Frank Emery's guest appearances

Frank Emery

Rooted in Reliability: The Plant Performance 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

Stuart Fergusson

255 – Forecasting the Future with Frank Emery &amp; Stuart Fergusson Welcome Stewart and Frank to the podcast. Briefly tell us about yourselves Stewart has a background in Big Data and Applied Mathematics and currently specializes in AI at Fiix. Frank on the other hand has a background in manufacturing of consumer products. In this episode, we covered:    Our topic of discussion today is around AI and its functionality in forecasting the future and how it helps maintenance professions. Tell us about AI. AI is basically defined as an algorithm or a series of algorithms that learn and configure themselves based on past activities to predict future behavior patterns.   Why should maintenance professionals care about AI? The unconventional but appropriate response is that they shouldn't. AI should be thought of as just another tool which must be evaluated based purely on its functionality. Its adoption should be assessed while looking at the contribution it makes towards the targeted efficiencies.   Will AI make people lose their jobs? Negative. If anything, AI is meant to aid professionals in doing their jobs in a better way than they would without it. It assists them in identifying problems much faster and eliminating extended downtime.   What makes a well-rounded AI?  A well-rounded AI is defined by the goals that your organization intends to achieve. It is therefore essential to determine what these goals are before obtaining AI. The tool exists to support the maintenance programs or identify points of asset failure.   Where does all the data used in AI come from? The data is often obtained from the CMMS already installed. Once you have defined the aspects of operations you want to monitor, the data will be obtained from the tools and systems that you already have.   The data obtained from the tools and systems in place is often inaccurate. How do you deal with this? The accuracy of data can be suspect but still be useful. If the data is fed into the AI it can show what is being measured and assist in rectifications. You can improve the quality of the data by perhaps populating more fields to obtain more granulated information from the AI. However, the key is to determine your goal, the tools being used and the accuracy needed.   How much data do we need to use AI models? The focus should be ensuring that even if the data is a lot, it ought to be the right set of data.  For instance, if you have a data set about a PLC's performance and another on task scheduling all mixed together, then it will unnecessarily bog down the system without giving data that the AI can optimize.   How do we select what to monitor? AI looks for anomalies and feeds from sensors to statistically determine which one has too much noise. Clients can also provide contacts for OEMs whom are contacted to help in identifying the sensors to select in an asset.   What are the benefits of AI at the shop floor? AI has significantly contributed in identifying problems much faster so that maintenance engineers can fix them without extended downtime. The tool also exposes points of highest risk and work order anomalies in perhaps planning the PMs.   Where does one get an AI? There are a lot of AI software out there. We highly recommend that you do not build one for you own use because it becomes too complex. However, use your problems in searching for a tool that can specifically address them.   What are the successes in AI application? AI has significantly assisted in flagging work orders. It helped a company in identifying gaps in the work order that ended up saving the entire asset and preventing downtime.   Where can those seeking AI get started? Chances are that the CMMS you have in place already have an AI option. However, in looking for another one, you have to be specific on what this tool should improve. The amount of information that it gives should also be easily consumed- AI should not create unnecessary work. It should make the process much easier.   What makes the biggest difference? The biggest difference is in specifically defining the desired outcomes. This will help in seeking proof of concept and measure results.   Key Takeaway Do not complicate the acquisition of an AI. It is intended to make things simpler than they already are. If it is too complicated, then it is the wrong tool for your process.   Eruditio Links:  Eruditio HP Reliability James Kovacevic's LinkedIn Reliability Report  Frank Emery &amp; Stuart Fergusson Links:  Using WO Data in a Well Rounded PdM Strategy with Frank Emery Fiixsoftware.com Frank Emery Linkedin Stuart Fergusson Linkedin  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 255 – Forecasting the Future with Frank Emery &amp; Stuart Fergusson appeared first on Accendo Reliability.

