Cloud brings AI/ML deeper onto plant floor

I use the word “platform” because not only have we done it from a product standpoint and functionality standpoint, we’ve also inserted a services layer within the product architecture. In that layer, we are deploying AI, machine learning, and cognitive services, and we’re delivering them as package components. Rather than some of our competitors who treat all of this as a services-led/consultancy-led project, we’re aiming to deploy packaged solutions in those automation areas that our customers can take but also can tailor.

We have a process going now where we’re taking the best of our ERP, which we have, the best EAM, which we have, but also taking some of our Gartner tier-one service capabilities and bringing that into the core application.

That doesn’t mean to say we’re initially dropping field service, it just means that over a period of time, we will migrate a lot of those capabilities into the core product. We believe that if you truly want to address the circular economy, you really have to have everything almost in one place purely to be able to track materials in the field and be able to bring them back via reverse logistics, repair, remanufacture, and put them back into the field. And to do that, we believe we need a single solution.

Q: Do some of those same features also differentiate IFS Cloud from others in the general EAM software market?

A: There are a lot of what I would call edge applications or core applications that sit on the side of an ERP business solution. We compete against traditional maintenance-type products, but we see maintenance as becoming more into the core application.

It’s not something that you just schedule the repair of an asset; it’s more understanding that asset in the context of manufacturing – making sure that you’re scheduling around those manufacturing tasks, that your costings are around those tasks. It’s a single purchasing solution whether you’re purchasing maintenance spares, or whether you’re purchasing for an ERP, or whether you are scheduling labor resources from an HR skills point of view; you’re not having to pass that data between two applications.

Rather than having multiple products with a single interface, which is a strategy of some of our other competitors, our strategy is to have a bigger core application and a single user interface.

Q: Let me ask about manufacturers or other customers who might have been working with beta versions of IFS Cloud. Can you talk the experience of those users?

A: One of our manufacturing customers that has been an early adopter is Cimcorp. So far the biggest thing they’re saying is the change in user interface is quite dramatic to them, going from very much a forms-based solution like most ERP solutions are, to something that is a little bit more wizard-based. It’s a little bit more like going to a website where you’d expect to be taken through a process rather than know the process when you begin it.

The other thing they’ve said is having everything in one place, data in one place, access to information, is a lot faster.

Q: What features in IFS Cloud should maintenance and reliability know about, or should they be able to take advantage of?

A: We’ve been doing maintenance and asset management for 30 years, so from a core functionality, it’s a Rolls Royce of the product, it’s a very capable product.

I think I stress the word asset management. It’s not just a maintenance product, there’s more to it than just pure maintenance. We are talking about managing assets and as you know, we talk about an asset being an oil rig, which is far more sophisticated than it just being a CNC machine or a production line within a factory.

I think the key takeaway about it is the MES layer: By putting an IoT strategy in place with a data lake, we are in some respects eliminating that need to put an MES layer in. And I’m not talking ISO 95 here, I’m talking just that sort of data connection to a service on a device to be able to pull data out of it. We cover most of ISO 95 within the ERP product anyway.

Rather than relying on strategies where you say, I’m going to maintain this on a predictive basis every three months or every six months, or I’m going to do on a cyclic count of every so many stampings or so many hours production, we’re looking at this far more than taking more data from the device via IoT and using predictive analytics using machine learning to actually come up with something that is far more robust.

We are currently in the middle of a project with a huge paint manufacturer operating in 45 countries, with 4,500 users, and with them we’re looking at using machine learning for predictive maintenance. They paint ships, giant ships, and one of the things they’ve been doing is working with their customers to work out the relative speed of the boat through water compared with engine power.

Basically as the antifouling wears off the ship (i.e., the paint), the ship will go slower for the same amount of power, so they’ve been able to create a software program that their customers use and actually work out this lot of speed and power data. They then can tell the shipowner, “You need to bring Ship Seven into dry dock in two months’ time because the performance of the engine against it through the water is actually now costing you money. It’d be better to repaint it.”

As people build more and more sensors in their devices or into the products they sell, I think we’re going to see a whole plethora of data, and the cloud is probably the single best way of deploying that data.

Taking a machine down for 4-5 hours is huge, which is why it takes a lot of planning. With machine learning, you don’t take the plant down until the machine learning program tells you that you need to take the plant down. I think machine learning ultimately will extend the life of assets, which will increase OEE and all the benefits you get out of having plants up and not suddenly having to schedule a maintenance crew when it’s not needed.

Extending life cycles of machines, and rather than actually have, what I would call routine maintenance, “because we’ve always done it,” we’re now being able to refine those maintenance tasks using real data to say, “Well, you don’t need to yet because it’s not going to fail.”

Q: Given that IFS Cloud is more of a platform than a product, what might be next in IFS’ strategic roadmap?

A: It’s all about the circular economy, the fact that service, maintenance, asset management, and manufacturing are all really starting to come together now. Manufacturers out there are redesigning their products; they’re looking a little bit more at the energy being used when they manufacture a product.

A machine is no longer just something that makes something. A machine starts to become something that adds to the cost of manufacturing. It’s not as simple as an overhead that’s applied generally within the financial ledgers anymore. It’s something where you can take a real look at what you’re doing.

This story originally appeared in the May 2021 issue of Plant Services. Subscribe to Plant Services here.