Condition based Maintenance—What the Industrial Sector Can Learn from Military and Defense

Discover more on how IoT for Service drives better business results

Condition based maintenance plus (CBM+) is a US Department of Defense initiative to improve the readiness of a diverse set of military assets—from air frames to ships. The Naval Sea Systems Command (NAVSEA) has said that when CBM+ is executed properly, it ensures: the right maintenance is performed at the right time, for the right cost, with improved operational availability. There are a lot of learnings the industrial sector can apply that the military and defense have already demonstrated. Improve operational efficiency, operational process improvement, and improve operational costs with a condition based maintenance system .

Transcript

0:00

Welcome to speaking of Service, the podcast that uncovers practical ways to grow service revenue, control costs, and improve customer satisfaction. If you're looking to innovate, gain a competitive edge, or just learn about the latest service trends, you've come to the right place today. Chris Wolf, VP of Strategy Partnership. Joins Jeffrey Miller, director of Digital Performance Management at Calypso. Jeffrey will speak to the topic of condition-based maintenance and how companies in the industrial sector can learn from military and defense when it comes to service operations.

Chris Wolff: 0:34

Welcome to speaking of service. It's nice to see you back again as a listener of this podcast. Intrinsically that technologies that enable data and analytics can make your services. More cost effective, create better customer experiences, all kinds of good stuff. But as a provider of service, how do you know if you're doing enough? How do you benchmark yourself against best practices? How do you, what kind of a framework do you use in order to measure your service delivery against the best? Way back in 2002, it's a coming of age story. The Department of Defense set out to understand just how effective they were in delivering. And availability of complex machines and harsh environments, as you can imagine they created a framework and a set of guiding principles on condition-based maintenance that has relevance to the private sector. I've asked an expert in that field, Jeff Miller from Calypso to join me here today to talk about how lay people and the civilian sector can apply some of these d o d Princip. So Jeff, thank you so much for being here. Welcome to speaking of Service. Tell our listers a little bit about yourself.

Jeffrey Miller: 1:46

Thanks very much for the opportunity to speak with you, Wolfie and with your audience. I'm delighted to be here. It's good to see you. My name's Jeff Miller. I'm a director in Calypso, which is the digital business unit of Rockwell Automation, one of the world's leading providers of industrial automation distributed control systems and motor controls and the like. I've been in this space for most of my career. I'm an engineer by training and have been at this intersection of supply chain logistics, field service, manufacturing, and importantly the technologies that drive them for the better part of 30 years. And this is a very interesting topic because it leverages so much of what's new in technologies today. So I'm looking forward to the chance to speak with you and and your listeners about it.

Chris Wolff: 2:28

Thanks Jeff. So take us back, to those early. Help me as a layperson understand what the DODs ambitions were in creating this framework in the first place, and how that's played out in the availability of the equipment that they maintain. The US

Jeffrey Miller: 2:42

Department of Defense, like every Ministry of Defense around the world is concerned with. The availability, the readiness and the reliability of its assets, whether it's transportation assets, weapons systems, or anything else. Around 2002, the under secretary for Material Logistics and acquisition penned a guidance piece that introduced the concept of condition-based maintenance, plus the plus being meant to focus. The use of data and analytics all towards the goal, measurable goal of improving the availability, readiness and reliability of the assets that were of concern. And the way they were gonna measure this was, besides those three, what were the costs in labor and maintenance and spare parts and logistics? So they had a rubric basically to. By which to evaluate these initiatives, it was all towards the goal of increasing those three things, availability, readiness, reliability.

Chris Wolff: 3:39

And how would they gather that information back in the days? Now we take it for granted that we've got monitoring technologies, but was it a pen and a clipboard? I imagine.

Jeffrey Miller: 3:49

That was the key. I think with the advent of the plus, this was in the early stages. If we think back to where we were 20, 22 years ago, 21 years ago with regard to connectivity and the ubiquitous availability of data, these things weren't there. And so this was groundbreaking at the time because it required new forms of data collection even new forms of communication, of data secure. And then even though, even with the analytics, these were immature at the time, we did have large computer programs and algorithms, but it wasn't as mainstream as it is today. So it really was groundbreaking at the time and imposed some challenges both on the government and on the industrial base, the defense industrial base that we were manufacturing these systems of concerns. So I think that's the important point. We were, we to do that today and come up with this idea today if we hadn't yet done. It'll be far easier to implement because of those things like connectivity that didn't exist back

Chris Wolff: 4:45

then. So now, fast forward maybe another 10 years or so, where did they land along this journey and help us understand the trajectory they've been on ever since?

