Keeping up with Smart Factories / DAVOS 2025

Summary

This panel discussion focused on the advancement of smart factories and the impact of AI and automation on manufacturing. The conversation brought together leaders from government, industry, and technology sectors to explore the challenges and opportunities in this rapidly evolving field.

Key themes included the importance of digital transformation in manufacturing, the role of AI in enhancing productivity and innovation, and the need for workforce upskilling. Panelists shared examples of how their organizations are implementing smart factory technologies, such as digital twins, industrial co-pilots, and AI-driven quality control systems. They emphasized the potential for these technologies to improve efficiency, reduce costs, and drive sustainability efforts.

The discussion highlighted the critical role of government policies in supporting industrial transformation, with Singapore’s initiatives in workforce development and industry partnerships cited as an example. Panelists stressed the importance of change management and addressing workers’ concerns about job displacement. They argued that while AI and automation may eliminate some jobs, they also create new opportunities and enhance human potential.

The conversation also touched on the challenges of scaling smart factory technologies, particularly for small and medium-sized enterprises and in legacy manufacturing facilities. Panelists discussed strategies for making these technologies more accessible and easier to implement, including the development of plug-and-play solutions and leveraging ecosystem partners.

Overall, the panel conveyed a sense of optimism about the future of smart manufacturing, while acknowledging the need for continued innovation, collaboration, and investment in human capital to fully realize its potential.

Keypoints

Major discussion points:

– The role of AI and automation in transforming manufacturing into “smart factories”

– Challenges and strategies for workforce training and upskilling as manufacturing evolves

– Balancing productivity gains with job creation and human-machine collaboration

– Scaling smart factory technologies across industries and to smaller companies

– Attracting talent and creating “smart jobs” in the manufacturing sector

Overall purpose/goal:

The discussion aimed to explore how smart manufacturing and AI technologies are reshaping factories, and to examine the challenges and opportunities this presents for companies, workers, and policymakers.

Tone:

The overall tone was optimistic and forward-looking. Panelists were enthusiastic about the potential of smart factory technologies, while also acknowledging challenges. The tone became more practical and solution-oriented when discussing workforce training and scaling issues. There was a collaborative spirit, with panelists building on each other’s points and sharing insights across industries.

Speakers

– Tian Wei: Host from CGTN, Moderator

– Roland Busch: President and CEO of Siemens

– Padraig McDonnell: Chief Executive Officer of Agilent

– Gan Kim Yong: Deputy Prime Minister of Singapore

– Anish Shah: Group CEO and Managing Director of Mahindra Group

– Stephanie Pullings Hart: Executive Vice President and Global Head of Operations at Nestle

Additional speakers:

– Greg McDonald: Chief Executive Officer of Agilent (mentioned but did not speak)

– Audience members who asked questions (names not provided for all):

– Shanti Raghavan: Schwab social innovator from India

– J. Liam: Professor, Director of Industrial AI Center at University of Maryland

– Isabelle Hartung: Multi-board member

– Liz Reynolds: From MIT

– Bahia Jafar: From Kuwait

– Marwan Shikarji: From MKS Pump

Smart Factories: Transforming Manufacturing Through AI and Automation

This panel discussion, moderated by Tian Wei from CGTN, brought together leaders from government, industry, and technology sectors to explore the advancement of smart factories and the impact of AI and automation on manufacturing. The conversation delved into the challenges and opportunities presented by this rapidly evolving field, with a focus on digital transformation, workforce development, and scaling smart factory technologies.

Implementation of Smart Factory Technologies

The panellists shared insights on how their organisations are implementing smart factory technologies to drive efficiency, reduce costs, and promote sustainability. Roland Busch, President and CEO of Siemens, highlighted the use of digital twins, AI-powered optimisation, and the industrial metaverse in factories, mentioning partnerships with NVIDIA and Microsoft. Padraig McDonnell, CEO of Agilent, discussed the concept of ‘lighthouse factories’ that significantly improve productivity and sustainability. Anish Shah, Group CEO of Mahindra Group, explained how AI applications such as agility.ai, quality.ai, uptime.ai, energy.ai, and connected.ai are enhancing quality control and efficiency in auto manufacturing. Stephanie Pullings Hart, Executive Vice President at Nestlé, emphasised the role of digital tools in enabling predictive analytics and workforce improvements, noting that data is becoming the “currency of the future.”

The discussion underscored the critical role of government policies in supporting industrial transformation. Gan Kim Yong, Deputy Prime Minister of Singapore, shared examples of government initiatives aimed at facilitating workforce development and fostering industry partnerships to accelerate smart manufacturing adoption, including the Skills Future Singapore program and company training committees.

Workforce Transformation and Skills Development

A key theme throughout the discussion was the importance of workforce transformation and skills development in the context of smart factories. All speakers agreed on the critical need for training and reskilling programmes to support the transition to smart factories and address potential job displacement concerns.

Gan Kim Yong highlighted government-led programmes for worker reskilling and training, while Roland Busch emphasised the need to reduce anxiety about job losses from automation. Padraig McDonnell pointed out the opportunities for workers to learn new skills with smart technologies. Anish Shah stressed the importance of purpose-driven organisations in workforce transition and provided historical context on job losses in manufacturing due to robotics. Stephanie Pullings Hart suggested pairing younger and older workers to bridge technology gaps.

The panellists argued that while AI and automation may eliminate some jobs, they also create new opportunities and enhance human potential. They stressed the importance of change management and addressing workers’ concerns about job displacement as crucial aspects of successful smart factory implementation.

Scaling and Accelerating Smart Factory Adoption

The conversation also touched on the challenges of scaling smart factory technologies, particularly for small and medium-sized enterprises and in legacy manufacturing facilities. Panellists discussed strategies for making these technologies more accessible and easier to implement.

