 from Times Square in New York City, it's theCUBE. Covering IBM's Change the Game, Winning with AI, brought to you by IBM. Welcome back to the Big Apple, everybody. I'm Dave Vellante, and you're watching theCUBE, the leader in live tech coverage. And we're here covering a special presentation of IBM's, Change the Game, Winning with AI. IBM's got an analyst event going on here at the Westin today in the theater district. They've got 50, 60 analysts here. They've got a partner summit going on. And then tonight at Terminal 5 up the West Side Highway, they've got a customer event, a lot of customers there. We've talked earlier today about the hard news. Seth Dobern is here. He's the Chief Data Officer of IBM Analytics, and he's joined by Shreysha Rao, who is the Senior Manager of IT Applications at California-based Niagara Bottling. Gentlemen, welcome to theCUBE. Thanks so much for coming on. Thank you, Dave. Yeah, well, thanks, Dave, for having us. Yeah, it's always a pleasure, Seth. We've known each other for a while now. I think we met in the snowstorm in Boston at the Spark Summit a couple of years ago. And we were both trapped there. Yeah, and at that time, we spent a lot of time talking about your internal role as the Chief Data Officer, working closely with Inderpal Bandari, and what you guys are doing inside of IBM. I want to talk a little bit more about your other half, which is working with clients and the data science elite team, and we'll get into what you're doing with Niagara Bottling. But let's start there in terms of that side of your role. Give us the update. Yeah, like you said, we've spent a lot of time talking about how IBM is implementing the CDO role. While we were doing that internally, I spent quite a bit of time flying around the world, talking to our clients over the last 18 months since I joined IBM. And we found a consistent theme with all the clients in that they needed help learning how to implement data science, AI, machine learning, whatever you want to call it in their enterprise. There's a fundamental difference between doing these things at a university or as part of a Kaggle competition than in an enterprise. And so we felt really strongly that it was important for the future of IBM that all of our clients become successful at this, because what we don't want to do is we don't want in two years from the go, oh my God, this whole data science thing was a scam, we haven't made any money from it. And it's not because the data science thing is a scam, it's because the way they're doing it is not conducive to a business. And so we set up this team, we call the data science elite team. And what this team does is we sit with clients around a specific use case for 30, 60, 90 days. It's really about three or four sprints, depending on the maturity of the client, how long it takes. And we helped them learn through this use case how to use Python, R, Scala in our platform, obviously, because we're here to make money too, to implement these projects in their enterprise. Now, because it's written in completely open source, they can, if they're not happy with what the product looks like, they can take their toys and go home afterwards. And so it's on us to prove the value as part of this. But there's a key point here, my team is not measured on sales, they're measured on adoption of AI in the enterprise. And so it creates a different behavior for them. So they're really about make the enterprise successful, not sell the software. Compensation drives behavior. So at this point, I always ask, well, do you have any examples? So Shrisha, let's turn to you. Niagara Bottling. As a matter of fact, Dave, we do. So you're not a bank with a trillion dollars in assets and under management. Tell us about Niagara Bottling and your role. Well, Niagara Bottling is the biggest private label bottle water manufacturing company in the US. We make bottle water for Costco's, Walmart's, major national grocery retailers. These are our customers whom we service. And as with all large customers, they're demanding and we provide bottle water at relatively low cost and high quality. Yeah, so I used to have a CIO consultancy. We worked with every CIO up and down the East Coast. And I always observed, I really got into a lot of organizations. I always observed that it was really the heads of application that drove AI because they were the glue between the business and IT. And that's really where you sit in the organization. Yes, my role is to support the business and business analytics as well as I support some of the distribution technologies and planning technologies at Niagara Bottling. So take us through the project, if you will. What were the drivers? What were the outcomes that you envisioned? And we can kind of go through the case study. So the current project that we leveraged IBM's help was with stretch wrapper project. Each pallet that we produce, we produce obviously cases of bottle water. These are all stacked into pallets and then shrink wrapped or stretch wrapped with a stretch wrapper. And this project is to be able to save money by trying to optimize the amount of stretch wrap that goes around the pallet. We need to be able to maintain the structural stability of the pallet while it's transported from the manufacturing location to our customer's location where it's unwrapped and then the cases are used. And over breakfast we were talking to you guys produced 2,833 bottles of water per second. Wow. It's an enormous, the manufacturing lines are high speed manufacturing lines and we have a lights out policy where everything runs in an automated fashion with raw materials coming in from one end and the finished goods pallets of water going out. It's called pellets to pallets. So you... Pellets of plastic coming in through the one end and pallets of water going out to the other end. And just as an aside, are