AI features in Maximo

AI in maintenance: From searching to decision-making

Which piece of equipment needs my attention next? This is precisely where AI in maintenance demonstrates how it supports decision-making.

A simple question. Yet in many maintenance systems, this is still followed by a search through work orders, documents, histories and free-text entries.

Understanding data rather than searching for it

In day-to-day operations, it quickly becomes clear at such moments just how time-consuming it is to make effective use of existing information. Data is available, but it is scattered, incomplete or can only be consolidated with considerable effort. Decisions are then often made under time pressure and not always on a clear basis – this is precisely where data-driven maintenance comes in.

The IBM Maximo Application Suite creates the conditions necessary for artificial intelligence to be effective in day-to-day maintenance. Data from EAM, ERP and sensor systems is consolidated and structured in such a way that it can be used for analysis.

Identifying patterns

Building on this, several modules work together, with AI taking on specific tasks in each and passing on information within the process. This creates a form of data-driven maintenance that goes beyond individual analyses.

In day-to-day operations, this becomes apparent more quickly than one might expect. Histories can be analysed in a targeted manner because similar processes are identified and linked together. Recurring patterns and typical fault patterns become visible without the need for prior manual analysis – a clear advantage of intelligent maintenance systems.

AI in practical use

As a result, decisions are based more on consolidated data and identified correlations than on individual empirical values. It is precisely here that AI reveals its true value in maintenance.