Roland Busch emphasised the need to make technologies easy to use and ‘plug-and-play’ for wider adoption. Stephanie Pullings Hart suggested focusing on incremental benefits when upgrading legacy facilities. Gan Kim Yong highlighted the importance of creating ‘smart jobs’ to attract talent to manufacturing. Anish Shah proposed using data and AI to upgrade existing equipment cost-effectively, while Padraig McDonnell stressed the need for clear leadership vision to drive transformation.

The panellists agreed on the importance of making smart factory technologies accessible and scalable, particularly for smaller companies and legacy facilities, through government support, easy-to-use solutions, and strategic implementation approaches. They also discussed the potential for smart factories to bring innovation closer to customers and help address labor shortages in some parts of the world.

Thought-Provoking Insights

Several comments from the panellists provided particularly thought-provoking insights. Roland Busch framed the discussion by outlining key challenges that smart factories aim to address, including climate change, resource scarcity, and labour shortages. Padraig McDonnell shared a concrete example of how AI reduced system design time from eight days to two days, doubling output and creating learning opportunities in the factory.

Stephanie Pullings Hart highlighted the crucial aspect of change management in digital transformation, broadening the discussion to include the human aspect of smart factory implementation. Anish Shah provided a real-world example of how smart manufacturing led to competitive advantages in the electric vehicle market, challenging assumptions about cost and quality trade-offs.

Gan Kim Yong broadened the perspective on implementing smart factories, emphasising that it’s not just about financial resources but also about vision and determination. He encouraged a more nuanced discussion about how different countries and companies can approach smart factory implementation, regardless of their current resources.

Unresolved Issues and Future Considerations

While the discussion was largely optimistic about the potential of smart manufacturing, several unresolved issues and areas for future consideration emerged. These included:

1. How to effectively upgrade legacy manufacturing facilities to smart factories

2. Strategies for attracting top talent to manufacturing careers

3. Ensuring safety and disability inclusion in smart factories

4. Addressing potential job losses from increased automation

5. The importance of data accuracy and interpretation in smart factory implementation

The panel addressed audience questions on these topics, particularly regarding safety, disability inclusion, and potential failures in smart factory implementation. They suggested some potential compromises and solutions, such as focusing on incremental upgrades for legacy facilities, using ecosystem integrators to help smaller companies adopt smart factory technologies, and pairing younger and older workers to bridge technology gaps.

Conclusion

Overall, the panel conveyed a sense of optimism about the future of smart manufacturing, while acknowledging the need for continued innovation, collaboration, and investment in human capital to fully realise its potential. The discussion highlighted the transformative power of AI and automation in manufacturing, the critical importance of workforce development, and the need for scalable and accessible smart factory solutions. As the manufacturing sector continues to evolve, it is clear that addressing these challenges and opportunities will require concerted efforts from government, industry, and technology leaders alike.

Tian Wei: Good morning, ladies and gentlemen, thank you so much for joining this session, Keeping Up with Smart Factories. Well from the NamUs itself, one could already tell the fascinating discussion we are to have. The Lighthouse Factories initiative coming from the World Economic Forum, as you know, are so well known over the years. Meanwhile, AI generation technologies in a way has been putting much more momentum to the overall process. So let’s go more in details about how companies are working with it and what kind of impact will that create? What does it take for this transformation? My name is Tian Wei, I’m a host coming from CGTN, it’s such a pleasure to be the moderator of this panel. But more than happy and honoured for me is to introduce the great panellists for this discussion. And I will go from this order, yes, Mr Gan Kim Yong, Deputy Prime Minister from Singapore. Thank you. Mr Deputy Prime Minister, what a pleasure to see you. Thank you, my pleasure. Mr Roland Bush, President and CEO of Siemens, good to see you, sir, again. And Mr Greg McDonald, Chief Executive Officer of Agilent. That’s me. Yes, I will, over there, right in here, yes. And also Stephanie Pullings-Hart, Executive Vice President of the Global Head for Operations with Nestle, over there. And last on this list, but not least, is Anish Shah, Group CEO and Managing Director of Mahindra Group. What a pleasure, thank you so much for joining us. Now, let’s go directly into the conversation. Time is very limited for such a grand topic. So policy, that’s really extremely important. Mr. Deputy Prime Minister, Singapore has been playing a key role in the region and beyond over the past few years in terms of smart manufacturing. Tell us more about what are some of the best experience you have acquired, and yet do you think there are several areas that can still be improved, not only with your regional partner, but also international partners, please.

Gan Kim Yong: Thank you very much. Thanks for inviting me to this dialogue. I think it’s a very interesting, exciting topic to talk about smart factories and how do we move forward, particularly with our manufacturing sector. Manufacturing has been a key component of our economy in Singapore, and we partner many of the major MNCs as well as the local companies to grow the manufacturing sector. But the manufacturing sector has to continue to transform itself to make sure that it is continue to be in touch with the latest technology, but at the same time continue to improve productivity and value add. And smart factory plays a very important role in this aspect. And while policies are important, it is also very important to ensure that the policies are aligned with the interests of the companies and the businesses. And therefore, one of the key driver of transformation of this smart factory and adaptation of AI and automation is really to be driven by the companies themselves. So the policies provide support and the necessary measures to allow that to happen. But the companies and the businesses must take the lead in the transformation of the manufacturing facilities. And in Singapore, we do it in two ways. First, we work with individual companies, big and small, to help them, to enable them, to support them, provide incentives for them to invest in automation, invest in transformation, invest in AI. But we also recognize that many of the smaller companies, MSMEs, they do not have enough resources to be able to tap. into some of these latest technologies and automation and AI capability. And therefore, we also work with sectoral leaders to set up sectoral-based centres of excellence for manufacturing, so that these smaller companies will be able to tap on these larger facilities on a shared basis, so that they can also have access to the latest technology in automation and AI. This way, we allow the entire manufacturing, big or small, to be able to embark on this transformation journey. The bigger ones will be able to do it themselves, within themselves, to set up centres of excellence for manufacturing, automation, AI, within the company. The smaller ones will be able to tap on the sectoral-based, broad-based shared facilities for automation and AI. And these are the sectoral centres of excellence. Manufacturing, for example, we just announced the AI Centre of Excellence for Manufacturing. So we want to encourage our manufacturers, the smaller ones particularly, to tap on these resources so that they can embark on this AI transformation journey. And once they are more familiar, they are able to develop their own capability, they are free to go on their own and set up their own company-based centre of excellence. So this is how we, through policy, we help to support both large companies as well as small companies in the manufacturing to embark on this journey. Thank you, Mr Deputy Prime Minister.

