Designing in the dark: How AI brings clarity to sustainable product development – Transcript
In this episode of The Industry Forward Podcast, Eryn Devola and Neil D’Souza explore how AI and digital technology can help companies shift left and gain earlier insights into designing sustainable products.
Sonya Sauve: Hello, and welcome to the Industry Forward podcast, where we explore key trends, transformative technologies, and real-world innovations that are reshaping fields from aerospace, industrial machinery, and semiconductor to pharmaceuticals and beyond. I’m Sonya, your host for today’s episode, “Designing in the dark: How AI brings clarity to sustainable product development in uncertain times.” And I’m excited to welcome Eryn Devola and Neil D’Souza to the Industry Forward podcast. Before we begin talking about sustainability within product development, could you please introduce yourself to the podcast listeners? Eryn, do you want to start first?
Eryn Devola: Hey, Sonya, my name is Eryn Devola, and I lead the sustainability efforts at Siemens Digital Industries. And what this really means is I get to work with really exciting technologies and help customers really reduce the footprint and increase the handprint of the products that they put into the market.
Sonya Sauve: Thank you. Neil, could you please introduce yourself?
Neil D’Souza: Absolutely. Thank you for having me on. My name is Neil, and I’m CEO and founder of Makersite. I’ve spent almost my entire career in the field of sustainability, helping companies understand product supply chains and make them better, and look at these supply chains in the perspective of cost, compliance, risk, and the environment, and what are the trade-offs one would need to make these KPIs better than they were before. And I’m really excited to be here.
Sonya Sauve: Me too. I can’t wait to talk about this. So let’s get into it. So first question for both of you is, as consumers, from your perspective, what makes a product a good product?
Neil D’Souza: I think a good product is one that’s successful, one that customers love and want to pay money for, right? And one that solves the problem, I think. Sometimes a good product is green because that’s what customers want at any price. Sometimes it’s cheap. Sometimes it’s just there when you need it. And I think designing products need to understand the trade-offs one has to make when choosing between these and many other criteria, right? How performant it is compared to how much it costs, how much harm it causes the environment compared to how easily available it is to us, or the cost angle to it. So I think the successful product is one that makes the best trade-offs in order for customers to pay money for it and be happy with that product so they come back for it.
Sonya Sauve: That’s a really good point. So I guess the question then becomes, how do you balance all of these things?
Eryn Devola: And I think it’s a little bit about intention and making sure that how you’re looking at these requirements and being really intentional about the choices you’re making. And that becomes sometimes complicated if you can’t see what those implications are of those decisions, if you don’t understand the trade-offs you’re making when you make those trade-offs that Neil was talking about. And so it’s one of those kind of continuously complex problems that technology has really helped us solve over time.
I don’t want to date myself to here, but I think about when I started as a design engineer, we were looking at performance, we were looking at size, looking at requirements for features, and we had started to look at requirements for manufacturing or requirements for serviceability. But now we need to look a lot broader and put into that equation so many more requirements and the complexity of the problem. just gets really, really big. And I think those successful products are those that are able to deliver with intention and mask that complexity of everything that’s going on behind them.
Neil D’Souza: To add to that, right, is this becomes extremely hard if you are designing without the context of what its impact is going to be later on cost or on environment or on risk and safety and so on. And if you’re not taking this into account upfront, it means you’re making the assumption that some of these products will fail in the market. And today, in these uncertain times, I think one of the most problematic things is not how much it costs to make a product, but it’s will this product be successful or not, right?
Failure of a product on the market is one of the most catastrophic things a company can face in a situation where everything is uncertain, right? There’s competition that will eat your pie if you’re not there to win it, right? And I think that’s the, there’s a trade-off in terms of how do you optimize a successful, for a successful product, and there is also a trade-off in terms of when you do this, because the risk that you embed into that design process only increases the longer you wait.
Sonya Sauve: Neil, during our preparation call, you referred to this process as designing in the dark. Could you give us more information on what exactly that means?
Neil D’Souza: It’s actually twofold. Designing in the dark, I refer to it as when you design without understanding what the implications of the design are going to mean later on, right? Let’s make a product and not understand where or how it’s going to be used, and therefore what environmental impact you’re going to have, or designing with a certain material in mind, but not understanding what its cost impact is going to be. And this is a state that many engineers will be in when they’re designing. I think this is one aspect of designing in the dark.
And it’s, of course, very, very expensive if you continue to do this throughout the design process. But the other angle to designing in the dark is, well, there isn’t an alternative at the early design stage. You are in the dark. You’re actually starting from a clean slate, and there isn’t a lot of information that you have to be able to make those decisions. And I guess the goal of any process is to ensure that we spread light as we move forward, right? Then shed light on the path in front of us. And using the right tools that present us with this information at every step of the way with increasing intensity is what creates a successful process and therefore a successful product.
Sonya Sauve: So then the question I guess at this point is, what does that mean, the right tool? What does this solution help you do exactly?
Neil D’Souza: Imagine an engineer that has just finished a CAD drawing, right, or an electromechanical component. and would like to understand what is the environmental impact associated with this particular product. There’s no other information available, right? There are some components that they’ve designed in the past and is available in their PLM system. There are some materials that they’ve used in the past, and we also have access to this information. And now they’ve come up with this new design and want to understand what the impact is.
Imagine clicking a button and getting a result, right? There’s no complexity, there’s no expert, there’s no import-export, there is no changing context. It is available to them right in the system that they’re working in. Now, imagine they found a problem, they found an opportunity to… improve performance. They go back, they change the design, and they say, okay, now what does it look like? Again, click a button and get a result. And you do this iteratively, and you do this not just with one dimension, but all of the different dimensions that are important for the success of the product, and understand what the trade-offs are with every change that you make.
