• Written by Sonali Subhadarsini, Product Manager

A maintenance team in a metals and mining operation often spends a significant amount of time waiting for reports to be generated, only to find that the information they need is not readily available or is buried beneath a mountain of irrelevant data. This can lead to delays in addressing critical maintenance issues, resulting in decreased productivity and increased costs. By leveraging AI for maintenance in metals and mining, teams can begin to ask more nuanced questions of their own data, such as what assets are most likely to fail, or which spare parts are most critical to keep in stock.

The maintenance questions that currently wait on a report

Many maintenance teams rely on periodic reports to inform their decisions, but these reports often do not provide the level of detail needed to effectively manage maintenance activities. For example, a shift supervisor may need to know which equipment is due for maintenance, or which spare parts are running low. Currently, these questions may wait on a report from the maintenance manager, which can take hours or even days to generate. By using AI to analyze their own data, maintenance teams can begin to ask these questions in real-time, and receive answers that are tailored to their specific needs.

A category manager, responsible for procuring spare parts, may want to know which parts are most frequently used, or which suppliers are most reliable. This information can be difficult to come by, especially if the category manager is relying on manual reports or spreadsheets. By using AI to analyze procurement data, the category manager can quickly identify trends and patterns, and make more informed decisions about which parts to stock and which suppliers to use.

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

The data that underlies maintenance activities in metals and mining is complex and multifaceted. Work orders, asset history, spare parts inventory, and condition data all play a critical role in informing maintenance decisions. For example, a maintenance manager may use work order data to identify which assets are most prone to failure, or which maintenance activities are most effective. Asset history data can provide valuable insights into the performance of specific equipment over time, while spare parts inventory data can help maintenance teams ensure that they have the parts they need on hand.

Condition data, where it exists, can provide real-time insights into the performance of critical assets, allowing maintenance teams to take proactive steps to prevent failures. By analyzing this data, maintenance teams can begin to identify patterns and trends that may not be immediately apparent, and make more informed decisions about how to allocate their resources. For more information on how AI can be used to optimize production, see our page on production optimization.

Sector constraints: grade variability and maintenance backlog

Maintenance teams in metals and mining face a number of unique constraints that can make it difficult to effectively manage maintenance activities. Grade variability, for example, can make it challenging to predict when equipment is likely to fail, while maintenance backlog can limit the ability of teams to perform routine maintenance activities. These constraints can have a significant impact on the effectiveness of maintenance teams, and can result in decreased productivity and increased costs.

By using AI to analyze their own data, maintenance teams can begin to better understand the impact of these constraints on their operations, and identify opportunities to mitigate their effects. For example, a maintenance manager may use AI to identify which assets are most critical to the operation, and prioritize maintenance activities accordingly. This can help to ensure that the most critical equipment is properly maintained, even in the face of significant maintenance backlog.

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

Improvement in maintenance activities can take many forms, but some key metrics include the planned versus reactive ratio, mean time between failures, and spares availability. By using AI to analyze their own data, maintenance teams can begin to optimize these metrics, and achieve significant improvements in productivity and efficiency. For example, a maintenance manager may use AI to identify opportunities to increase the planned versus reactive ratio, by scheduling more routine maintenance activities and reducing the need for reactive repairs.

Mean time between failures is another critical metric, as it can provide insights into the effectiveness of maintenance activities over time. By using AI to analyze data on equipment failures, maintenance teams can begin to identify patterns and trends that may indicate opportunities for improvement. Spares availability is also critical, as it can have a significant impact on the ability of maintenance teams to perform routine maintenance activities. By using AI to optimize spares inventory, maintenance teams can help ensure that they have the parts they need on hand, when they need them.

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

While AI for maintenance in metals and mining can be a powerful tool, it is not a panacea for all maintenance challenges. There are certain situations in which AI may not be the best choice, such as when data is limited or of poor quality. In these cases, more traditional maintenance strategies may be more effective. Additionally, AI is not a replacement for human judgment and expertise, but rather a tool to support and augment the decision-making process.

For teams that are considering using AI for maintenance, it is critical to carefully evaluate their needs and determine whether AI is the right choice. This may involve assessing the quality and availability of data, as well as the specific maintenance challenges that the team is facing. For more information on how to get started with AI for maintenance, or to speak with a member of our team, see our page on contacting us.