeds directly into calculating the failure distribution for each part — the basis for balancing an acceptable level of risk, including safety risk, against the cost of downtime. That's the analytical core of the reliability engineer's role.

The practical constraint is scale: with so many parts spread across multiple assets and sites, only the highest-cost components have traditionally been analysed with any rigour, simply because there isn't time to do the same for everything else.

Scaling the analysis to every part

IronMan® is built to remove that constraint, automating the data extraction, transformation and loading process, calculating part lives, and surfacing the insights a reliability engineer would otherwise have to build by hand. The effect is that the 80% of time historically spent reconstructing history can instead go toward acting on what the data shows — across every part on the fleet, not just the most expensive ones.