Governance and risk for maintenance
Enforcing permission at retrieval is crucial for enterprise AI governance in maintenance, as it ensures sensitive data is only accessible to authorised personnel, such as shift supervisors, who need to check maintenance history for specific assets, and category managers, who compare supplier performance.
A maintenance manager at a large manufacturing plant is deciding this week which equipment to prioritise for scheduled maintenance, and currently bases this decision on a combination of experience, manual checks of the maintenance log, and ad-hoc reports from the shift supervisors, but this process can be improved with better governance and risk controls in place, particularly when using enterprise AI systems like AsscherAi to inform these decisions.
Enforcing permission at retrieval
Most organisations that implement AI systems for maintenance focus on instructing the model on what it can and cannot do, but this approach can be problematic when the system is used by people who did not build it, as they may not fully understand the nuances of the model's permissions and limitations. A better approach is to enforce permission at the point of retrieval, so that even if a user has access to the system, they can only retrieve the data they are authorised to see. This requires careful configuration of the system's access controls and a deep understanding of the organisation's security policies.
For example, a shift supervisor may need to check the maintenance history of a particular asset, but they should only be able to see the history for assets that are relevant to their shift, and not for other assets that they do not have responsibility for. By enforcing permission at retrieval, the organisation can ensure that sensitive data is only accessible to those who need it, and that the AI system is used in a way that is consistent with the organisation's security policies.
Auditability
Auditability is critical for maintaining trust in AI systems, particularly in high-stakes environments like maintenance, where the consequences of a mistake can be severe. This means being able to track what questions were asked, what answers were returned, and on what data the answers were based. This information can be used to identify potential errors or biases in the system, and to improve the overall performance of the AI.
In the context of maintenance, auditability can help to ensure that the AI system is providing accurate and reliable information, and that any decisions made based on this information are transparent and accountable. For example, if a maintenance manager uses the AI system to identify a potential issue with a piece of equipment, the audit trail can help to ensure that the decision to take action is based on reliable data, and that the actions taken are properly documented.
Maintenance data
The effectiveness of an AI system for maintenance depends on the quality and completeness of the underlying data, which typically includes work orders, asset history, spare parts inventory, and condition data where it exists. This data must be accurate, up-to-date, and properly formatted in order for the AI system to provide reliable and useful information. For example, a category manager may use the AI system to compare the performance of different suppliers, and to identify opportunities to optimise the spare parts inventory, but this requires access to high-quality data on supplier performance and inventory levels.
In order to ensure that the data is of high quality, organisations should implement robust data management processes, including regular data validation and cleansing, and should ensure that all relevant stakeholders have access to the data they need. This may involve integrating the AI system with other systems, such as production planning and inventory management, in order to provide a complete and accurate view of the maintenance environment.
Reactive work and recurring issues
One of the key challenges in maintenance is the tendency for reactive work to crowd out planned work, as maintenance teams respond to urgent issues rather than focusing on preventative maintenance. This can lead to a vicious cycle, where the same assets recur under different descriptions, and where maintenance teams are constantly firefighting rather than taking a proactive approach. The AI system can help to break this cycle by providing maintenance managers with the information they need to prioritise planned work, and to identify potential issues before they become major problems.
For example, a maintenance manager may use the AI system to identify assets that are at high risk of failure, and to prioritise planned maintenance activities accordingly. This can help to reduce the amount of reactive work, and to improve the overall efficiency and effectiveness of the maintenance team. By focusing on planned work, maintenance teams can also help to improve the overall reliability and performance of the assets, which can have a positive impact on the organisation's bottom line.
Measuring performance
In order to evaluate the effectiveness of the AI system, organisations should track key performance metrics, such as the planned versus reactive ratio, mean time between failures, and spares availability. These metrics can help to identify areas for improvement, and to evaluate the overall impact of the AI system on the maintenance environment. For example, if the planned versus reactive ratio is improving, this may indicate that the AI system is helping to reduce the amount of reactive work, and to improve the overall efficiency and effectiveness of the maintenance team.
Organisations should also consider tracking other metrics, such as the number of maintenance requests, the time to complete maintenance activities, and the overall satisfaction of maintenance teams with the AI system. By tracking these metrics, organisations can get a complete picture of the AI system's performance, and can make informed decisions about how to improve and optimise the system. For more information on how to get started with AsscherAi, please contact us.
Limitations and caveats
While the AI system can be a powerful tool for improving maintenance, it is not a panacea, and there are certain limitations and caveats that organisations should be aware of. For example, the AI system is only as good as the data it is trained on, and if the data is incomplete or inaccurate, the system's performance will suffer. Additionally, the AI system should not be relied upon as the sole source of information for maintenance decisions, but rather as one tool among many that maintenance teams can use to inform their decisions.
Organisations should also be aware that the AI system may not be suitable for all types of maintenance activities, and that certain tasks may still require human judgment and expertise. By understanding these limitations and caveats, organisations can use the AI system in a way that is consistent with their overall maintenance strategy, and that helps to improve the overall efficiency and effectiveness of their maintenance teams.
Frequently asked questions
How can we ensure secure access to maintenance data with enterprise AI?
Enforce permission at retrieval to restrict access to sensitive data for authorised personnel only.
What is the importance of auditability in maintenance AI systems?
Auditability helps track questions, answers, and data used, ensuring transparency and accountability in decision-making.
How can we improve the effectiveness of AI systems for maintenance?
Implement robust data management processes, including regular validation and cleansing, to provide high-quality data.
Can AI systems help reduce reactive maintenance work?
Yes, AI systems can help identify potential issues before they become major problems, allowing maintenance teams to prioritise planned work.