Manufacturing is an integral and huge part of the economy and plays an essential role in accelerating progress toward the UN Sustainable Development Goals (SDGs). With the rapid development of information technology and the continuous deepening of the digital transformation process, AI is gradually applied to the whole lifecycle of manufacturing: AI in supply chain management, AI and the smart factory, and AI in the product lifecycle management. This trend is expected to accelerate.


Event page: “AI for Manufacturing – Breaking Beliefs – Dalith Steiger in AI for Good


Together with the United Nations Industrial Development Organization (UNIDO), ITU launched the AI for Manufacturing series. A kick-off event set the stage for a series which brings together leading voices from academia, industry and policy to discuss the latest advances of AI as applied to manufacturing, drawing from a variety of techniques such as modeling and simulation, digital twin, blockchain, 5G, and edge computing.

Listen to SwissCognitive co-founder Dalith Steiger’s presentation on “Breaking Beliefs in Manufacturing” in the “AI for Manufacturing” series first episode. Find the transcript below.


01:01:55 – Dalith: “Thanks a lot for this warm welcome, great audience and distinguished cool speakers. It was a very interesting journey until now, and I will be very short and brief. I want to deep dive a little bit into some use cases in manufacturing. We’re actually standing in the middle of industry 4.0, especially with manufacturing. And we do have great potential there. We’re talking about AI, but we’re also talking about IoT, and if we are marrying these two, we’re talking about the artificial intelligence of things. That is actually the next step, which is already implemented in different use cases. We can unlock the potential of these technologies in various industries. And one of the things that are always in the middle is the human being. How can we support and augment our capabilities, and make our life healthier and especially safer? What is a big issue in manufacturing in addition to that? Obviously, we always have the sustainability development goals in our minds. How can AI for good be implemented? So let me switch to my presentation.”

“We can unlock the potential of these technologies in various industries. And one of the things that are always in the middle is the human being.”

Thank you for reading this post, don't forget to subscribe to our AI NAVIGATOR!


01:03:20 – Dalith: “We actually talked about the title, and for me, it was like breaking beliefs in manufacturing. It’s not actually only in manufacturing. We need to break beliefs when we start to implement such technologies. So the potential, as mentioned, is huge. We do have technology on one hand; we do have the potential to marry these technologies together on the other hand. And we’ve heard from our previous speakers, that technology, especially cognitive technology is helping us to be more creative. AI can actually develop ideas that a human being would never come up with. And this is very important. This is where we also have to merge technology with a human being, what we’ve heard quite a lot today already. So it’s not only AI that drives innovation, it’s actually a lot about data. We have data, we have increasing computer power. We have the algorithms and we have the connectivity. Especially connectivity is one of the things that in manufacturing is essential.

01:04:39 – Dalith: “Let me just go through the various products and systems that we have. We start with just one product, and we can actually put some intelligence and some censoring into it, and then we can connect it to an ecosystem. And this is where we are actually going if we are building a system of systems. This is where the world is being more connected. Human beings are more connected and therefore devices and also society is getting more connected. So why do I say that we need to break beliefs? One of the most obvious use cases for me is actually coming from additive manufacturing: laser centring, and 3D printing. If you think about how you build a machine, how you build a turbine, you have obviously some physical boundaries on one hand, and also the boundaries that we had, how you can produce things, how we were used to producing it.”

01:05:58 – Dalith: “Now, if you are an engineer and there is a way how you learned it, it is going to be very difficult for you to imagine that suddenly with additive, laser centring, you can actually manufacture a turbine with holes in it. So this means you have to have a different way of thinking, a reengineering. We always say, we have to somehow unlearn to learn new things. We need to break beliefs and start doing new things. Let me skip through four use cases. Very simple. We had this information before about how robots can actually support human beings. And here it is very simple. It’s like actually the robot who can take over a repetitive task, something that is not really interesting for the human being, he can do it, or she can do it 7/24. And also the whole process is much more robust. We’re saving time, and we can also be more efficient. And with various kinds of sensors, we can also make sure that the temperature, as well as the water that we need for watering, is more effective where we are saving resources.”

01:07:22 Dalith: “The next one is how AI enabled supply chain. Most of you are very much aware, especially in manufacturing. The supply chain is one of the most critical things that we have today, starting with the COVID and now with the war. How can AI actually support the supply chain? How can we make it better?
One of the things that today we have access to is real time data. We have that huge possibility to reduce costs, be more efficient, to find out where is the risk. Where can I get certain things? Where do I have to adapt my process? Perhaps I need to do various things differently because something is not deliverable. I would like to read you quickly some numbers: 83% of large industrial companies believe AI produces better results. Just 20% have adopted it with this uncertainty around costs, counting for much of these reservations.”

01:08:29 – Dalith: “So if we’re thinking of how much we could actually save money and not only money but also resources like gasoline, energy and support the sustainable goals. The potential is huge, but we are going to discuss more during the panel about that.
So the next one is AI in automobiles. Also there, we know that we have a huge potential how we can invent the whole automotive world. We’re starting from the design or from material. We have a lot of new materials that we can actually design or manufacture with the technology because we are getting to new ideas. It’s about the possibility, and how new materials can support it. It’s also about predictive maintenance. With sensors, we know whether a car may have a problem. And also after status, we know everything regarding insurance. We can share data, reduce risk, and send alerts.”

01:09:49 Dalith: “The main information here is really that supply chain and manufacturing have a huge potential when they can use the real-time data. So for me, what is really important, and we’ve heard that from our previous speakers also, the interaction. Where is the connection between humans and machines? When do we do things with machines or algorithms? When are we doing it by ourselves? It’s a co-existence. It’s a co-intelligence. So these are some of the questions that trigger us that we think we need to discuss. And at the end of the day, for us, one of the most important thing is that we really need to shift from technology literal people, to people literal technology. We need to find a way how we can implement the technology to our best for the human being, as well as for the sustainable development goals. So therefore we need speed, and people, like all of us, all of you, to make sure that we can learn together and make the world better. Thanks.”