Tian Wei: Not only the content of your answer, but also the speed of your speaking seemed to give a lot of consideration to your other panellists. That’s right. So that they will also have a sufficient time. Because you keep reminding me that time is short, so I have to say what I can in the shortest possible time. You know, this is the role of the policy makers, to serve, to provide service. Let’s go to Mr Bush. We heard from the policy makers, but what about from our business players, especially on the ground, how on the floor of the factories, these frontier technologies are really translating into real smart manufacturing. Mr Bush.

Roland Busch: Yeah, very good point and let me you don’t have to talk as fast So, um, let me let me let me target a little bit backwards I mean the question is and what do we want to solve and what we want to solve? We want to solve the big challenges, which is not only climate change scarce resources, but it’s also labor shortage Shortage of skilled labor. We want to produce localization that you have more smaller factories smaller lot size Shorter cycle times so that that’s the idea and that’s a lot of technologies which behind which are serving these frontier technologies And in order to to frame it, I just Define it from the example of the industrial metaverse what it is. It’s a photo holistic physics based real-time representation of the real world Imagine that you have a plant that everything what you what you see there You have in a digital world including the people working on the shop floor and everything real-time photo mystic and What we need now we’re coming to this point of these thick frontier technologies We definitely need a digital twin physics based digital twin which behaves like the real world So example has been in a in a gaming world when you move a robot very fast It’s perfect in the real world. It starts winging. So you need physics based Otherwise, you cannot really make this real second so digital twins secondly You need all the hardware which run the software needs to be software defined as a certain software It has to be connected your sensors your devices Whatever you have talk about software defined hardware software and automation and the last element guess what it is It’s data and AI AI is supercharging what we do on the shop floor how we built this technology stack and this enables Completely new ways of how you really transform your manufacturing on the shop floor last element and you said it partners Even though Siemens spent six and a half billion on R&D and we have a strong portfolio. It’s not good enough We need strong partners and they give us three examples how that looks like We have a couple of VEF lighthouse factories, one in Erlangen, for example, where we are pushing the limit to what we call this industrial metaverse. You see a robot alley. You see a lot of any kind of automation devices, whatever we do. We have a digital twin of some cells. Not the whole plant yet, but we get there. What are the benefits? 70% higher productivity, 40% less energy cost, and what’s very important, 40% lower time to market. So it’s really amazing how we speed up. Partner, NVIDIA, because if you want to do something photorealistic, you better get a strong partner with you, and Omniverse, we do that. Second example, industrial co-pilot and related agents. So we’re using Microsoft large language models, in that case, to build an industrial co-pilot in the programming phase. I think this is well known, many people do that, but also in the shop floor. So that means it’s basically an expert system which interests all the learnings which you have in the past, helping you if there’s a red light, a line stops. And even if you’re in a night shift, you don’t have an expert, you have a co-pilot, you can ask something, and it comes back and tells you, I tell you this problem, we had it a couple of times, this is a problem, how you solve it, and what to do. Here again, Microsoft is a partner. We have fleet customers, more than 100 in short time, which are tapping into that. The last example is low-code. We want to democratize the way how you use software. So you need low-code capabilities, we have a Mendix platform, and we are empowering and supercharging this low-code platform with AWS Bedrock offerings for any kind of large language models which they have in the offering, to really enable and supercharge any kind of low-code. Let me give you one last point. We do that for big customers, where we go with accounts in the automotive industry, but since you talk about it, how can you scale it and transform it? Don’t forget the small and medium-sized companies. There are a couple of hundred thousand. China or in India, there are 50,000, 100,000 in the United States, 40,000 in Italy. To democratize this technology, to scale it, you really need to make it easy to use, plug and play, and eventually sell it over marketplaces, where you need, again, a strong ecosystem

Tian Wei: to do that. I see the lights in your eyes. We are describing all these players putting together. Very exciting, obviously, that is the process going on right now. While you were talking, Mr. McDonald seemed to always nodding his head, because he had some similar experiences, talking about lighthouse factories. Yes.

Padraig McDonnell: We were the recipient of the award for two lighthouse factories, one in Shanghai and one in Penang. A lot of overlap with Siemens in a lot of the areas. One of the areas, large language models about how we can reduce the number of failures and see where the red lines are, particularly in Penang. One of the things that we’re seeing as well with AI as well, how we can train the workforce and make sure that we’re developing the workforce with these new skills, because the workforce is going to have to learn and develop, and of course, there’s new skills needed. Having real skills matrices, understanding what are our skills today and what do they need to be for the future is one of our big areas. Of course, with Signapore, we have a world-class factory there, where we’re working with cobots and digital twins, and working with talent development with the Signapore government about how we can continue to bring talent in. Last example in our lighthouse in Shanghai, where we produce our gas chromatographs, which are a cornerstone of the analytical lab. We can have a thousand customizations on these systems, you can imagine the complexity with that. AI has helped us to reduce the time from eight days to two days in designing the systems and getting them up and running, doubling the output, and I think it also creates a really learning opportunity in the factory as well.

