Dane Boers's guest appearances

Dane Boers

Rooted in Reliability: The Plant Performance Podcast

Maintenance Mavericks Podcast

Kevin Clark

Dan Miller

Tim Holmes

Bryan Johnson

Bobby Lee

Joe Anderson

Ron Moore

Jeff Ma

Scott Taylor

Brian Turner

George Williams

Joe Geng

Andrew Jordan

Michael Mazur

Nancy Regan

OwlTail

Cost vs Benefit of Inspection with Dane Boers Welcome Dane back to the podcast. We last talked about asset modelling and factors that influence those assets. Today we will talk about optimising strategies; how we determine cost vs benefit of strategies. But first, give a brief introduction of yourself and Modla. Modla is an asset knowledge and analytic platform. The vision is to capture subject matter expertise and knowledge around an asset and business processes. It then combines that with data for continuous improvement. We have worked in the utility industry; particularly water and electricity. In this episode we covered:  How do we determine the probability of finding a defect to establish inspection frequency? Are organisations looking to optimise inspections to ensure the benefits are worth the costs? Can you borrow a task in an FMEA from another organisation?   You were a reliability engineer, right? Yes. I started out in mining and transitioned to reliability and asset management. I was interested in the combination of engineering principles and the business side in reliability.   A lot of organisations are looking to optimise inspections to ensure the benefits are worth the costs. Is this something they should be concerned about? A recommended approach would be to start from ground zero; activities such as inspections and condition monitoring are inherently information gathering as they do not interfere with the asset's operation. The key is to determine the information you are looking for and the expected value it should add. This determination will be affected by the monitoring frequency, operation factors such as timing, and detection probability.  You need to prove that the task will add value before performing it. I do not recommend adding default tasks out of perhaps an OEM manual to the system without determining its value.   You can't just borrow a task in an FMEA from another organisation because the operational contexts vary.  The consequences of it failing are going to be different. However, it is also dependent on the business maturity. Sometimes an organisation lacks the capability to do a detailed analysis of expected outcomes and so copy pasting from another is an approach. Although, it is risky to blindly trust it.   How do we determine the probability of finding a defect to establish inspection frequency? Both inspecting at a high frequency and spacing out the intervals can yield the same results because failure modes happen when they happen. The probability of finding a defect is almost 100% for obvious defects such as corrosion. It gets more complex when technologies that distance the operator from the underlying defect need to be analysed. At such a point, you might need to ballpark everything and adjust the frequency based on your operational context to improve on it.   We look to understand how a failure occurred in between our inspections without detection. For instance, changes in vibration indicate a misalignment that wasn't picked during inspection or failure to carry out lubrication by the operator. How do we isolate such events to prevent them from skewing the data we collect on defect detection?  You have to use all the different tools together i.e combine SMEs and data analysis to confirm or deny hypotheses.  None in isolation.   Lessons from machine learning are clear that you cannot just let statisticians build models independently from SMEs. Also you cannot have SMEs isolated from staticians. They need to be interdependent.  Even in failure analysis, you need to confirm whether the data you are seeing makes sense. You need the technical side to understand what is happening in the real world.   But you need data sets to make a business case for the changes you need to make.  Management is using data-driven decision making but it is important to include the SMEs in the justification of the changes. The justification given by an SME is as equally important if not more valid than the data.. This is because the data has issues, assumptions, and non adjusted parameters.   When optimising maintenance tasks we should use liable analysis based on a single failure mode and not mixed failure mode. The condition monitoring techniques that you might use may not pick up those multiple failure modes and this complicates the justification. To justify an inspection, look at what would happen if it is not done. Compare that with the suggested inspection intervals, probability of detection of failure modes then link them to the failure modes. Definitely, interventions will be needed and these need to be planned in terms of overall cost. Compare all these activities to absence of the inspection. The difference yields the value added.   The cost is built up from labour rates, supplies, periods of technology use. To link these, should they have a list of failure modes? The more you understand the assets and its failure modes the more accurate your answer will be. But if you do not have the failure level information you can use the estimates. Let the SME estimate the costs and benefits of doing or not doing the inspections. The analysis does not need to go down to details; it can be basic as long as the logic is sound and documented then improve on it.   The key is to learn, do an RCA and find out why the inspections did not catch the failure modes or were they just random.  When critical assets fail and an RCA is done, there are failure detection probabilities that need to be picked during the exercise. There is ofcourse a tradeoff between overinspection and lack thereof. However, do not be quick to write off your logic because it did not detect multiple failures. Instead, work on improving the logic.   Eruditio Links:  Eruditio HP Reliability James Kovacevic's LinkedIn Reliability Report  Dane Boers Links:  Dane Boers Linkedin Modla Linkedin Modla – Collaboration and Asset Analytics Past episodes with Dane Boers  Download RSS iTunesStitcher Rooted In Reliability podcast is a proud member of Reliability.fm network. We encourage you to please rate and review this podcast on iTunes and Stitcher. It ensures the podcast stays relevant and is easy to find by like-minded professionals. It is only with your ratings and reviews that the Rooted In Reliability podcast can continue to grow. Thank you for providing the small but critical support for the Rooted In Reliability podcast! The post 312 – Cost vs Benefit of Inspection with Dane Boers appeared first on Accendo Reliability.