<h1>255 – Forecasting the Future with Frank Emery &amp; Stuart Fergusson</h1> Welcome Stewart and Frank to the podcast. Briefly tell us about yourselves Stewart has a background in Big Data and Applied Mathematics and currently specializes in AI at Fiix. Frank on the other hand has a background in manufacturing of consumer products. In this episode, we covered: Our topic of discussion today is around AI and its functionality in forecasting the future and how it helps maintenance professions. Tell us about AI. AI is basically defined as an algorithm or a series of algorithms that learn and configure themselves based on past activities to predict future behavior patterns. Why should maintenance professionals care about AI? The unconventional but appropriate response is that they shouldn't. AI should be thought of as just another tool which must be evaluated based purely on its functionality. Its adoption should be assessed while looking at the contribution it makes towards the targeted efficiencies. Will AI make people lose their jobs? Negative. If anything, AI is meant to aid professionals in doing their jobs in a better way than they would without it. It assists them in identifying problems much faster and eliminating extended downtime. What makes a well-rounded AI? A well-rounded AI is defined by the goals that your organization intends to achieve. It is therefore essential to determine what these goals are before obtaining AI. The tool exists to support the maintenance programs or identify points of asset failure. Where does all the data used in AI come from? The data is often obtained from the CMMS already installed. Once you have defined the aspects of operations you want to monitor, the data will be obtained from the tools and systems that you already have. The data obtained from the tools and systems in place is often inaccurate. How do you deal with this? The accuracy of data can be suspect but still be useful. If the data is fed into the AI it can show what is being measured and assist in rectifications. You can improve the quality of the data by perhaps populating more fields to obtain more granulated information from the AI. However, the key is to determine your goal, the tools being used and the accuracy needed. How much data do we need to use AI models? The focus should be ensuring that even if the data is a lot, it ought to be the right set of data.  For instance, if you have a data set about a PLC's performance and another on task scheduling all mixed together, then it will unnecessarily bog down the system without giving data that the AI can optimize. How do we select what to monitor? AI looks for anomalies and feeds from sensors to statistically determine which one has too much noise. Clients can also provide contacts for OEMs whom are contacted to help in identifying the sensors to select in an asset. What are the benefits of AI at the shop floor? AI has significantly contributed in identifying problems much faster so that maintenance engineers can fix them without extended downtime. The tool also exposes points of highest risk and work order anomalies in perhaps planning the PMs. Where does one get an AI? There are a lot of AI software out there. We highly recommend that you do not build one for you own use because it becomes too complex. However, use your problems in searching for a tool that can specifically address them. What are the successes in AI application? AI has significantly assisted in flagging work orders. It helped a company in identifying gaps in the work order that ended up saving the entire asset and preventing downtime. Where can those seeking AI get started? Chances are that the CMMS you have in place already have an AI option. However, in looking for another one, you have to be specific on what this tool should improve. The amount of information that it gives should also be easily consumed- AI should not create unnecessary work. It should make the process much easier. What makes the biggest difference? The biggest difference is in specifically defining the desired outcomes. This will help in seeking proof of concept and measure results. Key Takeaway Do not complicate the acquisition of an AI. It is intended to make things simpler than they already are. If it is too complicated, then it is the wrong tool for your process. 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> Frank Emery &amp; Stuart Fergusson Links: <ul> <li><a href="https://accendoreliability.com/podcast/rir/253-using-wo-data-well-rounded-pdm-strategy-frank-emery/" rel="noopener noreferrer nofollow" target="_blank">Using WO Data in a Well Rounded PdM Strategy with Frank Emery</a></li> <li><a href="http://fiixsoftware.com/" rel="noreferrer nofollow noopener" target="_blank">Fiixsoftware.com</a></li> <li><a href="https://ca.linkedin.com/in/francis-emery-b835523b" rel="nofollow" target="_blank">Frank Emery Linkedin</a></li> <li><a href="https://www.linkedin.com/in/stuart-fergusson-711ba8165/" rel="noopener noreferrer nofollow" target="_blank">Stuart Fergusson Linkedin</a></li> </ul> <a href="http://episodes.reliability.fm/rir/rir-s01-e255-forecasting-the-future-with-frank-emery-and-stuart-ferguson.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/255-forecasting-the-future-with-frank-emery-stuart-fergusson/" rel="nofollow" target="_blank">255 – Forecasting the Future with Frank Emery &amp; Stuart Fergusson</a> appeared first on <a href="https://accendoreliability.com" rel="nofollow" target="_blank">Accendo Reliability</a>.