Tian Wei: Well, you are coming from similar areas in terms of sector, but there’s one consumer product from Nestle. Ms. Hart, would you like to share with us as to how it works in your sector?

Stephanie Pullings Hart: particularly. No, I absolutely would love to and super super honored to be a part of this panel and you know when we look at where Nestle or how Nestle performs in the fast-moving consumer goods segment you know we’re in 188 countries we have 340 manufacturing facilities, 780 distribution facilities around the world and you know 270,000 total Nestle employees but 150,000 ish that are in that operations capacity so when we look at the unlocking capabilities from an artificial intelligence perspective or machine learning and digital tools the possibilities are endless but really building on my colleagues point here is you know this digital transformation requires an investment in capabilities for our workforce. We are on this very very robust journey and have great partners like Siemens, Aviva that we’re working with as well but one of the things that we were talking about in the green room when you do have a large number of employees or even if you have a small number of employees oftentimes we underestimate the impact of change management and it is one of the most critical aspects that we are focused on as an organization and that I encourage other people that are on the journey to also be focused on because it could actually pause and really hold back the the improvements that we that you could potentially see. The unlock for us as an organization obviously you know labor productivity is a no-brainer but it’s more around looking at predictive analytics and allowing this generation of employees that we have within the workforce because if you think about it we have people within our workforce that are extremely savvy so the newer generation that kind of came out of the womb doing this and knows how to do that and a very mature segment that can be a little uncomfortable and it can be a little a little intimidating so we’ve actually done a lot particularly in our markets in South America in bridging that gap and pairing new generation with the more mature I like to say mature like a fine wine, mature generations together to unlock the potential and capability. So I’m excited to see what this means for the consumer sector, but it requires the work of partners, of government, and of learning institutions. Yeah, that’s a

Tian Wei: whole set of fascinating questions as to how to bring everybody on board, not only within the company, but also in the market you are in. Mr. Shah, I know you also have fascinating examples to share with us. Would you like to do that right

Anish Shah: now? So let me start with the world around us, which is a very high level of uncertainty, especially in the auto industry. Thank you for reminding us. We went through uncertainty in supply chains, where semiconductors were not available, and we kept getting these messages from our suppliers that we can’t give you the keys for your car, or we can’t give you the wireless charging part for your car, or some other part is not available, and we can’t shut a factory down because one part is not available. We also have the complexity embedded in our plants, where we bought one plant with 22 production shops and 250 variants being made on a real-time basis, and that’s where technology has come in in a big way. AI has helped us simplify a lot of these, and I’m going to outline five areas of AI that we’ve actually used. One is what I call agility.ai, which is essentially the digital twin that Roland mentioned, that helps us reroute production based on something that has gone wrong, or based on something that has gone right. If there’s greater demand for a particular variant, how can we create more of that, and how can we solve for challenges that we face from a supply chain standpoint. Eventually, this is also going to help us do real-time production, where a consumer can order a particular vehicle with a certain set of features, a certain color, and we can show them on the production line, this is where you are, and then this is how your vehicle is being produced. The second one I call quality.ai. which is helping us on a number of aspects of quality. One is around weld integrity. Now, weld integrity typically had to be done by sort of destructing something and to check whether that is done well or not. There are a lot of parameters that go into understanding what a good weld is. And those parameters have been put into a model. We can use that model today to look at it and say, we don’t need to go back and destruct something or check for quality. We know right up front whether this is a good weld or not, and we can correct for it immediately. Linked to that also is paint shops. And we use our vision.ai capabilities to look at paint on a vehicle and make sure that it’s a right quality, there are no spots that are left out, and there’s no rework that’s required. And that also results in less use of paint, which saves us cost as well. The next one I look at is uptime.ai. And here in the past, we’ve had our history in the minds of people recorded in a sense on some platforms and people look at it, if a machine’s down, what do I do? Now we actually have a bot or an agent that walks through, here’s what you should do. And the bot has information fed in from machine manuals as well as our history to be able to reduce downtime, and we’ve been able to reduce downtime by about 10 to 15%, depending on which shop we’re operating in, with using this. The fourth one I look at is energy.ai. And how do we optimize energy in our plants? And there are, again, significant savings that we’ve gotten from doing this. We’re not complete on this as yet. This is still a little bit of work in progress. And the last one is connected.ai, which is how do we connect all our machines together? Again, Roland, something that you mentioned. Something a little more straightforward than some of the other ones. But the net from all of this is what are the outcomes? And does this allow us to look at investments in each space? What do we get? But more importantly, in the marketplace, how can we compete better? And we’ve just launched two Born Electric vehicles. And the questions we’re getting back from customers as well as our investors is how can you afford to do this at such a low price as compared to the quality you’re offering these vehicles? Are you making losses? Are you, is this, I just had a media interview this morning where I was being grilled on that saying, is this an introductory price? And I said, no, we actually have good margins on this. We just have better manufacturing tools that allow us to do it. So the outcomes are more important from that perspective.

Tian Wei: You have very considerate customers. Yeah.

Gan Kim Yong: Can I ask a question? Can I ask a question? Yeah, please. Maybe I can ask, Mr. Park. Mr. Deputy Prime Minister. How is it, would it be possible to see how we can allow your experience to be shared by other industries? Because you’ve done very well, have the five AI modules and it will be very useful, I think, for the world. And if you can share with some of the industry, have a platform to allow some of your knowledge and your application and your approach to be shared by other companies, maybe Nestle can learn something from how you apply AI or Siemens. In other words, cross-sector.