<h1>Cost vs Benefit of Inspection with Dane Boers</h1> Welcome Dane back to the podcast. We last talked about asset modelling and factors that influence those assets. Today we will talk about optimising strategies; how we determine cost vs benefit of strategies. But first, give a brief introduction of yourself and Modla. Modla is an asset knowledge and analytic platform. The vision is to capture subject matter expertise and knowledge around an asset and business processes. It then combines that with data for continuous improvement. We have worked in the utility industry; particularly water and electricity. In this episode we covered: <ul> <li>How do we determine the probability of finding a defect to establish inspection frequency?</li> <li>Are organisations looking to optimise inspections to ensure the benefits are worth the costs?</li> <li>Can you borrow a task in an FMEA from another organisation?</li> </ul> You were a reliability engineer, right? Yes. I started out in mining and transitioned to reliability and asset management. I was interested in the combination of engineering principles and the business side in reliability. A lot of organisations are looking to optimise inspections to ensure the benefits are worth the costs. Is this something they should be concerned about? A recommended approach would be to start from ground zero; activities such as inspections and condition monitoring are inherently information gathering as they do not interfere with the asset's operation. The key is to determine the information you are looking for and the expected value it should add. This determination will be affected by the monitoring frequency, operation factors such as timing, and detection probability.  You need to prove that the task will add value before performing it. I do not recommend adding default tasks out of perhaps an OEM manual to the system without determining its value. You can't just borrow a task in an FMEA from another organisation because the operational contexts vary. The consequences of it failing are going to be different. However, it is also dependent on the business maturity. Sometimes an organisation lacks the capability to do a detailed analysis of expected outcomes and so copy pasting from another is an approach. Although, it is risky to blindly trust it. How do we determine the probability of finding a defect to establish inspection frequency? Both inspecting at a high frequency and spacing out the intervals can yield the same results because failure modes happen when they happen. The probability of finding a defect is almost 100% for obvious defects such as corrosion. It gets more complex when technologies that distance the operator from the underlying defect need to be analysed. At such a point, you might need to ballpark everything and adjust the frequency based on your operational context to improve on it. We look to understand how a failure occurred in between our inspections without detection. For instance, changes in vibration indicate a misalignment that wasn't picked during inspection or failure to carry out lubrication by the operator. How do we isolate such events to prevent them from skewing the data we collect on defect detection? You have to use all the different tools together i.e combine SMEs and data analysis to confirm or deny hypotheses.  None in isolation. Lessons from machine learning are clear that you cannot just let statisticians build models independently from SMEs. Also you cannot have SMEs isolated from staticians. They need to be interdependent. Even in failure analysis, you need to confirm whether the data you are seeing makes sense. You need the technical side to understand what is happening in the real world. But you need data sets to make a business case for the changes you need to make. Management is using data-driven decision making but it is important to include the SMEs in the justification of the changes. The justification given by an SME is as equally important if not more valid than the data.. This is because the data has issues, assumptions, and non adjusted parameters. When optimising maintenance tasks we should use liable analysis based on a single failure mode and not mixed failure mode. The condition monitoring techniques that you might use may not