255 - Forecasting the Future with Frank Emery & Stuart Fergusson

Using WO Data in a Well Rounded PdM Strategy with Frank Emery We’re excited to have Frank Emery, the product manager of AI at Fiix Software, with us to talk about how to make the Work Order data work. Fiix Software is a CMMS that was initially called Maintenance Assistant. He’s been in the software industry for at least ten years, figuring out how to use data. Frank’s work at Fiix is to try and make maintenance technicians’ lives a little bit easier. Key highlights from this episode are:  What is Work Order data? What type of data are you looking for in a WO? How to develop a well-rounded PM program using WO data  .. and so much more!  What is Work Order data? All the data you type into a maintenance system or write on paper helps you to find out:  What a Work Order is How you’re going to do it When it’s expected to be done  These include the tasks associated with the work you’re doing, who’s assigned, when you expect to finish, or how long it may take. All that data makes up a WO. Even the name of a WO or maintenance request is data you can pull and learn from.   How important is having WO data? It’s critical. If you’re doing things without having that data tell you what you did, how it went, and how it’s going to go next time, then you’ll get lost. You won’t know what’s working and what isn’t. You won’t find issues in your processes or figure out where teams are doing great things that you can cross-pollinate.   What type of data are you looking for in a WO? The most important things when looking at a WO are:  The problem with the asset What to do to fix it How long did it take?  Those are the core fields to start looking at. Everything else helps describe those three. For instance, looking at a task list is important because it says what you did to fix the problem. With those tasks, you’ll have other things like parts, durations, or signees, which help. But without those tasks, you can’t do anything else from there.   How to ensure data meets the expected needs There are two ways to look at this. To ensure the data’s high quality, you must make it easy. If it’s hard to fill the data in, or it takes time, that barrier comes up, and that person won’t do it to do it. So, ensure it’s easy to get data in, thereby ensuring it fits into someone’s routine. Another aspect is that you need to have the tooling to figure out when there’s bad data in there. If you know your technicians aren’t filling in data correctly because it’s too difficult, then you need to have a mechanism in place to fix that so you can still make the right business decisions. You can make data collection easy by:  Making all the data that’s there contextual Making auto-complete features and spell checks available Being able to fill in the details on mobile devices  These are all about muscle memory. If you make the data input a part of someone’s daily routine, that will also make it a lot easier to do.   How does the data help create a PM strategy? A good PM strategy is about:  Culture Data Technology Process  Data comes in to make sure that the culture and process are working and being adhered to. It’s there to help you figure out where the gaps are to improve things. With a good PM program, data is what lets you know what’s working so that you can go back and make adjustments. Data lets you know you have the right program because you’re getting the results you need   How to develop a well-rounded PM program using WO data People are using this data to figure out where processes are failing and not failing. It also tells you which assets need maintenance and which ones don’t. It helps you understand your environment and what’s going on with your assets and teams so you can adjust. A good rule of thumb is that you should be seeing some corrective maintenance coming in there. You don’t want to be doing PMing only since that’s overinvesting. Another pointer is how often you’re doing your PMs. If you’re never getting around to them, that’s a sign of process problems. Finally, make sure that you’re doing the full body of work. You have an issue if the PMs are coming, but you’re not finishing the whole thing. You can also use a 1:1 ratio for traditional PM activities. So, if you spend an hour on a traditional PM inspection, you should be creating one hour of follow-up work for corrective. It’s a lot higher for PDM because those are a lot less intrusive and less time-consuming.   Trends in the WO data One of the trends is around data quality. Keep the data consistent and have an idea of what the ideal work order looks like. That will give you a good feel for whether or not the data collection processes in place will give you the data you need so you can trust the decisions coming out of it. How often are things like rework or parts not showing up happen? That all ties into whether or not you have more downtime than you need to be having.   What is data quality? When looking at data quality, are you filling in all the fields needed, and are you filling them correctly? Some people do a lot of pencil whipping so that the forms look completed, which means you’re collecting the wrong data.   How to improve WO data quality Ultimately, it comes down to the culture. You could have the best tools and processes, but if your team doesn’t value the data, you’re always going to have problems. So, ensure a feedback loop around why you need the correct data, and reinforce that culture and consistency to build that muscle memory.   What makes the biggest difference with WO data? The technology that lets you go through it makes a big difference. There’s a lot of data that can get generated. So, you have to go in knowing what the metrics you’re checking or what’s the tooling you’ll use to clean up and read through the data are.   In summary Working through and learning from that WO data is more impactful than any other new technology coming out these days. You’re sitting on a gold mine of information that can help your team get better. So, the sooner you start working with that and learning from it, the sooner you’ll start to see benefits.   Eruditio Links:  Eruditio HP Reliability James Kovacevic's LinkedIn Reliability Report  Frank Emery Links:  Frank Emery LinkedIn Fiix website Fiix Foresight: AI tools for maintenance The Drunkards Walk: How Randomness Rules Our Lives  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 253 – Using WO Data in a Well Rounded PdM Strategy with Frank Emery appeared first on Accendo Reliability.

