Assets CollABorative March 2024 Session

IFS Assets CollABorative: Tech Talk Session with Jørgen Rogde, VP Asset Management at IFS

Date of Meeting: 21 March 2024 10:00 AM US Eastern Time 

Presentation:

Slide: IFS CollABorative Asset Management March 2024

• We have a special treat for you today, we're actually going to show you. I'll talk about some of the things that are up and coming in our software and I've got some colleagues with me to help present that.

Slide: Agenda

• My name is Jørgen Rogde. I'm based out of Norway and head up the asset management part of our R&D organization. And today, I'm going to give you a brief overview of some of our strategy updates that we have in the asset management and EAM area. And then we're going to go into a deep dive around failure modes, effects and criticality analysis. This is an investment that we are intending to do now for 24R2.
• We will also overlay that with the application of AI and with me on the call I got Shamila and Christian, who will be the main people they're going to talk today. They are program managers in my organization that are responsible for delivering on the strategy that we set out to achieve. So, I'm looking forward to them sharing some other thoughts on that and if you do have questions on the way, you can then ask them verbally by unmuting yourself and then saying it. Or you can write it in the chat and we will try to respond in the chat as well.

Slide: Asset Management Vision & Mission

• From a vision and mission perspective, I've challenged my organization and also myself. What do we actually want to achieve for? Because we have the ability with IFS Cloud to provide extensive capabilities, revolutionising how we actually do maintenance with the capabilities we have available within IFS Cloud. The composable architecture that we build it on, we can provide out of the box capabilities that are in the context of what you want to achieve, without a lot of effort. That's why we've used the word frictionless in this. So low effort to actually get to value. And we want to embed in industrial AI. AI in the context of the use cases that our customers need, without having to do a lot of work. And who wouldn't want to do that? Everybody talks about the AI and what it can do, but if you actually end up having to do a lot of work in order to get that up and running and functional, then that's going to be problematic as many don't have the skill set, know how, or time and resources to do so.

Slide: Asset Management: Strategy update

• A couple of comments on the strategy. So, we will going forward, continue to deliver capabilities, that are fit for purpose, out of the box. Fitting the customers within the industries that we've focused on, addressing regular requirements, and leveraging the technology partnerships that we have. We are doing some enhancements to the work tasks, to provide specific domain expertise. As you may know, the work task concept that we have are used within asset management, service management, etcetera and they sometimes prevents us from providing the flexibility we want, whilst retaining the compatibility. So, we are doing something that area that gives us more flexibility within my organization to actually develop the purpose built capabilities for task management and job planning and execution for asset management and still leaving the service processes, for instance, to be able to do the same for this.
• We intend to modernize our GIS usage. And we also want to build in 3D integration and visualization with the ability to actually use AI to import models for instance, directly into the system and mapping them against the objects within the asset definition. And we also intend to look into asset investment planning.
• We also want to enhance and expand our capabilities in order to address work safety in general.
• Within Asset Performance Management, which is probably the biggest and most important investment that we would be doing going forward, we want to leverage the IFS.ai capabilities that we have in the platform and there's a lot of new exciting capabilities that helps me deliver those type of capabilities quicker and consistently across asset management.  But the first priority for us is asset performance management, which we will be covering part of today with the FMECA we are intending to do. And applying AI to that, enabling us to do anomaly detection, and also then the predictive maintenance going forward. And for those of you that don't know FMECA, then you will be getting a good introduction to that by Christian and Shamila.

Slide: FMECA – Requirements

• I'm Shamila Fernando. I'm one of the program managers that responsible for this feature. So let us start with what is FMECA. Failure Mode Effect and Criticality Analysis is an analytical method which identifies how a class of assets might fail and what probability and consequences of these failures. Then analyze how critical those failures are, and then make and adjust maintenance strategies to minimize those failures. This process is mostly used by customers to achieve the business goals associated with asset performance management, so they can prioritize and deliver many tenants actions to increase asset availability and reliability. FMECA is typically performed by reliability engineers, maintenance engineers, asset managers and experienced technicians where all of them have a good level of understanding of how the asset is supposed to perform. So, they define the required functions, and how those functions can be failed by defining function failures and failure modes. Then they list down the consequences that is the effects of those failures and do the criticality rating for different categories that the company wanted them to find the most critical failure mode. Then based on the results they deliver the best operational decisions to mitigate those failures.
• Some FMECA users will leverage their industry standards to support their analytical approach. So, within our AI solution, we will address this. With introducing FMECA, we are enhancing existing structure failure management capability in the IFS application today.So, it will provide additional functionality in the setup, facilitation and execution to support organizations in the adoption of Reliability Centered Maintenance. That is RCM. Our solution will be connected to our existing EAM. We have a lot of functionalities within IFS application today, like tasks templates, PM actions, PM programs for reporting, work orders and reporting phase soft technicians as well, which will enable the customers to have full asset lifecycle management capability embedded to the system. So, it will be a complete solution for that.