Jeffrey Miller: 4:56

Sure. I think it's helpful to go back to some, or the base principles of maintenance. There are three kinds of maintenance, breakage, maintenance or run to failure. Preventative maintenance where you programmatically say every so often, every so month, many months or weeks, we'll perform a series of maintenance procedures and then condition-based maintenance, which in my view in definition, enables predictive maintenance or algorithmically based maintenance. So that's the maturity model. Arguably, we had mostly preventative maintenance, not a lot of condition based, or certainly predictive maintenance back in 2002, but in the ensu. As you say, a decade or so, the technologies began to evolve and the connectivity necessary to share data about what was happening to an asset. A generator, a forklift, an airplane engine, an air handler on a building or a weapon system. In the case of the Department of Defense, all of these grew. The difference with the D O D was really how far flung assets were all over the world. The complexities of se servicing those. So the need for analytics data was much more valuable perhaps in that setting than it was anywhere else. And that was what I think spurred the growth into CBM Plus and this idea, early idea of a digital. That sort of connected all these pieces together, suppliers, maintainers with the asset itself. That's what happened from 2002 to say 2012 or

Chris Wolff: 6:19

15. And now we know that data is everywhere. Data's the new oil or the new sunshine, depending on who you ask. How are the best companies making use of their data today for condition-based mainten?

Jeffrey Miller: 6:33

If you run out to the end of the maturity model two, condition-based maintenance, and it's associated predictive maintenance, which now invokes algorithms, either algorithms around models, so you have model predictive capabilities or just around processes themselves. The leading companies are not only looking at analytics around patterns of how the asset performs in certain conditions. But they're also beginning to connect. This is the digital thread portion, wolfie. They're beginning to connect what they learn from the data with other parts of the value chain, all of which contribute to maintaining it at a high level of availability or reliability. Readiness and availability to the Department of Defense and the defense industrial sector is analogous to service level agreements in the civilian. When you buy a piece of agricultural equipment, or you buy an air handler on a roof, if you do actually buy the air handler, there will probably be a service level agreement associated with it. Minimum time to repair time between breakage events. So now companies are using analytics as are the defense industrial companies, much more aggressively. One could argue this was really spearheaded by the defense industrial base, but now because of connectivity and the ubiquitous nature of data, most companies are doing that and the leading one. Build models around that to predict when the events are going to occur and to connect other pieces of the supply chain. I'll give you a very quick example. One of the air framers, and there are several around the world, so I'm, it can be any one of them had a product associated with basically aircraft health, everything but the engines and what they were trying to figure out how to do was to make this more. What they ended up doing was connecting the monitoring system to assets and building algorithms to predict failure, and they also on the backend, connected that system to the providers of maintenance services, M R O services parts, and they also connected it to dispatch so they would know where the aircraft was when it could be repaired. The point in that example, and there's much detail around it, was simply to say that doing predict condition based maintenance and predictive maintenance enabled them to keep the aircraft in the air much more efficiently without a corresponding increase in cost because they could connect these various components of the value chain together. That's where the leaders are and where much of the work. AI machine learning and supply chain

Chris Wolff: 8:53

connectivity. Jeff, if I'm a business person trying to improve the profitability of my business, this creates an awesome opportunity for me to provide aftermarket services, and yet I probably have a population of machines in the field that have spanned these generations that we've spoken about. How are the best companies sticking their toe in the water? Earlier in the maturity curve and yet also capitalizing on some of these new technologies. How do you manage that portfolio?

