• Written by Muhammed Abdulla NC, CEO

A maintenance manager checks the work order queue on a typical Monday morning, looking for opportunities to reduce downtime and increase productivity, with the help of enterprise AI adoption for maintenance.

Attaching new capability to existing routines

Most people get it wrong when they think that adopting new technology means overhauling existing processes, but this is not the case. By attaching new capability to a routine that already exists, organisations can make the most of their investment in enterprise AI for maintenance. This approach allows maintenance teams to build on their existing workflows and expertise, rather than trying to replace them entirely.

A shift supervisor, for example, can use AsscherAi to inform their daily routine of checking the maintenance log and prioritising tasks, without having to drastically change the way they work. This approach helps to increase the chances of successful adoption, as it minimises disruption to existing workflows.

Who needs to be able to ask, not just who needs the answer

When considering the adoption of enterprise AI for maintenance, it's crucial to think about who needs to be able to ask questions, not just who needs the answer. This means involving a range of stakeholders, from maintenance technicians to production managers, in the process of defining what questions need to be answered. By doing so, organisations can ensure that the right people have access to the insights they need to make informed decisions.

This approach also helps to identify potential roadblocks to adoption, such as lack of training or inadequate access to data. By addressing these issues upfront, organisations can increase the chances of successful adoption and ensure that the benefits of enterprise AI for maintenance are realised.

Maintenance data requirements

The effective use of enterprise AI for maintenance depends on having access to high-quality data, including work orders, asset history, spare parts inventory, and condition data where it exists. This data provides the foundation for informed decision-making and enables maintenance teams to identify trends and patterns that might not be immediately apparent. By linking this data to production schedules and workflows, organisations can gain a more complete understanding of their maintenance needs and make more effective use of their resources.

For example, a maintenance team might use data on work orders and asset history to identify recurring issues with a particular piece of equipment, and then use this information to inform their maintenance scheduling and spare parts procurement.

Changing the balance between reactive and planned work

One of the key benefits of adopting enterprise AI for maintenance is that it can help to change the balance between reactive and planned work. By providing maintenance teams with the insights they need to identify and address potential issues before they become major problems, organisations can reduce the amount of reactive work and increase the amount of planned work. This can help to reduce downtime, increase productivity, and improve overall maintenance efficiency.

This shift from reactive to planned work can also help to reduce the problem of the same asset recurring under different descriptions, which can make it difficult to identify and address underlying issues. By using enterprise AI to analyse data on maintenance history and asset performance, organisations can gain a more complete understanding of their assets and make more effective use of their maintenance resources.

Measuring success

So how can organisations measure the success of their enterprise AI adoption for maintenance? One approach is to track the planned versus reactive ratio, which can help to identify changes in the balance between planned and reactive work. Other metrics, such as mean time between failures and spares availability, can also provide valuable insights into the effectiveness of maintenance operations. By using these metrics to inform their decision-making, organisations can ensure that they are getting the most out of their investment in enterprise AI for maintenance.

For example, a maintenance manager might use data on mean time between failures to identify areas where maintenance scheduling could be improved, and then use this information to adjust their maintenance workflows and reduce downtime.

Limitations and exceptions

While enterprise AI for maintenance can be a powerful tool for improving maintenance efficiency and reducing downtime, it is not a silver bullet. There are certain situations where it may not be the best choice, such as where data quality is poor or where maintenance operations are highly variable. In these cases, other approaches, such as consulting with maintenance experts, may be more effective. By understanding the limitations and exceptions of enterprise AI for maintenance, organisations can ensure that they are using the right tools for the job and getting the most out of their investment.