you sitting on top of an aquifer or are you guys using some other techniques? Yes indeed, in fact we do bore wells and extract water from the aquifer. Okay, so the goal was to minimize what the amount of material that you use but maintain its stability, is that right? Yes, during transportation, yes. So if we use too much plastic we are not optimally, I mean we're wasting material and the cost goes up. We produce almost 16 million pallets of water every single year. So that's a lot of shrink wrap that goes around those. So what we can save in terms of maybe 15, 20% of shrink wrap costs will amount to quite a bit. So how does machine learning fit into all this? So machine learning is a way to understand what kind of profile we can measure what is happening as we wrap the pallets. Whether we are wrapping it too tight or we're using by stretching it. That results in either a conservative way of wrapping the pallets or an aggressive way of wrapping the pallets. I eat too much material. Too much material is conservative and aggressive is too little material and so we can achieve some savings if we were to alternate between the profiles. So too little material means you lose product, right? Yes, and there's the risk of breakage. So essentially while the pallet is being wrapped, if you are stretching it too much, there's a breakage and then it interrupts production. So we want to try and avoid that. So we want a continuous production at the same time. We want the pallet to be stable and while saving material costs. Okay, so you're trying to find that ideal balance and how much variability is in there? Is it a function of distance and how many touches it has? Maybe you can share it with us. Yes, so each pallet takes about 16 to 18 wraps of the stretch wrapper going around it and that's how much material is laid out. About 250 grams of plastic that goes on there. So we're trying to optimize the gram weight, which is the amount of plastic that goes around each of the plastic. So it's about predicting how much plastic is enough without having breakage and disrupting your line. So they had labeled data, that was if we stretch it this much, it breaks. If we don't stretch it this much, it doesn't break, but then it was about predicting what's good enough, avoiding both of those extremes, right? And so it's a truly predictive and iterative model that we built with them. And you're obviously injecting data in terms of the trip to the stores as well, right? You're taking that into consideration in the model, right? Yeah, that's mainly to make sure that the pallets are stable during transportation. And that has already determined how much containment force is required when you stretch and wrap each pallet. So that's one of the variables that is measured, but the input and outputs are, the input is the amount of material that is being used in terms of gram weight. We are trying to minimize that. So that's what the whole machine learning exercise was. And the data comes from where? Is it observation? Are you instrumental? Yeah, the instruments are stretch wrapper machines have an ignition platform, which is a SCADA platform that allows us to measure all these variables. We would be able to get machine variable information from those machines and then be able to, hopefully one day, automate that process of the feedback loop that says, this is, under this profile, we've not had any breaks. We can continue. Or if there's been frequent breaks on a certain profile or machine setting, then we can change that dynamically as the product is moving through the manufacturing process. Yeah, so think of it. It's kind of a traditional manufacturing production line optimization and prediction problem, right? It's minimizing waste, right? While predicting, while maximizing the output and throughput of the production line. And so it's a typical, when you optimize a production line, the first step is to predict what's gonna go wrong. And then the next step would be to include decision optimization to say, how do we maximize, using the constraints that the predictive models give us, how do we maximize the output of the production line? And so this is not a unique situation. It's a unique material that we haven't really worked with to predict, but they had some really good data on this material, how it behaves. And that's key, as you know, Dave, and probably most of the people watching this know, label data is the hardest part of doing machine learning and building those features from that label data. And they had some great data for us to start with. Okay, so you're collecting data at the edge, essentially, and then you're using that to feed the models, which is running, I don't know, where's it running? Your data center? Yeah, in our data center, there's an instance of DSX local that we stood up and most of the data is running through that. We build the models there. And then our goal is to be able to deploy it to the edge where we can complete the loop in terms of the feedback that happens. It iterates. And DSX local is data science experience? Yes, yeah, slash Watson studio. So there's nothing. Okay, now, what role did IBM in the data science elite team play? We could take us through that. So as we discussed earlier, adopting data science is not that easy. It requires subject matter expertise. It requires understanding of data science itself, the tools and techniques. And IBM brought that as a part of the data science elite team. They brought both the tools and the expertise so that we could get on that journey towards AI. And it's not a do the work for them. It's a teach the fish. And so my team sat side by side with an agriboddling team and we walk them through the process. And so it's not a consulting engagement in the traditional sense. It's how do we help them learn how to do it? And so it's side by side with their team. Our team sat