Stephanie Pullings Hart: Yeah. That’s right. And that’s actually what I was gonna add, Wei. Just as Mr. Hsiao was actually talking or speaking, the aspects that he’s highlighted around connectivity, around improvements from a sustainability, the whole aspect are exactly the areas that we’re focused on that we’re actually doing. We use different terminology. But what I was gonna highlight is, it doesn’t even matter which sector that we’re in, automotive, food, and consumer goods, the benefits and the opportunities that exist are really the same. And it’s driving consistent quality. It’s actually deeping into data or diving into data. And I think data or accurate data is the currency of the future. And how you understand and the individuals that you actually bring into the organization that know how to handle and interpret data and AI are the ones that are actually gonna be leading us to the future. See, Stephanie is very much a human-centered, very much how it works.

Tian Wei: But let me have Mr. Bush and then I go to Mr. McDonald, please. Can I just respond to this question, which is we are actually doing this today because

Anish Shah: a tech mind of business, which is in IT services, is actually bringing a lot of its customers to our shotgun plant for them to see what’s happening there. And part of what we are also struggling with, which I’ll be candid on, is that the fact that this is a competitive edge for us, because we feel that we can make products 30% cheaper than auto manufacturers around the world.

Tian Wei: And the question is… Which is all right, because don’t share it with other automotive. Share it with Nestle, which is not in the same business, and all will benefit, and share

Roland Busch: it with your suppliers, because you do want to level them up so that they are able to be competitive too. Absolutely right. We’re at this point where we come in, because this is how we make our living. I mean, we are not OEMs. I mean, we are tier one. With one exception, we build trains. This is one exception, and there’s a very good one, which we are scaling technology. We are scaling technology all the way from the machine builders, machine tooling, which you have all in your sites, and then we bring technology to… I mean, all verticals. Food and beverage, pharmaceutical, chemical, oil and gas, defense, aerospace, automotive. I mean, we call all these our customers, and this is the beauty about this, what I talked about before, platform-based technologies, that you have certain elements which scale across. So the machine learning algorithm, which allows us to predict within a lead time of five days to 10 days, which door of a train is going to fail and why. So we put it aside, repair it, and achieve almost 100% availability. The very same machine learning algorithm runs on a welding machine, and detects a wrong welding spot. The technology which allows us, for example, to optimize your machines when you fill a yogurt and you change your mixture. you have a lower fluidity, and you don’t want to spill. So we train it, rather than you stopping the line, we know exactly in simulating how that goes, so that you can just change your recipe and run it. The same algorithm runs in a chemical plant where you have bench processing. So this is the beauty about technology, and maybe one thing to add about the people. This is so important. The job description of our blue collar labor today is completely unlike the job description we have tomorrow. So that means when we are training with our industrial co-pilot, we want to democratize technology, to make it accessible via natural language, so people can program a robot with natural language. It is hard, it’s a way to go, because it should be an agent, that means you tell, and this thing does what you’re telling it to do. It’s a certain hardened technology, but then it really scales if you do it right, and you have to do it to the right people to use it on the shop floor.

Padraig McDonnell: Yeah, I think what you really have to remember is the why in all of this. We’re increasing productivity, we’re making it easier for workers to of course create new products, but it really brings us closer to the customer. It brings innovation closer to the customer, and it actually allows us to interact closer with the customer. And beforehand, sustainability and productivity were seen as maybe different things. AI is allowing those to merge in terms of our sustainability goals and productivity helping with that in terms of cost, et cetera. And when you think about the purpose of a company and the people in the company to work,

Tian Wei: aligning that purpose to the customer outcomes and using technology to bring these outcomes quicker I think is one of the key areas from a change management point of view. Yeah, I see everybody is having a great time on this panel. Either we have the topic right, or we just have the most fascinating panelists, or both. Or both. We’re gonna say both. But now I’m going to ask a little bit harder questions after having fun. So we are excited about this trend. But while we are excited about it, there are a lot of questions that is still unanswered. Even though we have already gradually find some answers, for example, every one of you already provided. What about the relationship between human and AI-generated technologies or robots, where the list goes on? Have you give some more sophisticated thinking about this link? Meanwhile, two to five years, technology are going to soar. Of course, with a lot of uncertainties, which Mr. Bush kindly remind us at the very beginning of his answer. But how do you see these technologies are really going, especially how would they intertwine during the process? I think I want to know more about that. Having said that though, how will policy accommodate while all these factors are keep on evolving? So Mr. Deputy Prime Minister, I give you, once again, a first opportunity. You can take your time in answering.

Gan Kim Yong: Thank you so much. I think this is very important. And then I go from the other side over here. This is a very important question. In fact, it has been a question that the Singapore government, I’m sure governments around the world, have been focusing on. How do we ensure the journey towards automation and AI will be beneficial, not to just a small segment of companies and workers, but broad-based, so that everyone can see the benefit of it? I think this is a very similar experience we’ve gone through in the early days when we have gone through the basic automation revolution. I think they will also impact on the workforce, workers and businesses. Some businesses have to transform. And therefore, this transition is very important. And how well we manage the transition will also determine how successful we will be at the end of the journey when we emerge from our transformation. So I think from a Singapore government point of view, we pay a lot of attention in managing the workforce transformation. And we develop, actually, industrial transformation maps for all the key sectors of industries in Singapore. And on top of that, within each industry transformation map, we also have developed manpower transformation strategies for each of the industry sectors to make sure that we are well prepared for the future as industry goes through a transformation journey. And one of the key focus in the manpower transformation map really is focusing on training and retraining and rescaling. And we introduced a scheme called Skills Future Singapore to allow Singapore workers to tap on the resources that government provides in terms of subsidy for the course fee of training, even in terms of a subsidy for their payroll, which some of them may have to take no-pay leave for a long period of time to go through formal, long form of education, to take another diploma or another degree so that they are able to change their career pathway to something that they’ve never learned before. So I think these trainings are very important and they are costly, but the government is prepared to invest in them. So we provide subsidy for the training courses, at the same time, provide support for their income if they have to leave their job or no-pay leave in order to pursue this training. But we think it is very important part of our journey towards this transformation. And therefore, I think we are emphasizing on this, but it’s also important for us to partner the industries very closely, my earlier point I mentioned in the beginning, that this transformation, these training courses and upgrading must be relevant to the industries. And therefore, we are very keen to partner our industries to embark on this training journey. And therefore, we also introduce a concept called company training committees. We support and subsidize companies to set up company training committees within the company to decide what kind of a training do you need so that the training you provide is relevant to the company’s needs. It’s so encouraging. We also know that some companies are not keen because they feel that once the workers are trained, they may leave the company and do something else. So, we also provide independent training and support for workers who want to change their pathway. So, I think training and transition management and involvement, engagement of a partnership with the companies is very important.