pick up those multiple failure modes and this complicates the justification. To justify an inspection, look at what would happen if it is not done. Compare that with the suggested inspection intervals, probability of detection of failure modes then link them to the failure modes. Definitely, interventions will be needed and these need to be planned in terms of overall cost. Compare all these activities to absence of the inspection. The difference yields the value added. The cost is built up from labour rates, supplies, periods of technology use. To link these, should they have a list of failure modes? The more you understand the assets and its failure modes the more accurate your answer will be. But if you do not have the failure level information you can use the estimates. Let the SME estimate the costs and benefits of doing or not doing the inspections. The analysis does not need to go down to details; it can be basic as long as the logic is sound and documented then improve on it. The key is to learn, do an RCA and find out why the inspections did not catch the failure modes or were they just random. When critical assets fail and an RCA is done, there are failure detection probabilities that need to be picked during the exercise. There is ofcourse a tradeoff between overinspection and lack thereof. However, do not be quick to write off your logic because it did not detect multiple failures. Instead, work on improving the logic. Eruditio Links: <ul> <li><a href="http://eruditio.com/" rel="noopener noreferrer nofollow" target="_blank">Eruditio</a></li> <li><a href="https://hpreliability.com/" rel="noopener noreferrer nofollow" target="_blank">HP Reliability</a></li> <li><a href="https://www.linkedin.com/in/jameskovacevic" rel="noopener noreferrer nofollow" target="_blank">James Kovacevic's LinkedIn</a></li> <li><a href="https://www.youtube.com/channel/UC88Nfs79n-wN6Io86SQ9B0g" rel="noopener noreferrer nofollow" target="_blank">Reliability Report</a></li> </ul> Dane Boers Links: <ul> <li><a href="https://www.linkedin.com/in/daneboers/" rel="noopener noreferrer nofollow" target="_blank">Dane Boers Linkedin</a></li> <li><a href="https://www.linkedin.com/company/modla-co/" rel="noopener noreferrer nofollow" target="_blank">Modla Linkedin</a></li> <li><a href="https://www.modla.co/" rel="noopener noreferrer nofollow" target="_blank">Modla – Collaboration and Asset Analytics</a></li> <li><a href="https://accendoreliability.com/search-results/?q=Dane%20Boers" rel="noopener noreferrer nofollow" target="_blank">Past episodes with Dane Boers</a></li> </ul> <a href="http://episodes.reliability.fm/rir/rir-s01-e312-cost-vs-benefit-of-inspection-with-dane-boers.mp3" rel="noopener noreferrer nofollow" target="_blank">Download</a> <a href="https://accendoreliability.com/series/rir/feed/" rel="noopener noreferrer nofollow" target="_blank">RSS</a> <a href="https://itunes.apple.com/ca/podcast/rooted-in-reliability/id1122844720" rel="noopener noreferrer nofollow" target="_blank">iTunes</a><a href="http://www.stitcher.com/s?fid=92320&amp;refid=stpr" rel="noopener noreferrer nofollow" target="_blank">Stitcher</a> Rooted In Reliability podcast is a proud member of <a href="https://accendoreliability.com/reliability-fm/" rel="noopener noreferrer nofollow" target="_blank">Reliability.fm</a> network. We encourage you to please rate and review this podcast on <a href="https://itunes.apple.com/ca/podcast/rooted-in-reliability/id1122844720" rel="noopener noreferrer nofollow" target="_blank">iTunes </a>and <a href="http://www.stitcher.com/s?fid=92320&amp;refid=stpr" rel="noopener noreferrer nofollow" target="_blank">Stitcher</a>. It ensures the podcast stays relevant and is easy to find by like-minded professionals. It is only with your ratings and reviews that the Rooted In Reliability podcast can continue to grow. Thank you for providing the small but critical support for the Rooted In Reliability podcast! The post <a href="https://accendoreliability.com/podcast/the-reliability-fm-network/312-cost-vs-benefit-of-inspection-with-dane-boers/" rel="nofollow" target="_blank">312 – Cost vs Benefit of Inspection with Dane Boers</a> appeared first on <a href="https://accendoreliability.com" rel="nofollow" target="_blank">Accendo Reliability</a>.