Slide: What is FMECA?

• Actually we have done a lot of UX work. We have done a lot of screen mock-ups and this is the main screen where FMECA functionality will be performed. First you need to select what is the asset class that you need to perform the FMECA analysis. Then you need to define the process class the that is the which context this items will be placed. Then you can define the failures, functions and failures of this Asset. Then which component this fail can happen. Then you can connect symptoms and what happens if it fails? That is the effect, then in the these two tabs we are giving the possibility to do the criticality analysis and do the decision and actions to define the decision and actions. We will give the capability to connect CM criticality matrix standards that will be connected to the company that they can define their own critical categories that they need to perform the FMECA analysis. So, for each of the categories, they can give the ratings for severity, probability and detectability. Then we will be calculating this RPN number, and then they can find out what are the most critical failure modes for this analysis, and then upon that they can take the decision and actions.

Slide: Why do organisations carry out FMECA?

• My name's Christian. Same position as Shamila based out of Sweden. Taking one step back here and saying why do we do an FMECA? And this is mostly a question to all of you because by the end of this presentation we will have to ask questions to you and would really appreciate if you can give us some feedback on not only why you are do it, but when you do it and what you expect expectations are of doing it. Listing here is just normal ways of why we're doing it. Everybody knows this, so I don't think we need to go through step by step here.

Slide: Why use an embedded FMECA?

• I would much rather focus of why we want to have an FMECA embedded inside of IFS. Why we want to have it in the same application, because normally a FMECA is done on pen and paper, stored in somewhere, some archive nobody can find and nobody really knows why they did it and when they did it. So, having this inside of FMECA, we get a traceability. We log everything to the equipments. We can say why we did it when we did it. We get the history and the revision control, so we can build on this for further updates and creating a new revisions and building on the old ones. Which in my experience been really, really hard, pretty much every time you do an FMECA you starts from scratch every time. We will make the possibility here to connect a maintenance strategy to this so meaning that every object in the system should have a strategy. Why are we maintaining the equipment the way we are? You all know run to failure is also strategy, but during audits you often get questions. Why are you not maintaining your equipment? And that is just as important to point out and that we have done the analysis and we found out that no maintenance was needed.
• Another big advantage of having this inside application is that by one click, one action, we can update both PM actions and PM programs and create work orders. So, the functionality will be that you say what you need to do, which work templates or which work description you need to do to avoid certain failures, and then connect that to objects and say create.
• Next big advantage is of course the access to documentation. Normally we will have the user manuals within the system, meaning that we can access all the data we need, but not only, of course, external documentation. We will also be able to have all the historical data for our equipment within IFS. This will be part of a full APM and EAM functionality. We are really big now. I would say in building our APM strategy here, with collecting a lot of data. We want anomaly detection, we want FMECA, we want to have asset health. We want to have a lot of data in the system which would help us, would help everyone to act on that data to have a better maintenance pretty much. And in the long run also with a lot of good data saying that we can expect when the next failure will happen. But everything is based on having good data in the system, and FMECA is one part of that.
• And then one of the keywords from IFS is to use a single platform. Having the same user experience, having the same end to end flow, having the same system for both the end users, for one doing the analysis, for one doing the planning, which we feel is a really, really big advantage. You're not sending data between systems. We are trying to keep everything within the same system. And of course, having it in one system means you don't have to buy any other systems. There is no need for any license costs or anything for anything else.

Slide: Challenges with FMECA today

• I think most of this covered by Christian already because we have done some user research and also some customer evaluations with our UX designers. We have identified some of the drawbacks with the existing third party tools and excel sheets that used to do the FMECA by most of the customers today. So, each of the steps that FMECA has, there are a lot of drawbacks within those existing systems. Mostly the manual work and there is no good connection between the steps, so each of them isolated and the communication and approval processes and the visibility throughout the system is not there with these third-party tools. So, we have identified those and within our solution we are trying to override those difficulties.

