Whether your favorite condiment is Heinz ketchup or your preferred spread for your bagel is Philadelphia cream cheese, ensuring that all customers have access to their preferred products at the right place, at the right price, and at the right time requires careful supply chain organization and distribution. Amid the proliferation of e-commerce and shifting demand within the consumer-packaged goods (CPG) sector, AI and machine learning (ML) have become helpful tools in enabling efficiency and better business outcomes.
The journey toward successfully deployed machine learning operations (MLOps) starts with data, says global head of machine learning operations and platforms at Kraft Heinz Company, Jorge Balestra. Curating well-organized and accessible data means enterprises can leverage their data volumes to train and develop AI and machine learning models. A strong data strategy lays the foundation for these AI and machine learning tools to use data to detect supply chain disruptions, identify and address cost inefficiencies, and predict demand for products.
“Never forget that data is the fuel, and data, it takes effort, it is a journey, it never ends, because that’s what is really what I would call what differentiates a lot of successful efforts compared to unsuccessful ones,” says Balestra.
This is especially crucial but challenging within the CPG sector where data is often incomplete given the inconsistent methods for consumer habit tracking among different retailers.
He explains, “We don’t know exactly and we don’t even want to know exactly what people are doing in their daily lives. What we want is just to get enough of the data so we can provide the right product for our consumers.”
To deploy AI and machine learning tools at scale, the Heinz Kraft Company has turned to the flexibility of the cloud. Using the cloud can allow for much-needed data accessibility while mitigating compute power. “The agility of the whole thing increases exponentially because what used to take months, now can be done in a matter of seconds via code. So, definitely, I see how all of this explosion around analytics, around AI, is possible, because of cloud really powering all of these initiatives that are popping up left, right, and center.” says Balestra.
While it may be challenging to predict future trends in a sector so prone to change, Balestra says that preparing for the road ahead means focusing on adaptability and agility.
“Our mission is to delight people via food. And the technology, AI or what have you, is our tool to excel at our mission. Being able to learn how to leverage existing and future [technology] to get the right product at the right price, at the right location is what we are all about.”
This episode of Business Lab is produced in partnership with Infosys Topaz and Infosys Cobalt.
Full Transcript
Laurel Ruma: From MIT Technology Review, I’m Laurel Ruma, and this is Business Lab, the show that helps business leaders make sense of new technologies coming out of the lab and into the marketplace.
Our topic is machine learning in the food and beverage industry. AI offers opportunities for innovation for customers and operational efficiencies for employees, but having a data strategy in place to capture these benefits is crucial.
Two words for you: global innovation.
My guest is Jorge Balestra, global head of machine learning operations and platforms at Kraft Heinz Company.
This episode of Business Lab is produced in partnership with Infosys Topaz and Infosys Cobalt.
Welcome, Jorge.
Jorge Balestra: Thank you very much. Glad to be here.
Laurel: Well, wonderful to have you. So people are likely familiar with Kraft Heinz since it is one of the world’s largest food and beverage companies. Could you talk to us about your role at Kraft Heinz and how machine learning can help consumers in the grocery aisle?
Jorge: Certainly. My role, I will call, has two major focuses in two areas. One of them is I lead the machine learning engineering operations of the company globally. And on the other hand, I provide all of the analytical platforms that the company is using also on a global basis. So in role number one in my machine learning engineering and operations, what my team does is we grab all of these models that our community of data scientists that are working globally are coming up with, and we grabbed them and we strengthened it. Our major mission here is the first thing we need to do is we need to make sure that we are applying engineering practices to make them production ready and they can scale, they can also run in a cost-effective manner, and from there we ensure that in my operations hat they are there when needed.
So a lot of these models, because they become part of our day-to-day operations, they’re going to come with certain specific service level commitments that we need to make, so my team makes sure that we are delivering on those with the right expectations. And on my other hand, which is the analytical platforms, is that we do a lot of descriptive, predictive, and prescriptive work in terms of analytics. The descriptive portion where you’re talking about just the regular dashboarding, summarization piece around our data and where the data lives, all of those analytical platforms that the company is using are also something that I take care of. And with that, you would think that I have a very broad base of customers in the company both in terms of geographies where they are from some of our businesses in Asia, all the way to North America, but also across the organization from marketing to HR and everything in between.