312 - Cost vs Benefit of Inspection with Dane Boers

Asset Modeling with Dane Boers Welcome Dane Boers who is the founder of Modeler. Our topic will be asset modelling.  More about Dane:  Dane started his career in mining then moved to consultancy before starting Modeler. The company was started to make reliability more efficient and automated because of the repetitive work he had observed. In this episode we covered:  What is asset modeling? What is an asset class? Does asset modeling take into account the failure modes in the operational context; what's impactful and what's not?     What is asset modeling? It is the digitization of asset knowledge. The idea is to develop a framework that combines tools such as work plans and working instructions into a structure that can be used to answer business-related questions. The goal is to have one centralized store of knowledge about an asset class. For instance, everything about pumps are fed into one structure i.e. A pumps failure data, failure mode details, decision making logics e.tc gets captured in one place. The information assists in answering queries related to budgeting and optimization of maintenance strategy etc.   What is an asset class?  Refers to a group of assets that share a lot of information that can be used to put them together.   Does asset modeling take into account the failure modes in the operational context; what's impactful and what's not? Yes. It takes into account the failure mode and all the factors that can change the probability of that failure mode happening. Such factors might be environmental conditions and operational parameters. All these are inputs in the model. The model then creates relationships between these factors and failure modes to provide links that can drive decisions.   So it gives an insight about what could happen and what to do about it? Yes. It is all about statistical probability that updates predictions regarding asset failures with reasons for the same. However, there is still an element of variance and unknown.   How can companies leverage this asset model to develop effective strategy? To understand how to leverage it requires an understanding of how the model works. It has 3 components;  Asset model itself that contains the asset knowledge data and the logic. The use of the model to perform analysis or calculations to answer business queries Turning the answer into a digestible format i.e integrations. SAP loads, documents, dashboards e.t.c.  If it is about RCM, of importance is that all RCM related information about an asset is logically fed into the system if a query is about RCM optimization. The information could be inspections, overhauls, and subtasks to be performed. The query then could be: which of the tasks gives the best cost-benefit or life cycle cost. The model then analyses the information and gives a calculated, best alternative.   What type of data do we need?  It starts from a specific asset e.g. a pump; inputs can be the type of pump. Perhaps a centrifugal pump and the fluid that flows through the pump. Then move to the next type of pump and build a model based on the same inputs. The inputs are then linked to turning on and off of certain components and changing failure distribution. The model improves and grows with varying operational contexts. The basic data that you need; date of installation, equipment age, type of pump, types of fluid, impeller size etc. The model increases the need to have more data because they are connected to asset performance.   What other outputs are there apart from effective strategy? It depends on the type of queries you have for the model. There are varying uses ranging from budgeting, equipment selection depending on operational context, intervention tradeoffs depending on the information fed in the model.   Can we update the inputs after perhaps selecting an equipment to capture the operational context? Yes. In fact, RCA outcomes, mitigating tasks, and problem trees can be updated into the model. However, single case failures may not skew the model as much. Longer period of repetitive failures is needed for the model to produce actionable predictions. Optimization of the model is to link back and find out what the new input means to the failure mode.   Would having a good asset hierarchy help build the model? We tend to take a subject matter expert (SME) approach when starting out. This is because of the expert knowledge of what is important and what isn't. However, certain important issues may not be quantified or recorded in usable data form but affect failure mode. In that case, we adjust failure rates using the SME approach. It is preferred to start modeling at the asset level rather than system level.   Do you ever see clients experiment with different assets concurrently to see which one performs better? Yes. Especially in equipment selection. Say; selection of electric pole materials while considering geographical, climate and environmental factors.   One thing that makes the biggest difference:  Apart from asset knowledge, linking factors to failure modes, it is essential to understand that the data in our procession is imperfect. You need to use the best assumptions in such gaps until you find better data.   Key takeaway The industry is moving towards data-based, prescriptive recommendations. We need to structure the data to suit this.   Eruditio Links:  Eruditio HP Reliability James Kovacevic's LinkedIn Reliability Report  Dane Boers Links:  Modla.co Dane Boers Linkedin Modla Linkedin Book: RCM2 by John Moubray Book: The Book of Why: The New Science of Cause and Effect by Judea Pearl &amp; Dana Mackenzie Reliawiki  Download RSS iTunesStitcher Rooted In Reliability podcast is a proud member of Reliability.fm network. We encourage you to please rate and review this podcast on iTunes and Stitcher. It ensures the podcast stays relevant and is easy to find by like-minded professionals. It is only with your ratings and reviews that the Rooted In Reliability podcast can continue to grow. Thank you for providing the small but critical support for the Rooted In Reliability podcast! The post 266 – Asset Modeling with Dane Boers appeared first on Accendo Reliability.