Jeffrey Miller: 9:22

The preventative maintenance piece is about parts and locations and speed to repair and programmatic maintenance, and that, that's never going to go away. That's really the foundation. I don't know, I dunno, any company who I speak with who operates assets on a rental failure basis that, that's just not economically viable for the machine or for the satisfaction of the user. So pretty much everybody has some form of preventative maintenance that the investments are in the moving to condition based and predictive, and they're usually. By the availability of data, are those assets properly instrumented? Were they instrumented by the oem? Lots of os are doing that now, and this is not new. Have been putting sensors for wider ranges of data collection, especially in more complicated assets. They'll put many more sensors and many more capabilities associated with data collect. On the device so that we can build these programs of condition-based maintenance reliability monitoring. And again, there's a convergence here of reliability into this as well, so that we can build better maintenance models and then sell them to the consumer, to the owner of that asset. So from the owner's side, it becomes a decision about who to buy from sometimes, or what third party to engage to help pull these data. That's where the economics come out for the owner operator lessor, if it's rental equipment. And for the o original equipment manufacturer, everybody sees value in keeping the asset operating. And depending on who you are in the value chain, it can be a compelling economic model.

Chris Wolff: 10:51

Calypso are leaders in this field, and when you take a look at the customers that you're serving, how many of them are wanting to be the general contractor? Providing a system of systems to collect data from all the disparate machines that they offer, how many of them lean more heavily on the data provided directly to them by the OEM maker?

Jeffrey Miller: 11:10

There's certainly an industry wolf. It varies by sector in the aviation industry, of course and PTC as well. In the middle of this as well, there's a very robust m r o industry in and of itself. And these are big beneficiaries of of. And of the algorithms that go with predicting failure modes and and setting strategy associated with that. And that's been around for many years. M r O and aviation as an industry is not new. It predates all of this, but it's effectiveness and the profitability while at the same time improving the availability of the asset, whether it's. In aircraft in flight entertainment system or gales or gear or engines or flight control systems. All of this has been improved for the owner operators, for example, while still managing to create a very profitable enterprise for the providers of M R O maintenance, repair and overhaul services. And spare parts. Whole networks of providers of spare parts were sprung up. This is true in other industries as well. We still see most clients, either the companies that operate large fleets of assets, manufacturers or field assets, or the OEMs themselves being the principal purchasers of these technologies. So it, I don't wanna call it a cottage industry by any stretch, but sector by sector you do. Sub industries like you do in aviation with M R O that are focused on this and big consumers of the data and the capabilities of CBM or CBM plus with the extended value chain. But I'd still suggest the predominant model is for the owners of assets to have these capabilities often provided by the OEMs. How do

Chris Wolff: 12:47

you benchmark how much. In your business you could be making based on the data that you're able to gather and how much money you're able to save, what's the profitability model, the benchmark of what the best companies are doing and what would an somebody exceeding that be looking at in terms of return on investment?

Jeffrey Miller: 13:05

It's interesting when you take the asset model and concerning all these maintenance procedures, it actually tr tracks pretty accurately as an analogy to what we do in the manufacturing setting, where we're always concerned with the effective capacity and we always want to create capacity within the existing assets we have. So move that over to fielded assets and condition-based maintenance or CBM plus. It really does come down to. How often is the machine not unavailable? And there may be commercial terms, like we said, service level agreements and so forth that provide financial penalties when something's not available. But most of the economic models revolve around the operating value of the asset. How much money per hour is it producing or generating for us? If it's a piece of rental equipment and the coral corresponding outages, if a piece of earth moving equipment in a mine is out of. What's the penalty on the mines operation or its revenue generation? And the other components, again, as usual, are parts and labor. All of those can be quantified and we see companies actually build these models. An hour of productivity is of production is worth x. So correspondingly an hour of downtime that was not planned. Also has a cost associated with it. We see these being used as the bases for justifying investments in the connectivity, the instrumentation, the connectivity, and some of the other systems associated with CBM Plus. And by the way, these are no different than what are used for computerized maintenance management systems. Most of the time when you buy those solutions, you're using the same kind of opportunity cost of a failed or a down. Or reduction in labor and material. It's really no different. The metrics are the same and they're very familiar. CBM Plus simply enables you to find more value because of this extension into the supply chain.