Going into your other question about how machine learning is helping our consumers in the grocery aisle, I’ll probably summarize that for a CPG it’s all about having the right product at the right price, at the right location for you. What that means is on the right product, their machine learning can help a lot of our marketing teams, for example, even when they are now with the latest generative AI capabilities are showing up like brainstorming and creating new content to R&D, what we’re trying to figure out what is the best formulas for our products, there’s definitely now ML is making inroads in that space, the right price, all about cost efficiencies throughout from our plans to our distribution centers, making sure that we are eliminating waste. Leveraging machine learning capabilities is something that we are doing across the board from our revenue management, which is the right price for people to buy our products.
And then last but not least is the right location. So we need to make sure that when our consumers are going into their stores or are buying our products online that the product is there for you and you’re going to find the product you like, the flavor you like immediately. And so there is a huge effort around predicting our demand, organizing our supply chain, our distribution, scheduling our plans to make sure that we are producing the right quantities and delivering them to the right places so our consumers can find our products.
Laurel: Well, that certainly makes sense since data does play such a crucial role in deploying advanced technologies, especially machine learning. So how does Kraft Heinz ensure the accessibility, quality and security of all of that data at the right place at the right time to drive effective machine learning operations or MLOps? Are there specific best practices that you’ve discovered?
Jorge: Well, the best practice that I can probably advise people on is definitely data is the fuel of machine learning. So without data, there is no modeling. And data, organizing your data, both the data that you have internally and externally takes time. Making sure that it’s not only accessible and you are organizing it in a way that you don’t have a gazillion technologies to deal with is important, but also I would say the curation of it. That is a long-term commitment. So I strongly advise anyone that is listening right now to understand that your data journey, as it is, is a journey, it doesn’t have an end destination, and also it’s going to take time.
And the more you are successful in terms of getting all the data that you need organized and making sure that is available, the more successful you’re going to be leveraging all of that with models in machine learning and great things that are there to actually then accomplish a specific business outcome. So a good metaphor that I like to say is there’s a lot of researchers, and MIT is known for its research, but the researchers cannot do anything without the librarians, with all the people that’s organizing the knowledge around so you can go and actually do what you need to do, which is in this case research. Never forget that data is the fuel, and data, it takes effort, it is a journey, it never ends, because that’s what is really what I would call what differentiates a lot of successful efforts compared to unsuccessful ones.
Laurel: Getting back to that right place at the right time mentality, within the last few years, the consumer packaged goods, or you mentioned earlier, the CPG sector, has seen such major shifts from changing customer demands to the proliferation of e-commerce channels. So how can AI and machine learning tools help influence business outcomes or improve operational efficiency?
Jorge: I’ve got two examples that I can say. One is, well, obviously we all want to forget about what happened during the pandemic, but for us it was a key, very challenging time, because out of nowhere all of our supply chains got disrupted, our consumers needed our products more than ever because there were more hunkered down at home. So one of the things that I tell you, at least for us, that was key was through our modeling, through the data that we’ve had, we’ve had some good early warning of certain disruptions in the supply chain and we were able to at least get… Especially when the outbreak started, a couple of weeks in advance, we were moving product, we were taking early actions in terms of ensuring that we were delivering an increased amount of product that was needed.
And that was because we had the data and we had some of those models that were alerting us about, “Hey, something is wrong here, something is happening with our supply chain, you need to take action.” And taking action at the right time, it’s key in terms of getting ahead of a lot of the things that can happen. And in our case, obviously we live in a competitive world, so taking actions before competition is important, that timing component. Another example I can give you and is more of something that is we’re doing more and more nowadays is this piece that I was referring to about the right location about product availability is key for CPG, and that is measured in something that is called the CFR, and is the customer field rate, which means is when someone is ordering product from Kraft Heinz that we are able to fulfill that order to 100%, and we are expecting to be really high with high 90s in terms of how efficient we are filling those orders.
We have developed new technology that I think we are pretty proud of because I think it is unique within CPG that allows us to really predict what is going to happen with CFR in the future based on the specific actions we’re taking today, whereas it’s changing our production lines, whereas changes in distribution, et cetera, we’re able to see not only the immediate effect, but what’s going to happen in the future with that CFR so we can really act on it and deliver actions right now that are in the benefit of our distribution in the future. So those are, I would call it, say, two examples in terms of how we’re leveraging AI and machine learning tools in our day-to-day operations.
Laurel: Are those examples, the CFR as well as the supply chain and making sure consumers had everything on demand almost, is this unique to the food and beverage industry? And what are perhaps some other unique challenges that the food and beverage industry faces when you’re implementing AI and machine learning innovations? And how do you navigate challenges like that?