there and walked them through it. For how many weeks? We've had about two sprints already and we're entering the third sprint. It's been about 32, 45 days between sprints. So you have your own data science team. Yes. Our team is coming up to speed using this project. They've been trained and but they needed some help with people who have done this, been there and have handled some of the challenges of modeling and data science. So it accelerates that time to outcome and value and then there's a knowledge transfer component that is occurring now. I guess it's ongoing, right? Yes. And the engagement is unique in that sense, in the sense that IBM's team came to our factory, understood what that process, the stretch wrap process looks like. So they had an understanding of the physical process and then how it's modeled with the help of the variables and understand the data science modeling piece as well. So once they know both sides of the equation, they can help put the physical problem and the digital equivalent together and then be able to correlate why things are happening with the appropriate data that supports those. Yeah, and then the constraints of the one use case and the up to 90 days, there's no charge for this too. So it's, like I said, it's paramount that our clients like Niagara know how to do this successfully in the enterprise. Is it freebie? No, it's no charge. Freebie makes it sound too cheap. Yeah. But it's part of obviously a broader arrangement with buying hardware and software. Yeah, it's a strategy for us to help make sure our clients are successful and I wanted to minimize the activation energy to do that. So there's no charge. And the only requirements from the clients is it's a real use case. They at least match the resources that I put on the ground and they sit with us and do things like this and act as a reference and talk about the team and our offerings and their experiences. So you got to have skin in the game, obviously. And you have, you know, an IBM customer has got to be some commitment for, you know, some kind of business relationship. But how big was the collective team for each, if you will? So IBM had about two to three data scientists. And Niagara matched that, two to three analysts. There was some working with the machines who are familiar with the machines and others more familiar with data acquisition and data modeling. So each of these engagements are, you know, it cost us about $250,000 all in. So they're quite an investment that we're making in our clients. Two to three weeks over many, many weeks of super geek's time, you know. So you're bringing in like hardcore data scientist, math whizzes, stat whizz, data hackers, developer. Data-vis people. Yeah, the whole stack. And the level of skills that Niagara has, you've got this. We've got actual employees who are responsible for production, our manufacturing analysts who help aid in troubleshooting problems. If there are breakages, they go analyze why that's happening. Now they have data to tell them what to do about it. And that's the whole journey that we are in, in trying to quantify with the help of data and be able to connect our systems with data systems that, and models that help us analyze what happened and why it happened and what to do before it happened. Your team has loved this because they're sort of elevating their skills, they're working with rock star data scientists. Yes. And we've talked about this before, you know, a point that was made here is that it's really important in these projects to have people acting as product owners, if you will, subject matter experts that are on the front line that do this every day, not just for the subject matter expertise, because I'm sure those executives that understand it, but when you're done with the model, bringing it to the floor and talking to their peers about it, there's no better way to drive this cultural change of adopting these things and having one of your peers that you respect talk about it instead of some, you know, guy or lady sitting up in the ivory tower saying, that shall. Now you don't know the outcome yet, right? It's still early days, but you've got a model built that you've got confidence in and then you can iterate that model, but what's your expectation for the outcome? We're hoping that the preliminary results should help us get up the learning curve of data science and how to leverage data to be able to make decisions. So that's our idea. There are obviously optimal settings that we can use, but it's going to be a trial and error process and through that, as we collect data, we can understand what settings are optimal and what should we be using in each of the plans. And if the plans decide, hey, they have a subjective preference for one profile versus another, with the data that we are capturing, we can measure when they deviated from what we specified. So we have a lot of learnings coming from the approach that we're taking. You know, you can't control things if you don't measure it first. Well, your objectives are to transcend this one project and do that across and essentially pay for it with a quick return on this. That's the way to do things these days, right? You get sort of more narrow, small projects that give you a quick hit and then leverage that expertise across the organization to drive more value. Yes. Love it. What a great story, guys. Thanks so much for coming to theCUBE and sharing congratulations. You must be really excited. No, it's a fun project. All right. Yeah, thanks for having us, Dave. Appreciate it. Pleasure Seth, always great talking to you and keep it right there, everybody. You're watching theCUBE. We're live from New York City here at the Westin Hotel, CUBE NYC, hashtag CUBE NYC. Check out the ibm.com slash win with AI. I changed the game winning with AI tonight. We'll be right back after this short break.