• Written by Sonali Subhadarsini, Product Manager

A shift supervisor checks the maintenance log on a typical Monday morning, only to find a long list of urgent repairs that need to be addressed, leaving little time for planned maintenance tasks. This scenario is all too familiar in many organisations, where reactive work tends to crowd out planned work, leading to a vicious cycle of inefficiency. The introduction of AI-powered tools, such as AsscherAi, aims to change this dynamic by providing real-time insights into maintenance data, potentially freeing up time for more strategic and proactive work.

The difference between time saved and time redeployed

While AI productivity in the enterprise for maintenance is often touted as a time-saving solution, it's essential to distinguish between time saved and time redeployed. Time saved refers to the reduction in hours spent on a specific task, whereas time redeployed refers to the reallocation of those hours to more valuable activities. In the context of maintenance, time redeployed might mean that technicians can focus on planned maintenance, reducing the likelihood of equipment failures and subsequent downtime. This subtle distinction is crucial, as it highlights the potential for AI to not only reduce workload but also to enable more strategic and proactive work.

The category manager, responsible for overseeing the maintenance of equipment and assets, can attest to the fact that time saved on routine tasks can be redeployed to more critical activities, such as analysing condition data to predict potential failures. By doing so, the category manager can work closely with the shift supervisor to ensure that planned maintenance is given the attention it deserves, rather than being constantly overshadowed by reactive work.

Why self-reported time savings overstate the effect

Self-reported time savings often overstate the actual impact of AI-powered tools, as they tend to focus on the initial reduction in workload rather than the long-term benefits. In reality, the time saved may be absorbed by other tasks or responsibilities, rather than being redeployed to more strategic activities. Furthermore, self-reported time savings may not account for the potential increase in workload that can result from the introduction of new tools and processes. A more nuanced understanding of the relationship between time saved and time redeployed is necessary to accurately assess the benefits of AI productivity in maintenance.

The maintenance data this depends on

The effectiveness of AI-powered tools in maintenance depends on the quality and availability of relevant data, including work orders, asset history, spare parts inventory, and condition data. This data provides the foundation for real-time insights and predictive analytics, enabling maintenance teams to make informed decisions about resource allocation and prioritisation. The shift supervisor, for instance, can use this data to identify patterns and trends in equipment failures, allowing for more targeted and proactive maintenance strategies.

In the production environment, access to accurate and up-to-date maintenance data is critical for optimising resource allocation and minimising downtime. By leveraging this data, organisations can unlock new opportunities for productivity gains and improved maintenance outcomes.

What it changes about reactive work and planned work

The introduction of AI-powered tools can significantly alter the dynamics between reactive and planned work in maintenance. By providing real-time insights into equipment condition and performance, these tools can help reduce the incidence of reactive work, freeing up time for more proactive and strategic activities. This, in turn, can lead to a reduction in the number of recurring failures and a decrease in downtime, as maintenance teams are better equipped to identify and address potential issues before they become major problems.

A common challenge in maintenance is the tendency for the same asset to recur under different descriptions, making it difficult to track and analyse maintenance history. AI-powered tools can help mitigate this issue by providing a more comprehensive and integrated view of maintenance data, enabling teams to identify patterns and trends that might otherwise go unnoticed.

Measured with planned versus reactive ratio, mean time between failures, and spares availability

The impact of AI productivity in maintenance can be measured using a range of key performance indicators, including the planned versus reactive ratio, mean time between failures, and spares availability. These metrics provide a more nuanced understanding of the benefits of AI-powered tools, beyond simple time savings. By tracking these metrics, organisations can gain a deeper insight into the effectiveness of their maintenance strategies and identify areas for further improvement.

For instance, a decrease in the planned versus reactive ratio may indicate that maintenance teams are spending more time on proactive activities, such as planned maintenance, rather than reactive work. Similarly, an increase in mean time between failures may suggest that equipment is being better maintained, reducing the likelihood of downtime and associated losses.

What this does not do

While AI-powered tools can bring significant benefits to maintenance operations, they are not a panacea for all productivity challenges. In some cases, the introduction of new tools and processes may not be the best solution, particularly if the underlying maintenance data is incomplete, inaccurate, or inconsistent. In such cases, it may be more effective to focus on improving data quality and integrity before introducing AI-powered tools. For organisations seeking to learn more about how AsscherAi can support their maintenance operations, please visit our contact page to get in touch with our team.