6.15.23-STEX-CA-SpencerMetrics

DAVID SPENCER: Good afternoon. Appreciate being invited. We're focused on the transition from big iron into the digital world, where people have manufacturing operations with large-- with lots of people, lots of equipment, and they're trying to do the transformation.

A lot of buttons on this. I found the right button. We started off in the printing industry and moved into the packaging industry. When you think about printing, you're thinking about consumer. But this is industrial printing. And these happen to be big digital industrial printers.

But the issue is that there's a tremendous amount of downtime in these. And that downtime not only costs labor time, but it also costs machine time. And in the end, it is a major factor in terms of not meeting the kind of productivity and cost-effective use of things.

So people are looking at how do they apply OEE type measurements or Six Sigma type technologies, et cetera, to try to improve manufacturing. And the big issue is getting data and analyzing data and presenting that data in a way so that it is usable by the management teams.

So there are lots and lots of issues in a manufacturing operation. And we're trying to resolve all of these by the analytics and data that we collect. All of this, for us, started when we were asked to go and measure in the real world some equipment that had been tested in a lab, but they wanted to know what's the real-world productivity. And when we finished doing the testing, what we found was the machines were great, but people didn't really use them well. And numbers like 40% to 60% productivity were somewhat typical of what we were finding on machines that people expected to be up at the 80% or 90% productivity level.

So in order to resolve this, we had to take a look at some things that people said were really difficult and couldn't be done, in some sense, which was-- number one was the information is in the mind of the operators. And you have to find a way to sort of plug into the mind of the operator and make that information available to everybody. And what we found was that to do that, you had to think about games. And people love to play games on their cell phones, et cetera. And if you can make the operator's environment such that he's happy to use the system in the same way that he might play with his phone, then you can get him or her to give you data that you need of what's going on when all of the measurements you make from machines can't tell you.

The second thing was that we had to be able to interface with all of the equipment-- the legacy equipment, the new digital equipment. So we had to develop custom software and use IoT technologies, et cetera, in order to be able to handle a complete shop, because in this age of transformation to digital, nobody's all digital, and nobody's all conventional.

And we had a customer who gave us this quote, which I thought was really pinpointing what we found, "We're now able to quantify downtime events we didn't even know we had, which has the potential to realize significant financial savings."

You have to present the data through analytics into visualization so that people can actually use it. Data is one thing when you get it. But it's got to be presented so that it can be used. So we present many, many different ways. We use drill-down techniques to do it so that you can mouse over, and you can click on things and see what's going on to be able to really try to understand what the problem is and what needs to be done to resolve it.

Our solution is available currently in two forms. The basic form, we call Lynk. It does all of the automatic machine data capture and gives basic machine information. And then we also have the Connect version of it, where now we're taking operator information, knowledge that only the operators have, merging that in with data information from machines, analyzing that, and providing that with analytics so that you can see the root cause of an issue.

One example of a customer-- this is a multi-billion dollar company with plants around the world. And they installed our Connect system. And they found that they were able to improve data accuracy, which is an interesting thing. Lots of data that comes in isn't accurate, and therefore the results that you get out of it isn't either. And improved operator morale, et cetera. The net impact was an increase of over 17,000 units per hour being produced and an additional revenue of over $1.5 million a year just on one problem that they were solving using our software.

Our business model is to do it as a SaaS system primarily. Competition is broadly based but not focused and including people who are trying to do this in-house, because there is no really good solution out there. We do have some traction now. And we have some partnerships, including, most recently, we're starting to work with MIT Sloan School in the AI area and operations research, which is what MIT likes to call this. And we have a team of people that's been working closely together for a while so that it's a nice core team of people that can execute well.

There's a number of areas in which we're looking for relationships that can help us, help them, help you. And we look forward to talking with people later on. So thank you so much for your time.

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