Slide: FMECA Story

• We have a listed down each of the steps. How we are going to tackle those difficulties. In the first step we need to define or initiate the FMECA analysis, we will be providing some lobbies, presenting data, which kind of asset needs. This analysis critical FMECA analysis by connecting to this other APM strategies as well.
• Then in the second step, we are going to get some AI help and try to help to define those functions, function failures, then failure modes and this critical analysis can easily be done then. As Christian also mentioned, we will be providing this revision control and also status handling so any of the approval process or review process can be done within the FMECA itself.
• So, with the this version control, and with AI suggestions, they can improve their FMECA without doing it only once and keep it aside, but they can engage the FMECA results into the maintenance strategies and day-to-day asset maintenance as well.

Slide: FMECA Wireframes

• Let me go through some of the wireframes we have created.

Live Demo:

• As I mentioned before, between UX work we have come up with lot of wire frames that our solution will look like this once developed.  So, in this form we can define the FMECA by giving the item class and process class and connected the same standard and then you can define the functions belongs to this type of assets. Also, for each functions there can be lot of function failures, in the first step you can define those function and function failures. Then this is the failure modes. Here you can use, reuse the functions and function failures defined in the first step. Then connect the failure modes for each of the functions and failing component can be connected. Symptoms and effects also can be connected. Then for each of the failure modes, you can do the criticality analysis as I explained before. Given the rate is 4 different categories, and then decision and actions can be performed, according to the ratings and according to the results of the FMECA. So here you can connect the codes. Each of the codes you can define different actions, preventive actions, corrective actions, or under failure. Any of the maintenance regimes can be defined here. After that, we will be publishing all the results. All the maintenance actions that taken for each of the function, function failure and failure modes. So, the review process can be done here, and the status is controlled, also can be done here. If the actions are OK, then this can be made ready for the applied phase. So, in the apply FMECA form you can do the real actions that taken through the FMECA analysis. So, for each of the objects that belongs to this item class and process class, you can perform this create work orders and create team actions. Then you can observe what are the objects that belongs to this analysis and also you can see the existing PM actions, existing work orders, all those existing maintenance actions in the system can be reviewed here. And together with first tab, you can initiate actions throughout this FMECA. Then the PM programs will be listed in the next tab and you can update the PM programs, because when you are doing the FMECA, you can add new work task templates. So, for that, with those things, you can update the existing PM programs. So those are the wireframes we have come up with. As I mentioned before, we will be providing a dashboard as well, so we’ve created different aspects of the current failures and you can have this type of filters and it will be helpful to do the FMECA.
• Slide: How can AI help? What is in the pipeline
• We're trying to add AI into this development. And how can AI help us? What is IFS doing and how will it be used for FMECA. So, one of the things is accessing your documentation, because documentation hopefully is stored in a document management inside of IFS. We are hoping to use copilot to access that documentation. But also in the future, accessing all other data, all structured data that we have in the in the maintenance system today and maybe not just in the maintenance system, but I guess that would be the main objective. But really getting AI to help you and check what is the history of my equipment. Did it really help when I performed this action? Why did it fail?  How did it fail? Often did it fail? And if we have that data inside the system, if we have good reporting, if we have good tools for doing all the analysis, if we have good tools for entering data, then we have good data for AI to access. And all that good data we have inside of IFS, we want to support the customer by taking it, by taking informative decisions and correct decisions and maintaining the equipment in the best possible way.
• The last point. We want to end up in a full APM story here when we are not only analyzing, we are also suggesting what should be the next step.