Jorge: Yeah, I think something that is very unique for us is that we always have to deal with an incomplete picture in terms of the data that we have in our consumers. So if you think about it, when you go into a grocery store, a couple of things, well, you are buying from that store, the Kroger’s, Walmart’s, et cetera, and some of those will have you identified in terms of what is your consumption patterns, some will not. But also, in our case, if you are going to go buy a Philadelphia [cream cheese], for example, you may choose to buy your Philadelphia in multiple outlets. Sometimes you want more and you go to Costco, sometimes you need less, in my case, I live in the Chicago land area, I go to a Jewel supermarket.
We always have to deal with incomplete data on our customers, and that is a challenge because what we are trying to figure out is how to better serve our consumers based on what product you like, where you’re buying them, what is the right price point for you, but we’re always dealing with data that is incomplete. So in this case, having a clear data strategy around what we have there and a clear understanding of the markets that we have out there so we can really grab that incomplete data that we have out there and still come up with the right actions in terms of what are the right products to put, just to give you an example, a clear example of it is… And I’m going back to Philadelphia because, by the way, that’s my favorite Kraft product ever…
Laurel: Philadelphia cream cheese, right?
Jorge: Yes, absolutely. It’s followed by a close second with our ketchup. I have a soft spot for Philadelphia, pun intended.
Laurel: – and the ketchup.
Jorge: Exactly. No, but you have different presentations. You have the spreadable, you have the brick of cream cheese, within the brick you have some flavors, and what we want to do is make sure that we are providing the flavors that people really want, not producing the ones that people don’t want, because that’s just waste, without knowing specifically who is buying on the other side and you want to buy it in a supermarket, one or two, or sometimes you are shifting. But those are the things that we are constantly on the lookout for, and obviously dealing with the reality about, hey, data is going to be incomplete. We don’t know exactly and we don’t even want to know exactly what people are doing in their daily lives. What we want is just to get enough of the data so we can provide the right product for our consumers.
Laurel: And an example like cream cheese and ketchup probably, especially if a kid is in the house, it’s one of those products that you use on a fairly daily basis. So knowing all of this, how does Kraft Heinz prepare data for AI projects, because that in itself is a project? So what are the first steps to get ready for AI?
Jorge: One thing that we have been pretty successful on is what I would call the potluck approach for data. Meaning that individual projects, individual groups are focused on delivering a very specific use case, and that is the right thing to do. When you are dealing with a project in supply chain and you’re trying just to, for example, say, “Hey, I want to optimize my CFR,” you are really not going to be caring that much about what sales wants to do. However, if you implement a potluck approach, meaning that, okay, you need data from somebody else, and it’s very likely that you have data to offer because that’s part of your use case. So the potluck approach means that if you want to try out the food of somebody else, you need to bring your own to the table. So if you do that, what starts happening is your data, your enterprise data, becomes little by little more accessible, and if you do it right eventually you pretty much have a lot and almost everything in there.
That is one thing that I will strongly advise people to do. Think big, think strategically, but act tactically, act knowing that individual projects, they’re going to have more limited scope, but if you establish certain practices around sharing around how data should be managed, then each individual projects are going to be contributing to the larger strategy without the largest strategy being a burden for the individual projects, if that makes sense.
Laurel: Sure.
Jorge: So at least for us that has been pretty successful over time. So we have data challenges absolutely as everybody else has, but at least from what I’ve been able to hear from other people, but Kraft Heinz is in a good place in terms of that availability. Because once you reach a certain critical mass, what ends up happening is there’s no need to bring additional data, you are always now reusing it because data is large but it’s finite. So it’s not infinite. It’s not something that’s going to grow forever. If you do it right, you should see that eventually, you don’t need to bring in more and more data. You just need to fine-tune and really leverage the data that you have, probably be more granular, and probably get it faster. That’s a good signal. I have the data, but I need it faster because I need to act on it. Great, you’re on the right track. And also your associated cost around data should reflect that. It should not grow to infinity. Data is large but is finite.
Laurel: So speaking of getting data quickly and making use of it, how does Kraft Heinz use compute power and the cloud scaling ability for AI projects? How do you see these two strategies coming together?
Jorge: Definitely the technology has come a long way in the last few years, because what cloud is offering is more of that flexibility, and it’s removing a lot of the limitations, both in terms of the scale and performance we used to have. So to give you an example, a few years back I had to worry about “Do I have enough storage in my servers to host all the data that we are getting in?” And then if I didn’t, how long is it going to take for me to add another server? With the cloud as an enabler, that’s no longer an issue. It’s a few lines of code and you get what you need. Also, especially on the data side, some of the more modern technologies, talking about Snowflake or BigQuery, enable you to separate your compute from your storage. What it basically means in practical terms is you don’t have people fighting over limited compute power.
