All Member Open Forum

Hi Terry,

In your experience, how do you ensure that Preventive Maintenance Optimization (PMO) initiatives are truly data-driven and not just a rebranding of time-based maintenance?

Specifically, which KPIs or decision criteria do you use to validate that PM tasks, frequencies, and material usage are effectively aligned with actual failure data?

Greetings Luis,

From an RE perspective developing a maintenance strategy is crucial for asset reliability. Many companies over look the value. My first check is to look at how reactive vs. proactive. I also compare existing KPI's with my own investigation. This identifies the existing conditions.
Your comment on data driven requires investigation of maintenance records, how accurate and consistent. Are all assets listed in detail? With complete bill of materials. Is there a CMMS? What is the PM completion percentage? The CMMS is like a filing cabinet; if it's all in order or all not in order determines the amount of work required. Good record keeping is crucial for accurate PMO evaluation. Hopefully there are work order types: breakdown/reactive, corrective, and preventive/predictive. And captured accurate labor hours and materials used on the asset. To ensure a good PMO rebuild, all costs need identified and compared to the existing PM. So looking from a 50k foot level; good procedures and records are necessary and requires discipline. Of which why responded to the MTTR discussion as it is one factor in labor and material capturing. From this short explanation, do any thoughts arise? This is a topic, at least in my mind, from which at least 11 Reliability Engineering Processes can be examined to achieve reliability excellence. 

Terry

Hi Terry,

In your experience, how do you ensure that Preventive Maintenance Optimization (PMO) initiatives are truly data-driven and not just a rebranding of time-based maintenance?

Specifically, which KPIs or decision criteria do you use to validate that PM tasks, frequencies, and material usage are effectively aligned with actual failure data?

This is a great discussion, and I agree that PMO only delivers real value when it is anchored in how assets actually fail, not how often we assume they might.

One limitation I've seen, even in organizations with disciplined CMMS use and solid KPIs, is the quality of pre-failure observations. If early condition changes lack context or consistency, downstream analysis may be technically correct but operationally shallow.

When condition evidence is captured before functional failure, those points effectively map a failure timeline. That allows PM work to align with how degradation actually progresses, rather than relying primarily on calendar or usage assumptions. This is where PMO moves beyond interval tuning and becomes true failure-mode alignment.

From a reliability perspective, that also reinforces PMO as a learning process, not a one-time optimization exercise. Curious how others have improved the consistency and fidelity of early condition data, since that often seems to be the limiting factor in practice.

Greetings Luis,

From an RE perspective developing a maintenance strategy is crucial for asset reliability. Many companies over look the value. My first check is to look at how reactive vs. proactive. I also compare existing KPI's with my own investigation. This identifies the existing conditions.
Your comment on data driven requires investigation of maintenance records, how accurate and consistent. Are all assets listed in detail? With complete bill of materials. Is there a CMMS? What is the PM completion percentage? The CMMS is like a filing cabinet; if it's all in order or all not in order determines the amount of work required. Good record keeping is crucial for accurate PMO evaluation. Hopefully there are work order types: breakdown/reactive, corrective, and preventive/predictive. And captured accurate labor hours and materials used on the asset. To ensure a good PMO rebuild, all costs need identified and compared to the existing PM. So looking from a 50k foot level; good procedures and records are necessary and requires discipline. Of which why responded to the MTTR discussion as it is one factor in labor and material capturing. From this short explanation, do any thoughts arise? This is a topic, at least in my mind, from which at least 11 Reliability Engineering Processes can be examined to achieve reliability excellence. 

Terry

Hi Terry,

In your experience, how do you ensure that Preventive Maintenance Optimization (PMO) initiatives are truly data-driven and not just a rebranding of time-based maintenance?

Specifically, which KPIs or decision criteria do you use to validate that PM tasks, frequencies, and material usage are effectively aligned with actual failure data?

This is a really insightful perspective,@Nick Bell. As someone that isn't a practioner like most of the group, this is the million dollar question I hear most often.

In most cases, the production side of the house either isn't capturing data or has poor data practices/quality. Even in the instance where the reliability team has quality condition data, wouldn't it only paint half of the picture without contextual performance data from operations? Or am I thinking about it wrong? 

This is a great discussion, and I agree that PMO only delivers real value when it is anchored in how assets actually fail, not how often we assume they might.