Sonya Sauve: So then the question becomes, how do companies do this exactly, right? How do they make sure they embed the sustainability early enough in that design process.
Eryn Devola: I think it’s really, for us, a little bit about getting more information at the beginning, trying to begin with a clearer picture of that end in mind and knowing that end has some uncertainty. So being able to model different options, being able to look at what alternate futures or alternate applications could even look like for those products. I think that’s the big challenge, and that’s the light that needs to go on, which is that you need to be able to have a clearer picture of the end so you can bring that intention into the beginning.
Sonya Sauve: And how do you do that?
Neil D’Souza: It is one of the hardest things, right? The earlier you are, and I think this is this conundrum, the earlier you are in the design process, the more control you have of what happens next, but also the less information you have about how to influence that decision. And so what you’re trying to do is, at least in its first set of these set of problems, is you want to make the best judgment at any given point in time. The second thing is you want to make sure that these decisions are tracked. As information improves and evolves, you want to make sure why you’re here in the first place.
And so this digital thread or this digital twin of information needs to be maintained throughout this design process. This is also important because when you take a product to market, you cannot really rely on the information that you used in the early stages. When you didn’t know where you’re going to manufacture, you didn’t know what material grade you’re going to use, you didn’t know how you were going to manufacture it, you didn’t know what kind of disassembly you were going to think about. what kind of downstream supply chains you needed to use, what kind of upstream supply chains you needed to use. These are things you don’t know in the early design phases. But when you take this product to market, you need to know this and you will need to declare this.
The other aspect of this is engineers hate shift switching context. It is, sure, let me give you a tool that you can do all of this stuff and it does all of the work for you. I have been in this space for 20 years and it has never worked. You’ve always had specialists that use their tools. In terms of engineering, it is CAD, PLM. This is where they live. I think these analyses need to be brought together into the world of PLM and CAD. This is my contention, that if you present this information to engineers in this way, the digital thread of information, supporting designs from very early stages all the way until we’re ready to manufacture. And do this within the PLM environment. I think that’s how you get change. That’s how you get adoption.
Sonya Sauve: But engineers may not be sustainability experts. So that creates, I guess, another constraint into embedding that early in the design process. How do you see that happening or changing?
Eryn Devola: Maybe the perspective from my side is that we see a lot of people getting really concerned about the environmental aspects of their products once it’s already been designed, once the parts have been sourced, and we’ve already bought all the equipment and we’re about ready to start manufacturing it. And the truth is, it’s really too late to have a substantial impact on those sustainability metrics then. We need to shift those left. And as Neil was saying, I think we need to put that data, that information, that expertise into the context that engineer is familiar with.
So we need to embed it into those tools that they’re already using. We need to show them what the impact of a material choice is on the CO2 usage of that product or on the energy efficiency of that product long term. And bringing those two things together, the knowledge, and pushing the culture a little bit, we’re moving from designing in the dark to at least turning the dimmer switch on a bit. And as Neil rightly said, that information is a little bit fuzzy at the beginning, but as you move through your engineering process, as the choices you make become more defined, you can see those impacts come much more into focus, going from something that feels maybe based on a catalog value to an actual value you’re getting from a vendor.
And this to me is where the magic happens. In shifting left, the management of these sustainability impacts and doing that through empowering our engineering community with the right tooling to do that in their context and building on the expertise that they already have. And this is where the life cycle analysis or LCA tools that we have are so important. And this is where the partnership we’ve got between Makersite and Siemens really brings that together. They bring that subject matter expertise, all of that container of experts with the power of AI to the PLM community, to the team center environment, fully integrating that early so they can see these impacts way earlier in the process and start turning that dimmer switch brighter and brighter earlier and earlier in the process.
Neil D’Souza: Sustainability will only become, will only be adopted when it’s no longer an art. but it becomes a science, right? At the end, you need to be able to reduce it to a number. Engineers can work with numbers. That’s where the hard part is, right? How do you take something that is so abstract, a data set for steel, what does that mean, right? What is the supply chain for aluminum sheet mean to an engineer?
And this is where AI comes in. So I do not think you can use traditional approaches of mapping data sets, because that’s where expertise comes in, knowledge in the field of LCA comes in, methodologies of how you should be calculating these things. AI and modern technology can help you solve this problem and remove some of that complexity away from an engineer. And I think this is where Makersite and Teamcenter Sustainability come in because we bring that AI into the PLM world so that it simplifies this process for an engineer where they don’t need to care about all of the methodology, all of the LCA that sits behind it. We give you the number so you can make that decision.
Sonya Sauve: Thank you both for such a thought-provoking discussion on how shifting left and AI can shed some light instead of designing in the dark. Hopefully, we’ll have you back on the podcast sometime soon for some great conversations. Thank you also to our listeners. We hope you enjoyed today’s discussion and that you join us again soon for more episodes on the exciting technologies and trends reshaping today’s industries. This has been Sonya, and we hope to see you next time on the Industry Forward Podcast.
Siemens Digital Industries Software helps organizations of all sizes digitally transform using software, hardware and services from the Siemens Xcelerator business platform. Siemens’ software and the comprehensive digital twin enable companies to optimize their design, engineering and manufacturing processes to turn today’s ideas into the sustainable products of the future. From chips to entire systems, from product to process, across all industries. Siemens Digital Industries Software – Accelerating transformation.
