Transforming Gigafactories with Siemens, AWS, and Voltaiq – The Battery Podcast S01E15 – Transcript
Gigafactories are not just hard to start, they are hard to perfect. But by going digital and pursuing a data-driven approach, our partners are helping us make the process easier and more profitable.
Nick Finberg
It’s time for another episode of The Battery Podcast from Siemens Digital Industries. We are wrapping up the season with a two-part discussion on data-driven manufacturing for the battery industry. We will hear from our host Puneet Sinha of Siemens, as well as experts from Voltaiq and AWS to examine what we are working on to deliver more effective manufacturing processes across industry.
Puneet Sinha
Siemens is partnering with AWS number one in cloud and Voltaiq which is number one company when it comes to battery intelligence. So we are bringing number one company in industrial software, number one player in cloud, and number one player in battery intelligence, and we put our technologies and expertise together to achieve what I just showed you in the previous slide. Before I go and tell you more about this data-driven manufacturing, I want to emphasize that this is not a concept. We are delivering this. to customers, and there are some of the benefits that we are helping our customers achieve. 15% improvement in production efficiency because they have additional to an factory, they can understand how different machines are working, what are the problems they can solve. Two times faster production scale up because they have the right IT-OT convergence, and 10% or more reduction in scrap rate because they are able to leverage the data that is coming out from their factories and extract the battery-specific intelligence from that. To talk about some of these customer stories, what we are doing, I would like to bring my partners in crime, Hari Gopalakrishna, who is worldwide head of strategy at AWS, and Tal Sholklapper, who is CEO and co-founder of Voltaiq. And what I’m hoping is rather than me talking, we talk through our collective vision and practice on data-driven manufacturing, how we are delivering this to customers, and what kind of questions we are getting from them. But before we go ahead, I would like to go ahead and Hari, help us understand how AWS is looking at battery, industry, what kind of values you guys are bringing to the industry.
Hari Gopalakrishna
Hello. First of all, thanks for having me. So you’ve done a really good job of summarizing the challenges, so I wouldn’t really dwell on too much. But I’ll share with you some of the things that are we’re here. from our customers. The first one is around high cost of experimentation in battery manufacturing. Developing new battery technology, new process. This is requiring a large amount of prototyping, lab testing, etc. And this is really expensive and time consuming. The other thing that we hear a lot is around the scaling of lab to giga scale, right? Really scaling up manufacturing while maintaining consistent quality across the production is a really complex challenge. And it’s something that we have to solve. And the third one is around really long time to market and high scrap rates. Of course, this is, you know, you’re producing millions of cells and you have to maintain a tight quality control across all of those. And that has to be done, but it’s not a trivial task. So what you might ask is, hey, what is AWS’s role in all of this? First of all, as you mentioned earlier, all of this generates massive amount of data. And this, obviously on the floor, equipment, process, MES systems, ERP systems, PLM systems, they all generate data, but there’s also data generated for materials from supply chain. So all of this data, you have to make sense of this data, right? And so for us, AWS offers a suite of data services, including advanced analytics, machine learning, generative AI, and all of this to make sense of this data, generate insights from the data. And we have partnered with yourself, Siemens, and Voltaiq, to bring in the domain expertise. So we consider us domain experts in data. Siemens brings expertise in industrial automation, connectivity, software, and you bring, from Voltaiq, battery intelligence, battery knowledge. And we want to bring all of this is in an integrated set of technologies and tools to help address all of the challenges that I mentioned and help accelerate innovation and really shorten that time to market.
Puneet Sinha
Awesome. And we will delve into all of these things in a few minutes. But before that, Tal, help us understand Voltaiq and what you are doing and how you are looking at the battery industry.