So data can be the same for everyone and everybody can be accessing the data without having to overlap each other and then fighting about, oh, if you run this, I cannot run that, and then we have all sorts of problems so definitely what the cloud allowed us to do is get out of the way in terms of the technology as a limitation. And the great thing that happened down there now with all the AI projects is now you could focus on actually delivering on the use cases that you have without having to have limitations around “how am I going to scale?”. That is no longer the case. You have to worry about costs because it could cost you an arm and a leg, but not necessarily around how to scale and how long it’s going to take you to scale.
The agility of the whole thing increases exponentially because what used to take months, now can be done in a matter of seconds via code. So definitely I see how all of this explosion around analytics, around AI is possible, because of cloud really powering all of these initiatives that are popping up left, right, and center.
Laurel: And speaking about this, you can’t really go it alone, so how do partners like Infosys help bring in those new skills and industry know-how to help build the overall digital strategy for data, AI, cloud, and whatever comes next?
Jorge: Much in the same way that I think cloud has been an enabler in terms of this, I think companies and partners like Infosys are also that kind of enablers, because, in a way, they are part of what I would call an expertise ecosystem. I don’t think any company nowadays can do any of this on its own. You need partners. You need partners that both are bringing in new ideas, new technology, but also they are bringing in the right level of expertise in terms of people that you need, and in a global sense, at least for us, having someone that has a global footprint is important because we are a global company. So I will say that it’s the same thing that we talked about earlier about cloud being an enabler: that expert ecosystem represented by companies like Infosys is just another key enabler without which you will really struggle to deliver. So that’s what I’ll probably say to anyone that is listening right now, make sure that your ecosystem, your expert ecosystem is good and is thriving and you have the right partners for the right job.
Laurel: When you think about the future and also all these tough problems that you’re tackling at Kraft Heinz, how important will something like synthetic data be to your data strategy and business strategy as well? What is synthetic data? And then what are some of those challenges associated with using it to fill in the gaps for real-world data?
Jorge: In our case, we don’t use a lot of synthetic data nowadays because at least from the areas that we have holes to fill in terms of data is something that we’ve been dealing with for a while. So we are, let’s put it this way, already familiar on how to produce and fill in the gaps using some of the synthetic data techniques, but not really to the same extent as other organizations are. So we are still looking for opportunities when that is the case in terms of what we need to use and leverage synthetic data, but it’s not something that least for Kraft Heinz and CPG at all we use extensively in multiple places as other organizations are.
Laurel: And so, lastly, when you think ahead to the future, what will the digital operating model for an AI-first firm that’s focused on data look like? What do you see for the future?
Jorge: What I see for the future is, well, first of all, uncertainty, meaning that I don’t think we can predict exactly what’s going to happen because the area in particular is growing and evolving at a speed that I think is just honestly dazzling just because of the major things. I think at least what I would say is the real muscle that we need to be exercising and be ready for is adaptability. Meaning that we can learn, we can react, and apply all of the new things that are coming in hopefully at the same speed that they’re occurring and really leveraging new opportunities when they present themselves in an agile way. But at least from the how to prepare for it I think it’s more about preparing the organization, your team, to be ready for that, really act on it, and be ready also to understand the specific business challenges that are there, and look for opportunities where any of the new things or maybe existences that are happening can be applied to solve a specific problem.
We are a CPG company, and that means the right product, right price, right location, so anything boils down to how can I be better in those three dimensions leveraging whatever is available today, whatever’s going to be available tomorrow. But keep focusing on, at least for us, we are a CPG company, we manufacture in Philadelphia, we manufacture ketchup, we feed people. Our mission is to delight people via food. And the technology, AI or what have you, is our tool to excel at our mission. Being able to learn how to leverage existing and future to get the right product at the right price at the right location is what we are all about.
Laurel: That’s fantastic. Thank you so much, Jorge. I appreciate you being with us today on the Business Lab.
Jorge: Thank you very much. Thank you for inviting me.
Laurel: That was Jorge Balestra, global head of machine learning operations and platforms at Kraft Heinz Company, who I spoke with from Cambridge, Massachusetts, the home of MIT and MIT Technology Review.
That’s it for this episode of Business Lab. I’m your host, Laurel Ruma, I’m the director of insights, the custom publishing division of MIT Technology Review. We were founded in 1899 at the Massachusetts Institute of Technology, and you can also find us in print, on the web, and at events each year around the world. For more information about us and the show, please check out our website at technologyreview.com.
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This content was produced by Insights, the custom content arm of MIT Technology Review. It was not written by MIT Technology Review’s editorial staff.
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