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Beyond 3D Podcast with Chris Jones of Actify – Having a digital strategy for big data is critical, and both small and large manufacturers can do it.

Posted by Tyler Barnes on Dec 5, 2016 5:13:39 PM

In our third episode of Beyond 3D, we talk with Chris Jones, CEO of Actify, about why it is critical for manufacturers of all sizes to have a digital strategy to consume, visualize and act on the quintillions of bytes being generated every day. It’s not a question of “should I do this” but rather “when and how will I do this?” Relational databases just don’t cut it anymore – the massive amounts of data being generated need to be able to be mined for specific things, visualized and acted upon quickly and easily. The technology is getting there, and is more accessible to manufacturers of all sizes – yes, even the small guys.

“Overall the stats and the estimates are the only 10% of all this data that we're collecting is being a) collected and then b) collated. In the manufacturing industry we're down 1 or 2%. I think there's opportunities in all industries but particularly in manufacturing companies because they're also producing a lot more data from their products on how it's used and so on and so forth, warranty issues and wear and tear et cetera. They've got all this information but I just don't think it's being used. Couple to that what's also happened is that I think engineering as a profession has certainly not been the favorite occupation for the brightest and smartest coming out of universities. There's a huge shortage of engineers world wide and particularly in the U.S.

The challenge going forward I think is this ... You've got these two opposing forces that need to be solved. You've got this wealth of information that is being produced and then how best to use it with far fewer people. I think going forward this is going to be a real challenge for the industry in general. As I said before, it's not just big sophisticated products and consequently not just big organizations, it's also small to medium companies as well that face this challenge of how best to use this information because if they don't somebody will and somebody will gain a competitive advantage from it I'm sure.”

Chris Jones’ challenge to our listeners: “I think if you're a company that doesn't have a digital strategy on how best to use the information that's been produced now that you have within your organization, the historical information and the information that is almost certainly going to be flowing into your organization over the next couple of years, you seriously need to sit down and think about it. This is a big strategic initiative and it needs executive input I think on how best to move forward to make use of data going forward. It's going to be a competitive advantage for sure.”

Listen to this episode:

To read the full podcast transcript, see below. 

read-transcript.png

Angela Simoes:

Welcome everybody to another episode of Beyond 3D. We are here today with a special guest, Chris Jones, President and CEO of Actify. Hi Chris, welcome and thank you for joining us today.

Chris Jones:

Hi Angela, my pleasure.

Angela Simoes:

From Tech Soft 3D we have Dave Opsahl, who is Vice President of Corporate Development. Thanks Dave for joining us again.

Dave Opsahl:

You're quite welcome Angela, hi.

Angela Simoes:

Hi. Chris, why don't you just spend a couple minutes telling us a little bit about yourself and Actify and maybe just a thirty second definition of discreet manufacturing in case some of our listeners aren't familiar with that term.

Chris Jones:

Sure, several questions there. Starting with Actify, Actify has been around for twenty years now. Our anniversary's coming up this December. We're mostly known for our CAD viewing software, SpinFire, the ability to view 3D models and interrogate those models without a CAD system being present. In recent years we've expanded that foot print to not only allow customers to view 3D CAD information but to also view all of the associated metadata that goes along with that 3D information.

Personally I've been in this industry an awfully long time now. I was working it out the other day, it's over thirty years.

Angela Simoes:

Wow.

Chris Jones:

I've worked for most of the major players. I know, it's an old photo up there so I don't look that old. I think I'm considered an industry veteran. I've worked for most of the big CAD companies and also been involved in their PLM, PDM strategies as well. For a time I was also, I guess a management consultant to some pretty prestigious manufacturing companies like Seiko and Mesder in Japan. That gives you a flavor for my background.

Then when it comes to your question about what is discreet manufacturing, to keep that definition simple I always think about that as people that manufacture things. Something that is physical that you can actually hold unlike the process industry which is all gas, utilities, et cetera. Discreet manufacturing for me is anything that is a physical object. That's how I would describe it.

Angela Simoes:

Okay. In thirty years you've seen a lot of changes happen. What in your opinion has been either the most surprising, or maybe the most exciting thing for you?

Chris Jones:

That's a good question. I think when I take a step back and look now, and this is what really surprises me the most right now, is that in those thirty years I've seen a massive change obviously in computing and computer software and the use of software by companies. I actually believe now the change, the rate of change is probably bigger than any time in the last thirty years. We've gone from, I remember writing my first program and using punch cards to get it into the CPU but now I think there's an absolute tsunami of change coming and you could call it a digital revolution but I believe the rate of change now is greater than any point in the past twenty or thirty years.