Tal Sholklapper
Yeah, so a little bit of background. I started my career as a materials engineer, designing what went into batteries and fuel cells. And from there, you know, technology to partners who manufactured it out of the Department of Energy and then started my own company where we manufactured technology as well. And one of the challenges my team constantly had is trying to make decisions from all this valuable data on the production line, whether it was to identify root cause, issues we started having in the field, where did those come from? And so we realized, you know, this opportunity here, especially as the battery industry is becoming really as a critical foundational component in our modern, not just electronics, but also our vehicles and on the grid, that we really needed this kind of technology to not only help accelerate the next generation of technology. But if you look at it today, you know, China has hit scale. They have hit economies of scale. Their price points for cells, for vehicles, for packs, everything is. dramatically lower because they have hit those inflection points. And for the rest of the economy here, outside of China, the key is really going to be how do you get to those economies of scale, how do you get to those quality levels in order to be competitive in the market going forward? And really one of the ways we saw to do that was to combine with a handful of key partners to deliver a really complete solution to the market that allows you to not only collect the data, you know, on the equipment and be able to bring that into a common platform, but also start to iterate and learn a whole lot faster with these modern, you know, data infrastructures, which involve the cloud. And so bringing that together to help, you know, shift that left, identify problems faster, help identify root cause, help spin up these new factories in a much faster, more agile way so that you can hit those price points with these new products.
Puneet Sinha
Awesome. Awesome. So, So let’s dive a little bit deeper. I talk about data-driven manufacturing, how we are looking at it, but I’d love to hear your perspective on criticality of data-driven manufacturing for battery industry. So if you want to share something more, I’ll start with you, Tal.
Tal Sholklapper
Yeah, I was just on with the head of quality at a factory. I’ve got a call yesterday morning, and it’s one of those things now as they’re bringing on their next factories, that things are pretty stable and the one they’ve been running for a number of years now, but with the new one, it’s all super manual. It is a slow process. You know, the reality right now is between when, you know, issues are identified and they’re able to remediate them, you may end up having months that are spent really on the ground trying to piece that all together. And you can’t take that long anymore. These multi-year ramp-ups, these high scrap rates or high return rates if stuff actually makes it into the field, are just not going to cut it as far if you want to sell at the low price points that are demanded by the market. And so data-driven manufacturing really gives you that infrastructure in place to not only collect that data, but actually learn from it and make it useful specifically around ramping up these factories and ensuring the quality of the cells and devices going forward.
Puneet Sinha
Awesome. Hari, how do you look at it?
Hari Gopalakrishna
Yeah, great points. I’ll just add on a couple of things. One, I would say, so why data-driven manufacturing, right? So you want to accelerate innovation in the space. Of course, there is a significant amount of technology changes that are happening. Battery technologies are being developed. Processing and manufacturing is also being developed at the same time. So you want to have the ability to rapidly innovate, and to innovate faster. You want to have the data, right? You want to have the data from, let’s say, manufacturing, supply chain, R&D, all of this data to be synthesized to be able to rapidly bring the product. And also not just rapidly bringing the product, but also making sure the quality, to your point, is where you need it to be. Second one I would point out is around process optimization. You’re collecting large amounts of data from across the equipment. process, MES systems, and how do you really take advantage of this data, really apply advanced analytics, machine learning, et cetera, and how could we generate insights that are actionable? I’ll give you a good example. We have a customer that’s in the U.S. who is currently using a classic vision-based system for material defect detection. And the system was generating a number of false positives. and they decided to create a second AI-based secondary detection system that required about 15,000 or so images to be trained in the cloud, and the model was deployed at the edge. And that model, the challenge really wasn’t about the model, and the model was doing really well, but on a fast line, the decision had to be made between 400 and 800 milliseconds. And so the secondary decision had to happen within that 400 to 800 milliseconds. And that’s after the primary, right? And so both had to happen within that 800 milliseconds time frame. And so the result of this was that they had an improvement of 15%, which for a factory producing about 5.5 million cells a day, you can imagine the ROI. So doing this continuous process optimization, It’s a journey and you cannot do it without data.
Tal Sholklapper
Yeah, and I think one of the things to emphasize for those who are now entering the battery space, this is a continuous evolution. You know, batteries are, as one of our advisors describes, we worked at Panasonic, Quantumscape and others, leading manufacturing. It’s an unstable equilibrium. There’s always something trying to push you off the edges. So it’s not just getting that factory up and running, you know, day one. It’s continuing to have that, you know, run effectively and it’s new materials, it’s new processes. We are continuously tweaking and improving things in these lines and making sure that runs stably is really key in that without data-driven tools, it really is a much slower and more costly process.