Chris Wolff: 14:51

It's interesting coming from the IT world where consumption economics have been with us for more than a decade, and now heavy equipment manufacturers and their service teams are having to face this idea of con providing consumptive models for the machines that they produce and. That brings people in from the product team, from marketing new types of skills, even HR, have to come together and have a common language about transforming their business and their culture in this way. How would you advise as a practitioner, a disparate team of entrepreneurial people in a business approach this discussion? What, how do you understand the basic ling. Build that framework. How do you get started?

Jeffrey Miller: 15:33

Again, I think it all springs from, we're all concerned with the availability of the asset. Then look at the stakeholders along the path, the owner of the asset and the incentive to connect it. The infrastructure providers with connectivity and acquisition of data edge technology providers to consolidate data and maybe do some analytical. So that data can be provided to maybe a material services provider or a labor provider. I think the key is to build that ecosystem of participants in the maintenance of availability, reliability of the asset. Using these tools, figure out what their respective interests and capabilities and needs are, and then try to respond to those. This was an element of CBM plus they first in back in the 2002 to 10 timeframe. They looked, they, the d o d looked at the participants along the continuum and asked, what do you need? They went to the Defense Logistics Agency and into all the commodity groups within the LA in Aviation and ground and Navy and so forth, and asked, what are the pieces of data that you need in order to better perform your function as you measure it, parts avail. In the logistics realm, how many times do you have to move a spare part around before it actually gets consumed, which is a measure of your planning accuracy. So they collected all this information and then sought to answer it with here's what we can get from the asset itself or from the operating environment. That hasn't changed 20 years on. So an entrepreneur who's trying to build a. Or a system of systems with various participants, commercial participants around this shared goal of maintaining the reliability, the availability and reliability of an asset, understand what they each need and what they each can in turn provide. And that I think is the key to this. And when we build ecosystems of participants around the service model, that's what we tend to. It's worth noting. I don't wanna stray from service since that's the subject of our podcast, this series. But it's worth noting that these same data very often are used for other commercial purposes. Were, for example, heavily involved in renewables energy, and companies and individuals who have banks of batteries behind the meter are reporting the health of those banks of batteries, not only for maintenance purposes, but so that, Transmission and distribution parties can see what's available and can decide to perhaps turn a bank of batteries on at a certain time to shave off peak power demand. So it has nothing to do with maintaining the asset, the battery, but it has everything to do with another commercial profit motivation. So we're seeing not only service the role of CBM and CBM plus enhancing service capability, but we're starting to see those same principles that were so important. Be used for other commercial purposes just validates that, as you said earlier, wolfie. The data are

Chris Wolff: 18:25

everything. Jeff, you're obviously an expert in this field and I really appreciate you sharing some of your expertise with our listeners today. We really do keep it all about service here, and if we think about how our listeners could get an understanding of how to optimize their existing services business, find incremental revenue. How would they connect with a company like Calypso or an expert like you to advance a project?

Jeffrey Miller: 18:52

The best way to do that is to go to calypso.com and you'll find resources and links there to connected operations and connected products. These are the two areas of our business where much of the technology and the business commercial interests we've talked about here today, Wolfie reside. We work with companies in those two arenas, largely around this, our smart connected operations business, and our smart connected products business. And you'll find both of those linked at Calypso dot. And I would welcome the opportunity to answer anybody's questions and certainly we'd be eager to help.

Chris Wolff: 19:23

And listeners, I also invite you to talk to my team. My organization is all about forming ecosystems that can serve you better. Calypso is one of our premier partners and a fantastic ecosystem participant. We're here to help you with the full digital thread, and particularly the Thingworks technologies that under. Some of these great solutions invite you to look for the next issue of speaking with service coming up very soon. And for today, Jeff, thank you so much for joining us.

Jeffrey Miller: 19:50

Thank you, Wolfie. Great to be with you. Thanks for listening to the Speaking of Service podcast, brought to you by ptc. If you enjoyed this episode, please subscribe wherever you get your podcasts and leave a rating or review. And be sure to check out other episodes to hear new perspectives on improving life for aftermarket professionals, service teams, and the customers they support. If you have a topic of interest or want to provide feedback, email us at speaking of service PTC dot. Or visit us at ptc.com/speaking of service.