Slide: Future: Use Copilot to chat with your documentation

• Already now in the release coming out this spring 24R1, we have an IFS AI copilot chat functionality embedded in the system. For 24R1 this is only chatting with IFS documentation. You can ask questions about IFS and tech documentation and how the functionality in the system should be. I forgot to mention. I had to say that this is a future thing. This is something in development. This it's not 100% certain that it will look like what it looks like in here, but we are hoping. This chat functionality we want to build on for the FMECA, where you can chat with your own documentation. So you can ask questions to your own documentation and say how would my user manual recommend that I maintain this equipment? What are the typical failure modes according to ISO standard for this type of equipment? Skys the limit, but this is the first step that we really, really hope will be part of the this development now for 24R2. When we release FMECA, we want to be able to have this chat functionality to unstructured data, meaning that whatever document you upload into a data lake you should be able to chat with in a safe and secure manner within the IFS application. So you can get feedback straight in.
• Slide: Future: Use Prompts to get better response – Context aware
• What we really hope for as well is to say that we will create client specific prompts which are context aware. This means that when you are inside one specific page in IFS, we want to say that OK, in this page you should pick up this and this value. And when you are asking questions as a default prompt. In this example we see here, you are Alex, an asset manager and you are performing an FMECA for this item clause. And then we pick up pumps in this process class. See water cooling system or whatever. So when you're asking a question, you don't see this. You just ask questions saying Ohh, how does this equipment work? How does this fail? And what do I do when it fails? And the chat, based on these prompts knows what you are asking about. And we're really hoping that this should be so easy that the customer can do it themselves. We're trying to build a user experience here where we don't really have to provide any predefined prompts because it should be as easy to create ones that anyone can do in a couple of minutes. But most likely we will come preloaded with a couple of prompts, which will then be context aware in in for the FMECA page.

Slide: Future: Prompt library – Connect Prompts to Clients

• We want to create a library of prompts which are the client specific. So for the FMECA client, we will use a prompt which are which are picking up on specific attributes in that page. But it doesn't just have to be limited to one prompt for that page. Maybe we want to have 10 different ones and the user can choose between which prompt they want to have. Or maybe we should have a standard prompt for the whole application or for all the asset management pages. You can just imagine what we want and how we can go there, but I mean sky’s the limit. And first release, as I said, we are really, really aiming for this unstructured data. But going into 25, we want to be able to also chat with our structured data. To be able to ask whatever question we have, as long as we have good data in the system. Scary thing with this is that user access control needs to be really thought about, because we can't have all data in the application, it's not for everyone, so we have to have a strict access control even if we are using a chat. That's a big issue for us right now. But other than security things, I think we are on a good path of having this developed quite soon.

Slide: Questions and Answers

• Q: We put up some standard questions that we want some feedback on and that's what I said earlier, are you performing an FMECA today? And when are you doing it? Are you doing it? When you are buying new equipment? Starting up a new site? Or is it after a couple of years of variation?
• A: I can say we are. We are working with FMECA here in our business, so this is very important for us. We use Excel and that's not good. We want to have it in IFS so it looks very good.
• Q: When are you doing it? Are you doing it early on? After a couple years of variation.
• A: If we change anything, then we change the FMECA alert criticality as well. We are using it for the every production line, every year. On a regular basis, but we can perform better, because we are very good when we have something in the system.

Questions / Answers / Feedback / Responses:

• Q: Can Copilot also create a case for Helpdesk if the users doesn't find the information needed?
• A: I don't think so right now. I would say that this is quite a good suggestion. Right now it's a search functionality. We are not really creating anything. Not in the system, not outside the system from the from the chat. Also, next step I would say that having a good answer and having a functionality to say create this based on the answer. We have thought about that in different pages, but it's a little bit tricky inside application because all pages look differently and all the answers can look differently. But yeah, right now, no.

• Q: We are using the Failure Analysis Navigator in IFS. Can you comment on some differences between Failure Analysis Navigator and doing the FMECA?
• A: We are hoping that Failure Analysis Navigator can be dismantled in a couple of releases. We won't do it right now, but we are hoping that FMECA will take over all the functionality that we have in that one today.
• This will be enhancement of a Failure Analysis Navigator to the functionality we have today in the application. So, we will be providing more RCM capabilities within FMECA functionality.
• I would say it mostly it's a combination now that we can act on the data. We can create data from the from the new FMECA. Also with the new design, it looks a little bit more like I'm used to people working with FMECA. As mentioned earlier, the standard way of doing it is in excel and we are trying almost to have UX user experience looking like an Excel spreadsheet because it feels like home for most users. The Failure Analysis Navigator has some other advantages with a tree structure, but it also has some limitations. A tree structure means that you can't skip a step. Everything is mandatory because you can't build on a tree structure if you don't have every branch. Many things you can do in the Failure Analysis Navigator is the basis of an FMECA.
• Q: We are exploring the use of the Failure Analysis in IFS but our IFS Partner didn't have any experience or examples. They've sent request to IFS for good customer example cases.
• A: We'll take that away and we'll put it on to our internal community for consultants and see what comes back.