<h1>Using WO Data in a Well Rounded PdM Strategy with Frank Emery</h1> We’re excited to have Frank Emery, the product manager of AI at Fiix Software, with us to talk about how to make the Work Order data work. Fiix Software is a CMMS that was initially called Maintenance Assistant. He’s been in the software industry for at least ten years, figuring out how to use data. Frank’s work at Fiix is to try and make maintenance technicians’ lives a little bit easier. Key highlights from this episode are: <ul> <li>What is Work Order data?</li> <li>What type of data are you looking for in a WO?</li> <li>How to develop a well-rounded PM program using WO data</li> </ul> .. and so much more! What is Work Order data? All the data you type into a maintenance system or write on paper helps you to find out: <ul> <li>What a Work Order is</li> <li>How you’re going to do it</li> <li>When it’s expected to be done</li> </ul> These include the tasks associated with the work you’re doing, who’s assigned, when you expect to finish, or how long it may take. All that data makes up a WO. Even the name of a WO or maintenance request is data you can pull and learn from. How important is having WO data? It’s critical. If you’re doing things without having that data tell you what you did, how it went, and how it’s going to go next time, then you’ll get lost. You won’t know what’s working and what isn’t. You won’t find issues in your processes or figure out where teams are doing great things that you can cross-pollinate. What type of data are you looking for in a WO? The most important things when looking at a WO are: <ul> <li>The problem with the asset</li> <li>What to do to fix it</li> <li>How long did it take?</li> </ul> Those are the core fields to start looking at. Everything else helps describe those three. For instance, looking at a task list is important because it says what you did to fix the problem. With those tasks, you’ll have other things like parts, durations, or signees, which help. But without those tasks, you can’t do anything else from there. How to ensure data meets the expected needs There are two ways to look at this. To ensure the data’s high quality, you must make it easy. If it’s hard to fill the data in, or it takes time, that barrier comes up, and that person won’t do it to do it. So, ensure it’s easy to get data in, thereby ensuring it fits into someone’s routine. Another aspect is that you need to have the tooling to figure out when there’s bad data in there. If you know your technicians aren’t filling in data correctly because it’s too difficult, then you need to have a mechanism in place to fix that so you can still make the right business decisions. You can make data collection easy by: <ul> <li>Making all the data that’s there contextual</li> <li>Making auto-complete features and spell checks available</li> <li>Being able to fill in the details on mobile devices</li> </ul> These are all about muscle memory. If you make the data input a part of someone’s daily routine, that will also make it a lot easier to do. How does the data help create a PM strategy? A good PM strategy is about: <ul> <li>Culture</li> <li>Data</li> <li>Technology</li> <li>Process</li> </ul> Data comes in to make sure that the culture and process are working and being adhered to. It’s there to help you figure out where the gaps are to improve things. With a good PM program, data is what lets you know what’s working so that you can go back and make adjustments. Data lets you know you have the right program because you’re getting the results you need How to develop a well-rounded PM program using WO data People are using this data to figure out where processes are failing and not