Tian Wei: Well, we all know Singapore is one of the most advanced economies in the world. You’ve got a lot of cash, sir. So, all these programs that you are establishing… It’s never enough. Yeah, never enough, that’s for sure. But, you know, what about, I will go to you on that, those questions as well. Being here, we have a very much diversity of economies coming from all over the world. So, what you’re talking about, are they applicable elsewhere? Have you ever thought about that question as a policymaker? Sir, very briefly before I go to the next. I think it is important.

Gan Kim Yong: Different countries will have different economies with different considerations, different priorities, and many countries actually have a lot of resources. Sometimes it’s not just about money, but about the concept, about the determination, about the focus on what you want to do. And this, in a way, if you look at it, it’s an investment in the future. It is not a cost that you write off. So, it’s something that’s important for companies around the world, governments around the world, to pay attention to training and upgrading, so that you are able to transform your workforce to be in line with the development and evolution of the industries. Right. And they have to do it fast. They have to do it fast. Mr Deputy Prime Minister talking about he has a lot of transformational maps. What about for you, Mr MacDonald? Are you having also a lot of maps? Yeah, lots of maps. And I think frontier technologies, when you think about it, it really is about accelerating and enhancing the human potential, the ability to learn going forward for two reasons.

Padraig McDonnell: It’s not only to increase productivity, but also to enhance innovation. And in one of our plans, when we’re using augmented reality with our workers and when we’re using advanced models, we actually see the attrition level drop to one third of what we see in the marketplace. Because we’re giving people opportunities to learn new skills, really allow us to bring out more complex products faster. And, you know, it’s going to really also help hyper-customization that’s going to be required

Tian Wei: by our customers. So I really think about it as an integrator for enhancing human potential. What about that human-machine collaboration look like? You know, not just for now, which you all described. What about two to five years, Mr. Busch?

Roland Busch: I think the first thing is, also in a broader context, to have to take the anxiety away from the people that this costs my job, and I cannot learn it. I mean, the young generation is easy to adapt, but the more mature ones, they need to learn into this technology. So I think people have to get used to having a colleague, a digital colleague, a digital agent. I mean, you have a lot of colleagues, and I have a new one, a digital one, and these guys are growing, and they are smart, but they will never, I mean, they will never take your job away, but they take your existing job away, but it creates a new one which makes a difference. So, I mean, and coming to your point, education and training, how you do that, how you interact, how you prompt, and taking the people along with you, we are also defining, I think, 30 different job profiles of where you are, what capabilities you have, and what you want you to learn. It could be a one-week training course, could be three or six months, and we have a problem of 80,000 people who are investing 400 million, more than 400 million euros in education and training of people. So therefore, and as you go along, and as you start appreciating that your work is just getting much easier, and you can do, and you can dedicate your work to what you are really good at, makes such a big difference. And maybe a last one is, coming to your point about can everybody afford it, the good news is also digitalization, because most of our trainings currently run digital, and we have a digital training platform, 100,000 offerings there, I mean, prompting courses, which God knows how many people already use, and we are opening this platform also to 30,000 people. third parties. It’s not that easy, but we are opening it so that we can scale it also to others and multiply it. So therefore, it’s a journey, but it’s worthwhile going that aspect because believe it or not, this turn is out of the station. I mean, AI will define the way how we go in the future. So you better adapt, we call it growth mindset, continue learning, give yourself into it, go over again, try something. If you fail, try it again, you will learn it. And that’s what I believe is the future.

Tian Wei: Mr. Shah earlier asked gently whether he should reveal all the business secrets to the others. So let me also ask you a question about future secrets. What about that two to five years, human machine collaboration? What is the secretive map you have in your mind already?

Anish Shah: So let me start first with a look at history because there’s a lot of noise around jobs being lost with AI. But in the manufacturing sector, in automotive in particular, most of the jobs are lost with robotics. If you look at what manufacturing plants were like 30 years ago and what they’re like today, you see a huge difference in terms of people on the shop floor versus not. So AI is going to change some things, but really not as much as what robotics has done. So I just want to provide that perspective first. So as we look forward, I think the key as both the Deputy Prime Minister and Mr. Bush have mentioned is going to be around skilling and upskilling because we’re going to create new opportunities. If you think about it from a customer perspective, if we improve what the customer wants, we’re going to create new mechanisms or new ways of doing things. And that’s what’s going to take jobs. So in our history, we’ve always had multiple things that have taken jobs away and made life easier in a sense for people. There always has been a new set of jobs. One of the classic example is. At the airport, in most places, we don’t have a check-in counter that we check in. It’s the kiosks that we do, and that, again, has taken away lots of jobs, but society has evolved. That’s not that many compared to the auto manufacturing, right? Compared to auto manufacturing 30 years ago or now, so if you look at both perspectives. So I think these are the things that we’ve got to keep in mind. So, again, it starts with the customer. The last point I’ll mention there is the organization has to be a purpose-driven organization. So when you start with purpose, it gives a lot more comfort. Again, as Mr. Bush mentioned, that comfort is essential for people, that we’re going to do things the right way. And if you’re going to do things the right way, then you have everyone collaborating with you to make it happen.