• Q: Predictive Maintenance? MTBF and Life time analysis etc. Availability. A failure from us is Stop producing.  Total stop.
• A: Yeah, absolutely. I mean predictive is where many people want to go and AI could help us achieve that predictive. It's pretty much based on having good data. I don't really know exactly what you mean with AI helping predictive maintenance, but AI could suggest the way the way forward from the data you have in your system. I really like meantime between failure, but I always have a question straight on when I see that. What is a failure? Have we defined a failure within our organization? Is a failure like a limited output or it's a failure death of an equipment? To be able to know what a meantime failure is, we need to know what a failure is. As customers as end users do you, do you have you describe where a failure is for your equipment?
• And yeah, there might be degraded performance for instance, that could be classified as a failure as well when it goes below a certain threshold, is still functioning, but it is likely to stop functioning at all unless you do something right. So they're probably different ways you would find that, and then it needs to be flexibility enough to be able to define what those type of failures are and degraded performance and other things that would cause you to start intervening in some way.
• Q: Is the idea that there isn't necessarily a right or wrong answer, it's just having clarity as an organization on how you're defining each of those things.
• A: Yes, absolutely.
• Yeah, I think different organizations will have a different need. And it also comes down to the criticality of the asset, and in some cases, as Christian has said, you can run something to failure because it's not critical to our operation, whilst in other cases you stop to see something not performing optimally, if that carries on, it will have a significant negative impact on the ability to produce something or to operate something. And if that stops the function at all, you might have a catastrophic impact on your operation. And that's why I think FMECA doing the failure modes, the criticality, identify which assets are critical to your operation and what do you do in those type of cases. Now I’m just going to throw something out there. My wish is once you've done, your FMECA, you will have a fully autonomous solution. This is a vision, right? It's not going to be there day one, where you can have monitoring on your asset. There are sensors on managing the assets that you have today, right? We can apply self-learning anomaly detection with a limited set of data upfront. That will identify when an anomalous event happens. That would cause an alarm to go off, initiating a process fully automated within IFS, where an action needs to be taken. However, that action does not necessarily be taken by an individual, because we have AI to support this. Knowing that if this kind of thing occurs, these are the typical things that would cause that occur, because we have the maintenance access to the maintenance manuals, we got access to the maintenance history, we got access to all the different failure modes and what could potentially cause that, or has caused that in the past. The AI would then suggest what should be done. And then the first time a human interacts with that, is in cases where you either say, this has happened. These are the three likely things that could cause this. Normally it's this, so we suggest you do this and you say yes or no to that. As you start to trust the system more and more you would say I don't need to say yes or no to that. I'll just let the system do that. Then it will do whatever it's thinking is the best thing and the first time we actually have a user interacting with IFS Cloud is when they get alert on the mobile device to go out and do some work. And with that they go on site to do that work with all the tools needed, and all the spare parts needed, and all that is needed to rectify that fault. The first time to fix will be close to 100%. We can do that. It’s not going to be that day one, but it's certainly possible to get to a point for where you can automate a significant proportion of that. We would like to have that. And that's my aspiration. This is back to where I want to revolutionize the way we do maintenance, because if you think about it, this is a fantastic opportunity to apply AI in the context of the business process that we do. We don't want to do more maintenance than we need to do. And when initially you have an asset, you got an OEM agreement from an OEM and got it within warranty, you probably have to do maintenance according to the maintenance regime that they defined right. Once you're beyond that, you can do whatever you need to do in order to get it operational with the least amount of maintenance you have to do to make sure that it operates to the level that you needed to operate. It should be safe. You need to balance the cost of doing that and keep in mind that the risk of something that currently works if you touch it, it breaks. It's increasing right? The moment you touch something, the risk of it actually stopping functioning is higher than if you didn't stop in the first place, if it did work at the point of you touching it. And we need to get to a point where the system actually does a lot of that for us and it's never going to be perfect. That's why initially you probably need to know that you trust the system. You need to see that it's doing the right thing and you were probably apply this type of intelligence on less critical assets or you have them a high critical assets as well, but you do this in addition to whatever you do at the moment. And then you start to find out how you can actually leverage this and do less on what you know, how you do things today and more of actually getting the AI to help you do that. We can do this, but then we also need the data in order to achieve that. I think it will be fantastic stuff. We can spend time on instead of actually having to do all the manual working there, think all the money you will save from not having to do things you don't need to do anymore.
• Q: How big of a challenge, though, is building that trust. I mean, it seems like from a change management perspective, that's a piece that a lot of companies are going to struggle with.
• A: Yeah, obviously it will be a different challenge to pay depending on the type of asset it is and the criticality of it. so more or less. I was an early adopter of buying a Tesla myself and by no means that's not impressive in Norway when Tesla's they've got 20% - 25% of the market for vehicles in Norway for the last three years. So, lots of people have Tesla’s. With a Tesla, I haven't got a full self-driving, but I got an advanced autopilot where I can drive for 8 hours I might use the autopilot for six hours. And letting the car drive for me, still keeping attention on the road and on hands on the vehicle. Because it's not fully self-driving. So how long did it take me to trust the car to do the things I would never have contemplated trusting your car to do? Took some time, but it didn't take that long for me to start to get comfortable seeing that you were more predictable. But our certain things, I know that I would never allow my car to do by itself, because I know that it would not react as I wanted to based on that experience. But I can't really answer how long it will take, but it's based on trust, right? And that's why you can't go fully autonomous straight away. You learn from it, and if you can have a system that proposes things for you, that is an initial sanity check that you can do, and say I want to do all three of them because it could be all three. And you start to see actually it's getting this right most of the time, and then worst case if I send a technician out and it turns out I don't necessarily have all the spare parts with it, but I sent someone out. But previously I would have sent that technician out every time. And my first-time fix rate would be 30% - 40% and now I'm at 60% - 70%. That's way better than what I have at the moment, so I'm not saying we should go all in AI and autonomous, it would be combination of doing it the way you have used to do it and a new way of doing it. And then you start to fine tune that as you go along, but we will be there to help you achieve that, by mining in the maintenance history, the maintenance manuals and everything else. And the first step to this is during the copilot. And even from a copilot perspective, mining the maintenance manual, these are the typical things you need to do if this type of failure or an anomaly occurs.