failing. It also tells you which assets need maintenance and which ones don’t. It helps you understand your environment and what’s going on with your assets and teams so you can adjust. A good rule of thumb is that you should be seeing some corrective maintenance coming in there. You don’t want to be doing PMing only since that’s overinvesting. Another pointer is how often you’re doing your PMs. If you’re never getting around to them, that’s a sign of process problems. Finally, make sure that you’re doing the full body of work. You have an issue if the PMs are coming, but you’re not finishing the whole thing. You can also use a 1:1 ratio for traditional PM activities. So, if you spend an hour on a traditional PM inspection, you should be creating one hour of follow-up work for corrective. It’s a lot higher for PDM because those are a lot less intrusive and less time-consuming. Trends in the WO data One of the trends is around data quality. Keep the data consistent and have an idea of what the ideal work order looks like. That will give you a good feel for whether or not the data collection processes in place will give you the data you need so you can trust the decisions coming out of it. How often are things like rework or parts not showing up happen? That all ties into whether or not you have more downtime than you need to be having. What is data quality? When looking at data quality, are you filling in all the fields needed, and are you filling them correctly? Some people do a lot of pencil whipping so that the forms look completed, which means you’re collecting the wrong data. How to improve WO data quality Ultimately, it comes down to the culture. You could have the best tools and processes, but if your team doesn’t value the data, you’re always going to have problems. So, ensure a feedback loop around why you need the correct data, and reinforce that culture and consistency to build that muscle memory. What makes the biggest difference with WO data? The technology that lets you go through it makes a big difference. There’s a lot of data that can get generated. So, you have to go in knowing what the metrics you’re checking or what’s the tooling you’ll use to clean up and read through the data are. In summary Working through and learning from that WO data is more impactful than any other new technology coming out these days. You’re sitting on a gold mine of information that can help your team get better. So, the sooner you start working with that and learning from it, the sooner you’ll start to see benefits. 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> Frank Emery Links: <ul> <li><a href="https://ca.linkedin.com/in/francis-emery-b835523b" rel="noopener noreferrer nofollow" target="_blank">Frank Emery LinkedIn</a></li> <li><a href="https://www.fiixsoftware.com/" rel="noopener noreferrer nofollow" target="_blank">Fiix website</a></li> <li><a href="https://www.fiixsoftware.com/foresight/" rel="noopener noreferrer nofollow" target="_blank">Fiix Foresight: AI tools for maintenance</a></li> <li><a href="https://amzn.to/3gzlJvw" rel="noopener noreferrer nofollow" target="_blank">The Drunkards Walk: How Randomness Rules Our Lives</a></li> </ul> <a href="http://episodes.reliability.fm/rir/rir-s01-e253-using-wo-data-in-a-well-rounded-pdm-strategy-with-frank-emery.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/253-using-wo-data-well-rounded-pdm-strategy-frank-emery/" rel="nofollow" target="_blank">253 – Using WO Data in a Well Rounded PdM Strategy with Frank Emery</a> appeared first on <a href="https://accendoreliability.com" rel="nofollow" target="_blank">Accendo Reliability</a>.

253 - Using WO Data in a Well Rounded PdM Strategy with Frank Emery