<h1>Asset Modeling with Dane Boers</h1> Welcome Dane Boers who is the founder of Modeler. Our topic will be asset modelling. More about Dane: Dane started his career in mining then moved to consultancy before starting Modeler. The company was started to make reliability more efficient and automated because of the repetitive work he had observed. In this episode we covered: <ul> <li>What is asset modeling?</li> <li>What is an asset class?</li> <li>Does asset modeling take into account the failure modes in the operational context; what's impactful and what's not?</li> </ul> What is asset modeling? It is the digitization of asset knowledge. The idea is to develop a framework that combines tools such as work plans and working instructions into a structure that can be used to answer business-related questions. The goal is to have one centralized store of knowledge about an asset class. For instance, everything about pumps are fed into one structure i.e. A pumps failure data, failure mode details, decision making logics <a href="http://e.tc" rel="nofollow" target="_blank">e.tc</a> gets captured in one place. The information assists in answering queries related to budgeting and optimization of maintenance strategy etc. What is an asset class? Refers to a group of assets that share a lot of information that can be used to put them together. Does asset modeling take into account the failure modes in the operational context; what's impactful and what's not? Yes. It takes into account the failure mode and all the factors that can change the probability of that failure mode happening. Such factors might be environmental conditions and operational parameters. All these are inputs in the model. The model then creates relationships between these factors and failure modes to provide links that can drive decisions. So it gives an insight about what could happen and what to do about it? Yes. It is all about statistical probability that updates predictions regarding asset failures with reasons for the same. However, there is still an element of variance and unknown. How can companies leverage this asset model to develop effective strategy? To understand how to leverage it requires an understanding of how the model works. It has 3 components; <ol> <li>Asset model itself that contains the asset knowledge data and the logic.</li> <li>The use of the model to perform analysis or calculations to answer business queries</li> <li>Turning the answer into a digestible format i.e integrations. SAP loads, documents, dashboards e.t.c.</li> </ol> If it is about RCM, of importance is that all RCM related information about an asset is logically fed into the system if a query is about RCM optimization. The information could be inspections, overhauls, and subtasks to be performed. The query then could be: which of the tasks gives the best cost-benefit or life cycle cost. The model then analyses the information and gives a calculated, best alternative. What type of data do we need? It starts from a specific asset e.g. a pump; inputs can be the type of pump. Perhaps a centrifugal pump and the fluid that flows through the pump. Then move to the next type of pump and build a model based on the same inputs. The inputs are then linked to turning on and off of certain components and changing failure distribution. The model improves and grows with varying operational contexts. The basic data that you need; date of installation, equipment age, type of pump, types of fluid, impeller size etc. The model increases the need to have more data because they are connected to asset performance. What other outputs are there apart from effective strategy? It depends on the type of queries you have for the model. There are varying uses ranging from budgeting, equipment selection depending on operational context, intervention tradeoffs depending on the information fed in the model. Can we update the inputs after perhaps selecting an equipment to capture the operational context? Yes. In fact, RCA outcomes, mitigating tasks, and problem trees can be updated into the model. However, single case failures may not skew the model as much. Longer period of repetitive failures is needed for the model to produce actionable predictions. Optimization of the model is