• Written by Muhammed Abdulla NC, CEO

A maintenance manager sits in a meeting, staring at a spreadsheet that is supposed to show the return on investment for a new predictive maintenance system, but the numbers do not seem to add up, and the manager is not convinced that the system is actually making a difference. This is a common problem in maintenance, where the counterfactual - what would have happened without the intervention - is invisible, making it difficult to measure the impact of a new system or process.

Leading measures you can actually observe

One way to approach this problem is to focus on leading measures that can actually be observed, such as the number of planned maintenance tasks completed, or the amount of time spent on proactive maintenance activities. These measures can provide insight into whether the maintenance team is actually doing things differently as a result of the new system or process. By tracking these leading measures, the maintenance manager can get a sense of whether the new system is having an impact, even if the ultimate outcome metrics are not yet showing a clear change.

For example, a maintenance team might track the number of condition-based maintenance tasks completed, or the number of spare parts ordered in advance, as a way of measuring the effectiveness of a new predictive maintenance system. By focusing on these leading measures, the team can identify areas for improvement and make adjustments to the system or process as needed.

Why aggregate outcome metrics move for unrelated reasons

Aggregate outcome metrics, such as overall equipment effectiveness or maintenance cost as a percentage of asset value, can be influenced by a wide range of factors, many of which are unrelated to the maintenance team or the new system or process. For example, changes in production volumes or schedules can impact equipment usage and maintenance needs, while changes in staffing or training can impact the maintenance team's ability to perform tasks effectively. As a result, it can be difficult to determine whether changes in these metrics are actually due to the new system or process, or if they are the result of other factors.

This is why it is so important to focus on leading measures that can be directly observed and influenced by the maintenance team, rather than relying solely on aggregate outcome metrics. By doing so, the team can get a more accurate sense of whether the new system or process is actually making a difference, and make adjustments as needed to optimize its impact.

The maintenance data this depends on

The ability to measure the impact of a new maintenance system or process depends on having access to high-quality maintenance data, including work orders, asset history, spare parts inventory, and condition data where it exists. This data provides the foundation for tracking leading measures and understanding the impact of the new system or process on maintenance activities and outcomes. For example, a maintenance team might use production data to understand equipment usage patterns and identify opportunities for proactive maintenance, while also using asset history data to track the effectiveness of maintenance activities over time.

Condition data, such as vibration or temperature readings, can also be used to track the health of equipment and identify potential problems before they occur. By integrating this data with work order and spare parts inventory data, the maintenance team can get a complete picture of maintenance activities and outcomes, and make informed decisions about how to optimize the maintenance process.

What it changes about: reactive work crowding out planned work and the same asset recurring under different descriptions

One of the key challenges in maintenance is the tendency for reactive work to crowd out planned work, as maintenance teams are forced to respond to urgent breakdowns and repairs rather than focusing on proactive maintenance activities. This can lead to a cycle of reactive maintenance, where the same assets are repeatedly repaired or replaced without ever addressing the underlying causes of the problems. By using leading measures and maintenance data to track the impact of a new system or process, the maintenance team can identify areas where reactive work is crowding out planned work, and make adjustments to prioritize proactive maintenance activities.

Another challenge is the tendency for the same asset to recur under different descriptions, making it difficult to track maintenance activities and outcomes over time. For example, a pump might be described as "pump 1" in one work order, and "pump A" in another, making it difficult to track the maintenance history of the asset. By using standardized asset descriptions and tracking maintenance activities and outcomes over time, the maintenance team can get a more accurate picture of maintenance needs and optimize the maintenance process.

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

There are a number of metrics that can be used to measure the impact of a new maintenance system or process, including the planned versus reactive ratio, mean time between failures, and spares availability. The planned versus reactive ratio provides insight into whether the maintenance team is prioritizing proactive maintenance activities, while mean time between failures provides a measure of equipment reliability and maintenance effectiveness. Spares availability, meanwhile, provides a measure of the maintenance team's ability to respond quickly to breakdowns and repairs.

By tracking these metrics and using them to inform maintenance decisions, the maintenance team can optimize the maintenance process and improve overall equipment effectiveness. For example, a maintenance team might use the planned versus reactive ratio to identify areas where reactive work is crowding out planned work, and make adjustments to prioritize proactive maintenance activities. To get more information about how to implement these metrics, you can contact us for more details.

What this does not do

This approach to measuring the impact of a new maintenance system or process does not provide a complete picture of the return on investment, as it focuses primarily on maintenance activities and outcomes rather than broader business metrics. Additionally, it does not account for external factors that may influence maintenance needs and outcomes, such as changes in production volumes or schedules. As a result, it is important to consider these limitations when using this approach to measure the impact of a new maintenance system or process.