Just to sort of try and qualify that with some numbers, today the estimate is that we're producing 2.5 quintillion bytes of data daily and that's out from 2.4 last year. A quintillion is 1 times 10 to the power of 18. 80 to 90% of all the world's data has been created in the last two years. I can see that trend just accelerating because the internet of things explosion hasn't really started yet. Current estimates say that there's between 10 and 12 billion connected devices worldwide today and approximately 2 billion were added in the last twelve months. The estimate is that that number will probably be 50 billion by the start of 2020. If you think about the information all those connected devices are creating or the data that they're creating and how best we can use that then clearly companies need to have a step change in the next couple of years to make the most of that information that's been provided.

The other thing is that a few years ago that data was really only coming from, in my opinion, how would I best describe that? Sophisticated machinery. People have heard stories about how GM, Rolls Royce collect data from a jet engine so they can anticipate maintenance schedules and the whole car industry with the autonomous car. A car like that is actually generating about a gig of byte data per second. It's not just about big things anymore and complex machinery. Just an interesting aside, I went out couple of weekends to buy a padlock.

Angela Simoes:

Okay.

Chris Jones:

I was intrigued to discover there's no combination lock on a padlock anymore, there's no key involved in a padlock, it's a smart padlock. You control it from your phone.

Angela Simoes:

You can't even buy a regular one or an school one?

Chris Jones:

I think you can but you have to find old stock. You can't think of a more simpler device really. My belief is that with all of this data, we've got to start making better use of it. For manufacturing companies, I think they're seriously behind the curve on how to make best use of the data.

A good example would be of another industry, there's a grocery store in England called Tesco's, they have a couple of stores now where as you go in the entrance you pick up your trolley and your cart and you swipe your loyalty card in a card reader and then as you're walking up and down the isles it knows two things. It knows who you are and it knows where you are in the store. It will flash up marketing offers to you as an individual. If you're a new mum for instance, just had a baby, as you're going down that baby isle it will flash up offers about diapers et cetera. If you're a middle aged gentleman like myself or Dave, it probably flashes up offers on red wine as you go into the wine section. That's a really clever use of big data.

Overall the stats and the estimates are that only 10% of all this data that we're collecting is being a) collected and then b) collated. In the manufacturing industry we're down 1 or 2%. I think there's opportunities in all industries but particularly in manufacturing companies because they're also producing a lot more data from their products on how it's used and so on and so forth, warranty issues and wear and tear et cetera. They've got all this information but I just don't think it's being used. Couple to that what's also happened is that I think engineering as a profession has certainly not been the favorite occupation for the brightest and smartest coming out of universities. There's a huge shortage of engineers world wide and particularly in the U.S.

Angela Simoes:

Yeah, I've been talking about that for a while.

Chris Jones:

Yeah. I think probably 600 to 700 thousand engineers down on where you guys need to be. The challenge going forward I think is this ... You've got these two opposing forces that need to be solved. You've got this wealth of information that is being produced and then how best to use it with far fewer people. I think going forward this is going to be a real challenge for the industry in general. As I said before, it's not just big sophisticated products and consequently not just big organizations, it's also small to medium companies as well that face this challenge of how best to use this information because if they don't somebody will and somebody will gain a competitive advantage from it I'm sure.

Angela Simoes:

I can't help but wonder if some of the numbers that you threw out were astronomical numbers, right? We all know that there's a lot of data out there but when you put a quintillion number on it, it's hard to comprehend just how big that is. I can't help but wonder if manufacturers are slow to adopt or slow to do something with it because they're just overwhelmed and don't know what to do with that if they had that kind of data. Do you think that's part of it?

Chris Jones:

No, I think you're absolutely right. I think this comes down to one of the reasons why not much data is being collected and sorted because it's question, what do we do with it when we got it? There's the lack of engineers and they're also manufacturing companies are going through their own quiet revolution I think as well. In the products they produce are also becoming more and more sophisticated so they're trying to keep up with that trend as well. I mention the simple padlock, I was with a customer a couple of weeks ago, the manufacturer's car bumpers. Five, ten years ago this was just a big piece of plastic. Today it's a big piece of plastic that has lane sensors, it has cameras, it has reversing and forward collision cameras and sensors, et cetera, et cetera. They're really fighting a battle I think on several fronts and this is one of the reasons that they are slow to adopt.

That said, I think, I've attended a couple of conferences in the last six months and I'm seeing more and more people ask the question, this is clearly something we need to do, how do we embrace this information that we're receiving? Once we've embraced it, how best to understand what we're receiving? I also believe that an awful lot of the data that is being collected is quite frankly noise.