• Q: In Oil and Gas we have rigs offshore with limitations in bandwidth. How much impact will AI have? Is much data transfer needed?
• A: I think that the application of AI is something that needs to happen onshore because we're making use of things that are available, for instance, in the cloud. If you have a normally detection monitoring things, it needs the data to be streamed onshore. So, if you want to apply anomaly detection and you got the a lot of sensors that deliver reads at a high frequency, that is what's going to consume bandwidth. There is potentially a challenge if you want to apply that AI offshore in the replicated environment, for instance. That said, I don't think that with the oil and gas, if you got rigs offshore, that the infrastructure is going to prevent you from actually leveraging this in the future. We see more and more of our customers that are that are and going to go always connected. An example (not oil and gas), of a company having 700 vessels operating all over the world. So this is a significantly more complex scenario than having stationary rigs in one location for several months or years. If you have accommodation rigs, accommodation units there. They have signed an agreement with Starlink. A global operation. They also have other types of satellite communication. They used 4G, maybe 5G and other communication approaches. But the fact that signed a global agreement with Starlink, I assume there are SLA’s associated with that, and you will see a significant ramp above of availability to use Starlink. And if you have Starlink, it shouldn't be as much of a communication infrastructure issue. If you want to apply the anomaly detection, and those type of things, you can also decide the frequency of the data stream. How often do you take test points from all of the assets?
• I think it depends on how you adopt AI and the meaning behind it. But if you're want to apply AI to local data, then as long as your network on the platform is restful, then you're OK for that cloud.
• You also comment on the SLA. And that's been a problem, right? They can't guarantee that the service that you buy will be available tomorrow. If you've got Elon Musk's managing this, he may choose to do something erratic at some point. Unpredictable, right? I saw this a press release that they've signed an agreement with Starlink. So, I assume that also will include SLA’s, and if they can do that, they will also benefit you, I'm pretty sure.

Next Meeting: 18 April 2024 10:00 AM US Eastern Time
IFS Assets CollABorative: Think Tank - Regulatory Compliance with Jon Mortensen, Global CTO EAM at IFS

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