Tian Wei: And to me, that is the most important part for the future. Ms. Hart, I know she has a great point on that, too.

Stephanie Pullings Hart: Yeah, no, I mean, I was going to compliment and maybe just give a slightly different perspective as well to leveraging AI and what it looks like two to five years from now. You know, quite frankly, I think that we are experiencing in many parts of the world labor shortages. You know, my kids are not the ones that are raising their hands saying, I’m going to go into manufacturing, you know, or work in a distribution center. And so this is also an opportunity. Or consumer products. Or consumer products, right? And so, I mean, I’d like to bring sexy back to manufacturing and distribution and make it attractive. And one of the ways to be able to do that is to really leverage and build on technology and show the opportunities that can exist so that we can consistently provide you with the best cup of coffee, you know, every time you drink Nespresso or Nescafe. So that, you know, when we are experiencing different types of downtime activities, we have machines that are learning because they’re taking data at milliseconds. And instead of having someone trying to translate all of this information, we have the ability to have it interpreted and really provide us with better long-term sustainable solutions so that we can continue our path of growth. And so, you know, I think that it complements, as you actually said, AI and humans complement each other. And it’s just a different skill set and part of the evolution. And, you know, when you think about maybe for all of us on stage, we remember where the world was when there wasn’t a remote control for a television or there wasn’t this thing we call an iPod. There was a record player, there was a cassette player, and you push play and record. And, you know, we’ve seen this evolution go really fast, but we’ve all adapted. Yeah, thank you very much. I think we open the floor because there are more fascinating questions

Tian Wei: coming from the experts in the audience than mine. Yeah, a lady over there and we’ll come back to this gentleman, yes. Maybe we collect the question, how about that? We collect the question, we ask the panelists to answer. That would be more efficient. Would you like to do that? Your name, where from, and then the question. Please, thank you. Hi, thank you so much. I’m Shanti Raghavan, I’m from India and I’m a Schwab social innovator working on livelihoods for persons with disability.

Audience: So I have two questions. One, how about? Sorry? How about one question or two, very quickly? It’s kind of, you’ll understand it’s linked. One is, I didn’t hear about safety in this whole smart factory thing because quite a few people become disabled, especially in the suppliers. I’ve seen that in Haryana especially. The second question is, I won’t consider these smart factories if they’re not built with disability inclusion. So I just wonder, yes, please. Okay, can I answer? Yeah, very quickly before I go to the next. Ah, you wanna collect all of them? Yeah, yeah, collect all the questions. No problem. Yeah, I’m a J. Liam Professor, Director of Industrial AI Center of University of Maryland, College Park. My question is because without a smart factory, beside workforce, we have to attract smart people. I agree, you’re 100% right. How do you attract smart people? My student just graduated, get $200,000 offer from semiconductor company. How can your company get $200,000? Sorry, for engineers? Talk to this professor after this, yeah. Okay, great question. Please, this lady, yeah. I’m Isabelle Hartung, a multi-board member. I have a question. I mean, I love the topic of smart factories, but honestly, it’s around like 20 years or even more. The tools have become more powerful. And with AI, of course, you have a booster. Right now, we all talk about competitiveness and growth. And it feels like, I mean, smart factories could be the solution or like, you know, it’s like the accelerator to get there because you’re improving your cost base, you’re improving quality. I mean, you elaborated all that. But I mean, how can we unleash right now these productivity gains and really accelerate smart factories across Europe, across the world to, you know, like make the big jump? that, you know, we all dream of. Yeah, updated version, right? Okay, a lady over there, and then we go to the gentleman over there. I think after these two questions, we have one more question if we can, and no more. Thank you, Liz Reynolds, MIT. I wanted to ask about legacy factories versus greenfield factories, because what we see in the States right now is a lot of discussion about new factories, new investment based on some of the incentives, et cetera. And at the same time, the States is lagging behind in a lot of different metrics on adoption. And is that a problem with just a legacy system in general? Or is that something unique to the country? Or should we just be pushing for new factories, you know, which is probably unrealistic? Thank you so much. And there’s one question here and one question over there, and then we’re done, okay? So please. Morning, Bahia Jafar from Kuwait. We have a small dairy. And my question is to all of you, how would you turn an old factory into a smart factory? Which is similar. Yeah, okay. Thank you. Yeah, last one, yeah. If we could remember all these questions. Yeah, yeah. MKS Pump, Marwan Shikarji. Just, we heard of the successes. What are the failures we should be looking for?

Tian Wei: Okay, so no, no more questions, I’m really sorry. So we got a lot of things about whether it is new or not, how to update it, right? Related to legacy, related to some of the other things. And the other thing is how to make it more society-friendly, I think, as the lady just asked. And think about the talent. How to really attract the talent into the areas that you’re talking about. How about these three areas, generally speaking? If I would opt in for the first one, this legacy, but also scaling that can combine this. Please, yeah. If you like. And, I mean, you’re. Briefly, for everybody. Yeah, yeah, yeah, you’re to the spot. Because scaling, we have a lot of proof of concepts. But scaling technology is the name of the game. And Brownfield is super important.

Roland Busch: So here comes my point. Number one is, what you need to do is you need to create technology which is easy to use, plug, and play. This is so important because if you want to scale it, if you want to accelerate, and also in Brownfield, don’t forget this. The majority is small and medium-sized enterprises, semi or non-automated, and how to get there. So number one is, it’s two ways, or basically three ways. Number one is that we, it’s on our turf, make it easy to use, plug, and play. So that you go to Brownfield, you plug in an IoT device which just works, and you get your data uploaded, and you can start running an app from the very beginning. Number two is use ecosystem systems integrators. They’re the big guys, Accenture we are working with, but also small ones, small which are local. If they train their muscles on this technology, they can help locally. So this is definitely level number two. Last point. And the last point is, start at a certain point which gives you a return. I mean, don’t make an industrial metaverse on a small manufacturing site to the full version. Start where you have the incremental benefit, and then you can roll it up one by one, cell by cell.