Angela Simoes:

It's not useful, yeah.

Chris Jones:

You're looking for the 1% that is truly meaningful.

Angela Simoes:

Right.

Chris Jones:

Then that comes down to software companies like us trying to filter that and make better use of the information that people are receiving.

Angela Simoes:

It's funny, in this conversation we might think that manufacturing has been slow to adopt technology but in fact, I think the building industry has been even slower to adopt the use, especially the use of 3D technology, 3D design, viewing 3D models, things like that. Dave would love your perspective on this because you've been in the industry for a long time as well and how you saw that adoption of the use of 3D data in manufacturing in a very intelligent way and lessons learned from that adoption process to okay guys, that was the first wave of really sophisticated technology or some people will say it's a third wave actually. Now you have, let's say this fourth wave where now we have these other things like IOT and AI and AR, what lessons can customers learn from the adoption and incorporating the use of 3D data into now incorporating some of these other technologies?

Dave Opsahl:

I think that the complexity that Chris talked about of trying to deal with the noise of the data coming through is probably more prevalent to discreet manufacturing than it is in the building industry. In some ways the building industry was a little bit more aggressive in the way that it used 3D in some respects but it's also a much ... My brother who owns a structural engineering company would probably kill me if he heard me say this but it's a much simpler problem than say, trying to figure out if I'm a supplier of suspension systems, how what I do integrates into the design of the car that Audi or Volvo is making. That problem of how to filter out the noise and get to the data that has meaning I think is a bigger problem for manufacturing.

One of the ways that we're starting to see more activity from that lesson learned is is that if you're dealing with something that fundamentally is a spacial object, something that exists in space the way that Chris was defining discreet manufacturing, why not use the 3D data as a principal way to navigate that data and let that help you figure out where the noise is coming from or at least maybe even not presenting you with the noise.

For example, if what I'm looking at is a way to be able to understand the material properties of a particular component in an assembly of a suspension system, why do I need to know what the rest of the car looks like or why do I need to know anything about the other parts of that information? Why do I care about a feed and speed data that's coming from an NC machine if it's not related to the particular thing I'm looking at? There's a lot of decisions that can be made but at the end of the day, somebody, a human being of one sort or another is the one that has to look at that information. The context of who they are, what their role is, what kind of data matters to them is a way of being able to filter out that noise and then allowing them to see that in the same way that the designer intended it instead of looking rows and tables of data I think is another way you can be a lot more effective, filtering out that noise.

Chris Jones:

I think actually Dave brings up an excellent point as another challenge that's facing discreet manufacturing companies is that you've got all this information coming from hundreds of different data sources. The same could be said for the retail banking industry and so on. The big fundamental difference is that discreet manufacturers make something that's typically a 3D object. You've got this 3D database that over the years has grown in the amount of data that is being stored in that 3D database and today to make use of it, along with all the other information, is a unique requirement for manufacturing companies.

Dave mentioned rows and columns of data, I think that is another fundamental weakness of in fact the software industry and this is something that Actify's been working on hard and to produce a foundation or a solution that allows somebody to view information from multiple different data sources including 3D. Unless you can mash the 3D data together with this other information, it could be material, it could be cost, time, warranty information, et cetera, information that's coming from the shop floor via sensors or wherever, unless you can include 3D in that equation it's almost meaningless. I think that the software industry and the people that provide solutions to manufacturing companies have some catching up to do. If I look at most PDM, PLM systems and the RP systems that they're at there in the marketplace today, 99.9% of them are using based on the foundation of a solution that was invented by Ted Codd from England 50 years ago and that's a relational database. With the rate of change and the amount of information coming into an organization and that information that needs to be collated and diced and sliced and viewed and made sense of, a relational database quite frankly just doesn't cut it anymore.

Angela Simoes:

 

Right.

Chris Jones:

You have to go to a graph type architecture. We've taken the lead from people like Facebook and other social media companies in our solutions and they're built on top of the graph database, which allows you to view and interrogate information that's coming from multiple different data sources in a very flexible way. You just can't do that with a relational database. It also allows people to search it in a far more intuitive matter because relational databases are very good at short facts searches, how many people over 50 live in North London, for instance. They're not very good at things like, have I manufactured anything that looks like this before? If so, where did I manufacture it? Who's the engineer in charge? What facility did I make it in? Have I still got the toolling? What was the lead time? Those long thin queries that engineers tend to do are also not suited to the old fashioned relational databases. You can see how you've got all these different moving parts. It's a challenge, certainly for companies like Actify to produce solutions that satisfy that need and also for manufacturing companies to understand how best to use that information once it's presented to them.