One limitation I've seen, even in organizations with disciplined CMMS use and solid KPIs, is the quality of pre-failure observations. If early condition changes lack context or consistency, downstream analysis may be technically correct but operationally shallow.

When condition evidence is captured before functional failure, those points effectively map a failure timeline. That allows PM work to align with how degradation actually progresses, rather than relying primarily on calendar or usage assumptions. This is where PMO moves beyond interval tuning and becomes true failure-mode alignment.

From a reliability perspective, that also reinforces PMO as a learning process, not a one-time optimization exercise. Curious how others have improved the consistency and fidelity of early condition data, since that often seems to be the limiting factor in practice.

Greetings Luis,

From an RE perspective developing a maintenance strategy is crucial for asset reliability. Many companies over look the value. My first check is to look at how reactive vs. proactive. I also compare existing KPI's with my own investigation. This identifies the existing conditions.
Your comment on data driven requires investigation of maintenance records, how accurate and consistent. Are all assets listed in detail? With complete bill of materials. Is there a CMMS? What is the PM completion percentage? The CMMS is like a filing cabinet; if it's all in order or all not in order determines the amount of work required. Good record keeping is crucial for accurate PMO evaluation. Hopefully there are work order types: breakdown/reactive, corrective, and preventive/predictive. And captured accurate labor hours and materials used on the asset. To ensure a good PMO rebuild, all costs need identified and compared to the existing PM. So looking from a 50k foot level; good procedures and records are necessary and requires discipline. Of which why responded to the MTTR discussion as it is one factor in labor and material capturing. From this short explanation, do any thoughts arise? This is a topic, at least in my mind, from which at least 11 Reliability Engineering Processes can be examined to achieve reliability excellence. 

Terry

Hi Terry,

In your experience, how do you ensure that Preventive Maintenance Optimization (PMO) initiatives are truly data-driven and not just a rebranding of time-based maintenance?

Specifically, which KPIs or decision criteria do you use to validate that PM tasks, frequencies, and material usage are effectively aligned with actual failure data?

This is a really insightful perspective,@Nick Bell. As someone that isn't a practioner like most of the group, this is the million dollar question I hear most often.

In most cases, the production side of the house either isn't capturing data or has poor data practices/quality. Even in the instance where the reliability team has quality condition data, wouldn't it only paint half of the picture without contextual performance data from operations? Or am I thinking about it wrong? 

This is a great discussion, and I agree that PMO only delivers real value when it is anchored in how assets actually fail, not how often we assume they might.

One limitation I've seen, even in organizations with disciplined CMMS use and solid KPIs, is the quality of pre-failure observations. If early condition changes lack context or consistency, downstream analysis may be technically correct but operationally shallow.

When condition evidence is captured before functional failure, those points effectively map a failure timeline. That allows PM work to align with how degradation actually progresses, rather than relying primarily on calendar or usage assumptions. This is where PMO moves beyond interval tuning and becomes true failure-mode alignment.

From a reliability perspective, that also reinforces PMO as a learning process, not a one-time optimization exercise. Curious how others have improved the consistency and fidelity of early condition data, since that often seems to be the limiting factor in practice.

Greetings Luis,

From an RE perspective developing a maintenance strategy is crucial for asset reliability. Many companies over look the value. My first check is to look at how reactive vs. proactive. I also compare existing KPI's with my own investigation. This identifies the existing conditions.
Your comment on data driven requires investigation of maintenance records, how accurate and consistent. Are all assets listed in detail? With complete bill of materials. Is there a CMMS? What is the PM completion percentage? The CMMS is like a filing cabinet; if it's all in order or all not in order determines the amount of work required. Good record keeping is crucial for accurate PMO evaluation. Hopefully there are work order types: breakdown/reactive, corrective, and preventive/predictive. And captured accurate labor hours and materials used on the asset. To ensure a good PMO rebuild, all costs need identified and compared to the existing PM. So looking from a 50k foot level; good procedures and records are necessary and requires discipline. Of which why responded to the MTTR discussion as it is one factor in labor and material capturing. From this short explanation, do any thoughts arise? This is a topic, at least in my mind, from which at least 11 Reliability Engineering Processes can be examined to achieve reliability excellence. 

Terry

Hi Terry,

In your experience, how do you ensure that Preventive Maintenance Optimization (PMO) initiatives are truly data-driven and not just a rebranding of time-based maintenance?

Specifically, which KPIs or decision criteria do you use to validate that PM tasks, frequencies, and material usage are effectively aligned with actual failure data?