Puneet Sinha
Right, right. So at this point, I want to, for our audience benefit, I want to show a simple schematic of how the three of us are coming together. It’s a simple schematic, but, and I would love to hear, and I would love to hear, here your perspective, but let me take a crack at it. So at the very bottom, you see your actual factory and your whole production line. As Hari and Tal also mentioned, if you’re just doing things relying on your machine builders or experience of your personnel, it is a very cumbersome process. What we are doing is we are putting few layers of how you can build the right tech stack to do the things in a fashion and a scale that you want to do at. And it is, you can do it at the pilot scale, you can do it at giga scale. So there are three key layers of it. One is creating a digital twin of your manufacturing. That can be for the whole production line, can be for each of the machines, having the execution with MES, but also how you connect all the machines together. And with that, you are collecting the right data. and the data then the middle layer becomes very, very critical, that is to have the right backbone of how you’re collecting the data. And then once you have the data, you need to contextualize it, and the top layer is critical, but that only works when you have the bottom two layers, and that’s what we call battery-specific intelligence based on the work. From Siemens, Volt-Ike has enterprise battery intelligence. AWS brings those AI services. So you make decisions. First, you understand the data, what the data is telling about the things that are happening at a microscopic level inside a cell and then make the right decisions of how you need to go and tweak machines operations or something about material and feed that information back. So AWS also provides that backbone to make all of this happen. Anything more you guys want to add to this?
Hari Gopalakrishna
I’ll give an example to make this real. We’re doing, we actually have in production, implemented a solution for a customer that has needs around training that was taking almost about nine months to train new employees. What we did was train, essentially bring all of the data from all of the different systems of records like MES and systems, for example, like your standard operating procedures, your data from your operating, you know, your manufacturing floor, PLM systems, logs, data from, for example, sound data, video data. So structured and unstructured data, bring all of this in to train an LLM. And that LLM was essentially cutting down that training time from nine months to just, you know, just a few months, like significantly cutting down that time. So this, you know, especially in an industry that is struggling with, you know, labor shortage and technical skill shortage. These kinds of solutions can be, you know, can be the top, you know, that takes advantage of all of these different layers and all of the different functionality that you’re seeking in different different levels to come to life at the top.
Tal Sholklapper
Now, and if you look at this ecosystem, it sort of makes sense, but it’s one of those things that’s trivialized as you go to a lot of the new factories as they’re being rolled out. It’s, you know, we haven’t done this before. We’ve got to get the, the land. the land, we’ve got to get the equipment. This is sort of an afterthought. We’ll like put it all together later. And we’re seeing the repercussions of that at a lot of the factories and facilities that are out there that are struggling to scale now. And it is not trivial to, you know, network your equipment to get the SKADA up and running, to get the MES, to get the data into analytics infrastructure so you can learn from it. And really what we’re trying to do here is provide, you know, really a reference architecture. There’s always going to be a little bit of customization, configuration, different tools you bring in, but really have this reference architecture that we could take to customers and help you get up and running very quickly rather than have to take years, struggling to piece these things together. As you’ve already hired 1,000 engineers, you’re putting material through the equipment, and, you know, one of our customers talk through, it’s a relatively small factory, and for each year of these delays, they’re having, you know, it’s $250 million. It’s even for a tiny facility. And so the opportunity here to have this, packaged up and ready to go as you’re starting to produce is really important.
Nick Finberg
This seems like a good spot to pause the conversation and take a break. Thank you, Tal, Hari, and Puneet. And thanks to the audience for tuning in to this episode of the Battery Podcast. We will be back soon to talk about the structure or business architectures that help make data-driven manufacturing possible for the battery industry. Make sure to subscribe so you don’t miss future episodes and until then, be sure to check out our website siemens.com/battery for more information across the entire industry.