Angela Simoes:

Can you think of, and maybe you have a customer that's doing this or can you think of a manufacturer either large or small that has started to tackle this challenge and is headed in the right direction? Probably they have not solved it completely but they are headed in the right direction and it's an example that maybe other manufacturers can follow?

Chris Jones:

Sure. I can go both ends of the spectrum. A small company we have, have a very simple business rule. If the price of the raw material of the component they are manufacturing goes above 30% of their quote price, their profit margin starts to be eroded. That's a quite simple business rule. Now today, how do you know that's happening? It's very difficult to collate information from 3D CAD to get the size and the volume of the component, the material information, the price of the raw material in the open marketplace, the price that was quoted to the customer in the first place and then apply that business rule. Today they typically don't know until it's very late in the project, it's then difficult to tell the customer, by the way we want to put the price up.

You're collecting information from 3, 4, 5 different data sources and then joining the dots manually. That's something that we have done for a customer where instantly once the engineer saves the 3D CAD file it converts using Tech Soft, we extract the metadata, the volume, we compare the quote information, we apply a business rule, and if it breaks that business rule and there is an erosion of profit margin, we email the COO in this case. That's I think a very a good simple example of how we are collating information from multiple different data sources, mashing it together to present an actionable alert to somebody.

At the other end of the spectrum we did some work for a very large U.K. automotive company. That was looking at the whole car program basically. We would pull up the car platform, the model year, and we could immediately alert an engineer if there was an issue of a pre-used sub-assembly that they were planning to use, perhaps the rear suspension system from the 2016 model and the 2018 model, if there was a warranty issue. Again, this was collating information from CAD and issues management data base, their warranty data base, and mashing that information and again, presenting it to an engineer so that he could take action. Again previously, that time line from there being a warranty issue in the field getting through to a design engineer that was designing a car for two years hence was protracted to say the least and probably they were going to miss those release gates. Just a couple of relatively simple examples.

Angela Simoes:

It's good to hear that both the small manufacturer as well as a large manufacturer can implement these kinds of solutions because I think oftentimes and Dave you can attest to this, that the small guys will say, yeah we'd love to do that but and I don't have the budget of a big guy to implement this huge enterprise system. As the technology becomes more sophisticated, it also becomes more accessible, would you say?

Dave Opsahl:

Yeah, I think that's another lesson learned. Chris was talking about the pace of change or the rate of change with technology. That's made it accessible to small and medium sized companies. From a lessons learned standpoint, I think not so many years ago small and medium sized companies could afford to not really spend a lot of time thinking about, do they need to solve problems like the one Chris was describing? They've always had the problems, they've just considered that the solutions were only accessible to the General Motors or the Boeings, or the other large manufacturers out there. The technology has changed so fast and is continued to change at such a rate that it's not produced tools like what Actify does that make it accessible for those small to medium manufacturers to do that. I would say that beyond it just being accessible now, it's becoming a way that they can grow their business because they're much better positioned to respond when there's a change like that.

I think we're going to see more and more of that pressure and I think it's a positive pressure, it's going help manufacturing continue to be competitive regardless of where you are in the world.

Angela Simoes:

With that, I think we're at our time. We just have one last question for Chris. Chris, we ask all of our guests at the end for a call to action or a bit of advice for our listeners. If you could have one bit of advice or want our listeners to go out and do, one thing after listening to this podcast, what would you ask them to do?

Chris Jones:

I think if you're a company that doesn't have a digital strategy on how best to use the information that's been produced now that you have within your organization, the historical information and the information that is almost certainly going to be flowing into your organization over the next couple of years, you seriously need to sit down and think about it. This is a big strategic initiative and it needs executive input I think on how best to move forward to make use of data going forward. It's going to be a competitive advantage for sure.

Angela Simoes:

Agreed. I think all of us can agree on that. We will definitely include a link to Actify in our show notes so that for those of you who will be starting to create a digital strategy or modifying your existing one, definitely encourage you to look at Actify as the best part of that solution, as well as Tech Soft 3D of course Dave. Don't worry I would not forget to mention us. Thank you both for joining us, I think this is a really great conversation and I hope that our listeners found it helpful. If you have not subscribed yet to our listeners, if you haven't subscribed yet, please do so on iTunes or on SoundCloud and share this podcast with your colleagues and friends, other folks in the manufacturing industry. We thank you very much for listening and until our next episode, have a great day.

 

Topics: Manufacturing, Discreet Manufacturing, Big Data, 3D, IoT, Connected Devices, Database, SMB

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