A CMMS (Computerised Maintenance Management System) running for twenty years can hold hundreds of thousands of logged work orders. That data is already paid for: it cost hours of typing, year after year. Yet it sleeps, unreadable, never read back. Industrial intelligence platforms such as Mimorian structure this raw material and cross it with equipment modelling, to support fault diagnosis for maintenance teams and to turn history back into a basis for decisions.
Faced with a reliability project, the reflex is often to add sensors and fresh data. Before that, one question deserves an answer: what are we doing with the data we already hold?
The data mine nobody works
Take a precision machining subcontractor spread across fifteen or so sites. Its in-house CMMS holds years of history and thousands of work orders every year. A mine. And nobody really reads it.
Why does this data sleep? Because it is heterogeneous. Free-text fields filled in at speed, different wording from one technician to the next, no tool to read it across the board. The material exists, but it stays unusable as it stands.
This waste has a price. Panopto's report on workplace knowledge puts the productivity lost in a large company through poor knowledge sharing at 47 million dollars a year. Maintenance history that is never read back is a direct share of that waste: the knowledge is written down, but it reaches nobody.
Why raw history is not enough
The problem is not volume. It is structure. The ISO 14224 standard states that a reliability record only has value when it is tied to an item of equipment, a cause, a failure mode and an action. A history of free-text fields carries none of that structure.
As long as each work order stays an isolated sentence, recurrences cannot surface. The work orders have to be linked to one another: which component, which symptom, which functional chain. That linking work is what turns a pile of lines into usable knowledge.
💡 The data you are looking for is already in your plant: paid for, typed in, stored. It is only waiting to be structured before it starts to speak.
Waking the data to anticipate recurring faults
Mimorian reads the natural language of the reports, with no prior configuration, and brings together the work orders that resemble one another: same symptom, same component, same functional chain. Repeat failures surface on their own.
A telling example: showing that the same component has been replaced three times in six months. The maintenance manager now holds a reliability signal. They set a threshold, move from reactive replacement to targeted preventive work, and stop paying for the same failure over and over.
The decision maker, for their part, does not want AI for its own sake. They want gains they can measure: diagnostic hours saved, stoppages avoided. Data that has already been paid for is the cheapest lever to get them, because it is already there.
Frequently asked questions
Do you need sensors to make use of maintenance history? No. CMMS history and technical documentation are enough to surface recurrences. Sensors are a separate subject, that of real-time monitoring.
Our history is poorly filled in, is it still usable? Yes. Natural language analysis copes with free-text fields and varied wording. It brings similar cases together without demanding perfect data entry upstream.
How is this different from a CMMS dashboard? A dashboard displays what was tagged in advance. Here, recurrences emerge from what the technician actually wrote, with no manual sorting beforehand.
Conclusion
Three points to remember.
- Your history is an asset, not an archive: it is already paid for and holds the recurrences you are looking for.
- Volume is worth nothing without structure: tying each work order to an item of equipment and a cause changes everything.
- The cheapest gain is the data you already have: before adding sensors, work what is sleeping in the CMMS.
Next step for a reliability engineer or a maintenance methods manager: pull one year of history on a critical item of equipment and count how many times the same failure comes back.
To go further, read our complete guide: Capitalisation du savoir-faire en maintenance industrielle.