to link back and find out what the new input means to the failure mode. Would having a good asset hierarchy help build the model? We tend to take a subject matter expert (SME) approach when starting out. This is because of the expert knowledge of what is important and what isn't. However, certain important issues may not be quantified or recorded in usable data form but affect failure mode. In that case, we adjust failure rates using the SME approach. It is preferred to start modeling at the asset level rather than system level. Do you ever see clients experiment with different assets concurrently to see which one performs better? Yes. Especially in equipment selection. Say; selection of electric pole materials while considering geographical, climate and environmental factors. One thing that makes the biggest difference: Apart from asset knowledge, linking factors to failure modes, it is essential to understand that the data in our procession is imperfect. You need to use the best assumptions in such gaps until you find better data. Key takeaway The industry is moving towards data-based, prescriptive recommendations. We need to structure the data to suit this. Eruditio Links: <ul> <li><a href="http://eruditio.com/" rel="noopener noreferrer nofollow" target="_blank">Eruditio</a></li> <li><a href="https://hpreliability.com/" rel="noopener noreferrer nofollow" target="_blank">HP Reliability</a></li> <li><a href="https://www.linkedin.com/in/jameskovacevic" rel="noopener noreferrer nofollow" target="_blank">James Kovacevic's LinkedIn</a></li> <li><a href="https://www.youtube.com/channel/UC88Nfs79n-wN6Io86SQ9B0g" rel="noopener noreferrer nofollow" target="_blank">Reliability Report</a></li> </ul> Dane Boers Links: <ul> <li><a href="http://modla.co/" rel="noopener noreferrer nofollow" target="_blank">Modla.co</a></li> <li><a href="https://www.linkedin.com/in/daneboers/?originalSubdomain=au" rel="noopener noreferrer nofollow" target="_blank">Dane Boers Linkedin</a></li> <li><a href="https://www.linkedin.com/company/modla-co/?originalSubdomain=ph" rel="noopener noreferrer nofollow" target="_blank">Modla Linkedin</a></li> <li><a href="https://amzn.to/3mNGZRk" rel="noopener noreferrer nofollow" target="_blank">Book: RCM2 by John Moubray</a></li> <li><a href="https://amzn.to/3adAMsX" rel="noopener noreferrer nofollow" target="_blank">Book: The Book of Why: The New Science of Cause and Effect by Judea Pearl &amp; Dana Mackenzie</a></li> <li><a href="http://reliawiki.org/index.php/Main_Page" rel="noopener noreferrer nofollow" target="_blank">Reliawiki</a></li> </ul> <a href="http://episodes.reliability.fm/rir/rir-s01-e266-asset-modeling-with-dane-boers.mp3" rel="noopener noreferrer nofollow" target="_blank">Download</a> <a href="https://accendoreliability.com/series/rir/feed/" rel="noopener noreferrer nofollow" target="_blank">RSS</a> <a href="https://itunes.apple.com/ca/podcast/rooted-in-reliability/id1122844720" rel="noopener noreferrer nofollow" target="_blank">iTunes</a><a href="http://www.stitcher.com/s?fid=92320&amp;refid=stpr" rel="noopener noreferrer nofollow" target="_blank">Stitcher</a> Rooted In Reliability podcast is a proud member of <a href="https://accendoreliability.com/reliability-fm/" rel="noopener noreferrer nofollow" target="_blank">Reliability.fm</a> network. We encourage you to please rate and review this podcast on <a href="https://itunes.apple.com/ca/podcast/rooted-in-reliability/id1122844720" rel="noopener noreferrer nofollow" target="_blank">iTunes </a>and <a href="http://www.stitcher.com/s?fid=92320&amp;refid=stpr" rel="noopener noreferrer nofollow" target="_blank">Stitcher</a>. It ensures the podcast stays relevant and is easy to find by like-minded professionals. It is only with your ratings and reviews that the Rooted In Reliability podcast can continue to grow. Thank you for providing the small but critical support for the Rooted In Reliability podcast! The post <a href="https://accendoreliability.com/podcast/the-reliability-fm-network/266-asset-modeling-dane-boers/" rel="nofollow" target="_blank">266 – Asset Modeling with Dane Boers</a> appeared first on <a href="https://accendoreliability.com" rel="nofollow" target="_blank">Accendo Reliability</a>.

Asset Modeling with Dane Boers