• Written by Sonali Subhadarsini, Product Manager

A dashboard that nobody opens, filled with metrics on equipment uptime and maintenance schedules, is a common sight in many automotive manufacturing plants, a testament to the fact that maintenance teams often struggle to get the insights they need from their data, and this is where AI for maintenance in automotive can help, by allowing them to query their own data in natural language and get answers in real time.

The maintenance questions that currently wait on a report

The maintenance team in an automotive plant typically has to wait for a report to be generated before they can get answers to questions like what is the current status of the production line, or which machines are due for maintenance, and this can lead to delays and inefficiencies, as the shift supervisor has to rely on manual checks and updates from the maintenance crew, rather than being able to get the information they need quickly and easily, and this is particularly problematic in a tiered supply chain with tight takt times and long programmes, where any disruption can have a ripple effect throughout the entire production process.

For example, a shift supervisor might need to know which machines are currently offline, and why, in order to plan the day's production schedule, but if they have to wait for a report to be generated, they may not get this information until it is too late to make any changes, and this can lead to wasted time and resources, as well as potential quality control issues, and it is not just the shift supervisor who is affected, as the category manager may also need to know which suppliers are providing spare parts, and when they can expect deliveries, in order to plan their inventory and minimize downtime.

The data behind them: work orders, asset history, spare parts inventory, condition data where it exists

The data that the maintenance team needs to answer their questions is typically scattered across different systems and departments, including work orders, asset history, spare parts inventory, and condition data, where it exists, and this can make it difficult to get a complete picture of the current state of the production line, and to identify potential issues before they become major problems, and this is where AI for maintenance in automotive can help, by providing a single platform for querying and analyzing this data, and providing insights and recommendations to the maintenance team.

The asset history, for example, can provide valuable information on the maintenance and repair history of each machine, including when it was last serviced, and what parts were replaced, and this can help the maintenance team to identify potential issues before they become major problems, and to plan their maintenance schedules more effectively, and the spare parts inventory can provide information on what parts are currently in stock, and when new deliveries are expected, and this can help the maintenance team to minimize downtime, and to ensure that they have the parts they need to complete any necessary repairs.

Sector constraints: supplier escalations and engineering change propagation

The automotive sector has a number of constraints that can make it difficult to implement AI for maintenance, including supplier escalations and engineering change propagation, and these constraints can have a major impact on the effectiveness of any AI system, and must be carefully considered when designing and implementing the system, and this is where the experience of the category manager can be invaluable, as they have a deep understanding of the supply chain, and can help to identify potential issues and develop strategies for mitigating them.

For example, if a supplier is experiencing delays or quality control issues, this can have a ripple effect throughout the entire production process, and the maintenance team may need to adjust their schedules and plans accordingly, and this is where AI for maintenance in automotive can help, by providing real-time insights and recommendations, and enabling the maintenance team to respond quickly and effectively to any changes or disruptions, and the production team can also benefit from this, as they can get a better understanding of the current state of the production line, and plan their production schedules more effectively.

What improvement looks like: planned versus reactive ratio, mean time between failures, spares availability

Improvement in the maintenance team's performance can be measured in a number of ways, including the planned versus reactive ratio, mean time between failures, and spares availability, and these metrics can provide valuable insights into the effectiveness of the maintenance team, and the impact of AI for maintenance in automotive, and can help to identify areas for further improvement, and this is where the shift supervisor can play a key role, as they have a deep understanding of the production line, and can help to identify potential issues and develop strategies for mitigating them.

For example, if the planned versus reactive ratio is improving, this can indicate that the maintenance team is becoming more proactive, and is able to identify and address potential issues before they become major problems, and this can lead to a reduction in downtime, and an improvement in overall productivity, and the mean time between failures can also provide valuable insights, as it can indicate how often machines are failing, and how quickly the maintenance team is able to repair them, and the spares availability can help to minimize downtime, by ensuring that the maintenance team has the parts they need to complete any necessary repairs.

What this does not do, or when it is the wrong choice

AI for maintenance in automotive is not a replacement for human judgment and expertise, and it is not a solution that can be implemented overnight, and it requires careful planning, and a deep understanding of the production line, and the maintenance team's needs and challenges, and this is where the experience of the category manager, and the shift supervisor can be invaluable, as they have a deep understanding of the supply chain, and the production line, and can help to identify potential issues, and develop strategies for mitigating them, and if you are considering implementing AI for maintenance in automotive, and you want to learn more about how it can help your organization, you can contact us to discuss your needs, and develop a plan that is tailored to your specific requirements.