Tian Wei: Any more questions, any more answers coming about update, traditional to smart factory, and any more answers? I’m happy to engage with you after anybody else. I mean, we have 340 manufacturing facilities, and they’re not all brand new, and we’re not gonna continue

Stephanie Pullings Hart: to build just brand new factories. And so it is about making the right choices and focus so that you can actually address

Tian Wei: the opportunity appropriately. Okay, Mr. Deputy Prime Minister?

Gan Kim Yong: I think this is a very exciting journey that we are embarking on. The transformation to automated smart factories. To answer your question, the real truth is that we need to create smart jobs so that the smart people can take up these smart jobs and feel excited about it. And your point that you want to bring sexiness into. the manufacturing, you know. So I think therefore, I think that there are three key roles that we’ve generated because of this transformation. The first one is smart operators that operate smart systems. Second role is a smart integrator to be able to integrate like digital twins. How do I integrate it into the manufacturing facility? You need smart integrators. The last one is smart innovators. So you need to really innovate new systems and the journey will never end. You’ll continue to have new innovation, smarter and smarter factories. It’s not just smart factories, but smarter factories. So the key is generate smart jobs so that you can attract smart talent into this manufacturing sector.

Padraig McDonnell: So, you know, we talked a great, fantastic question about scalability of smart factories and it’s about productivity, but what does productivity do? It allows us to invest back into the business. And I fundamentally believe that innovation is at the core of smart factories. So how do we bring it closer to customers? How do we bring more innovation out of factories that are linked to our R&D groups? And I think when we are attracting talent, I really believe in the purpose of a company can really attract talent into industries. And also in these smart factories using these technologies, it’s a wonderful jump off point for your career

Tian Wei: into different aspects of the company. The value is really behind all of this. That really speaks. Mr. Shah and Ms. Hart, anything to add?

Anish Shah: I’ll just quickly add that there are enough technology tools out there. And especially with data being the heart of AI, you don’t always need to change machines to be able to create a smart factory. And maybe the last thing is just, you know,

Stephanie Pullings Hart: I think one of the essential aspects of doing what all of us are talking about is ensuring that you have leaders that have a clear vision so that people know what is possible and where they can actually go and know that they have the support to be able to make those transformations.

Padraig McDonnell: One last point, if it’s okay, it was a key question. Both of our lighthouse factories were in existence for 30 years and were changed into lighthouse factories by having a clear vision. and not doing small silo changes, but looking at overall changes with an end point to why we were actually doing it. You know, everyone on this panel, well worth five hours of interview.

Tian Wei: But we tried to squeeze them into 45 minutes and they did such a great job with the help of all of you. So thank you so much, ladies and gentlemen. Thank you. Thank you, everyone. Thank you, sir. Thank you. Thank you. Thank you. Thank you.

Agreement Points

Importance of workforce transformation and skills development

speakers

– Gan Kim Yong
– Roland Busch
– Padraig McDonnell
– Anish Shah
– Stephanie Pullings Hart

arguments

Government programs for worker reskilling and training

Need to reduce anxiety about job losses from automation

Opportunities for workers to learn new skills with smart technologies

Importance of purpose-driven organizations in workforce transition

Pairing younger and older workers to bridge technology gaps

summary

All speakers emphasized the critical need for workforce development, training, and reskilling to support the transition to smart factories and address potential job displacement concerns.

Implementation of AI and digital technologies in manufacturing

speakers

– Roland Busch
– Padraig McDonnell
– Anish Shah
– Stephanie Pullings Hart

arguments

Digital twins and AI-powered optimization in factories

Lighthouse factories improving productivity and sustainability

AI applications for quality control and efficiency in auto manufacturing

Digital tools enabling predictive analytics and workforce improvements

summary

Speakers agreed on the significant benefits of implementing AI, digital twins, and other smart technologies in manufacturing to improve productivity, quality, and efficiency.

Need for scalable and accessible smart factory solutions

speakers

– Gan Kim Yong
– Roland Busch
– Padraig McDonnell
– Anish Shah
– Stephanie Pullings Hart

arguments

Government support for smart manufacturing transformation

Making technologies easy to use and plug-and-play for wider adoption

Clear leadership vision needed to drive transformation

Using data and AI to upgrade existing equipment cost-effectively

Focusing on incremental benefits when upgrading legacy facilities

summary

Speakers agreed on the importance of making smart factory technologies accessible and scalable, particularly for smaller companies and legacy facilities, through government support, easy-to-use solutions, and strategic implementation approaches.

Similar Viewpoints

Both speakers emphasized the importance of addressing workers’ concerns about job displacement and the need for comprehensive training and reskilling programs to support the transition to smart factories.

speakers

– Gan Kim Yong
– Roland Busch

arguments

Government programs for worker reskilling and training

Need to reduce anxiety about job losses from automation

Both speakers highlighted specific examples of successful smart factory implementations in their respective industries, demonstrating tangible benefits in productivity, quality control, and efficiency.

speakers

– Padraig McDonnell
– Anish Shah

arguments

Lighthouse factories improving productivity and sustainability

AI applications for quality control and efficiency in auto manufacturing

Unexpected Consensus

Importance of purpose-driven organizations in smart factory transformation

speakers

– Anish Shah
– Stephanie Pullings Hart

arguments

Importance of purpose-driven organizations in workforce transition

Pairing younger and older workers to bridge technology gaps

explanation

While most discussions focused on technological aspects, these speakers unexpectedly emphasized the importance of organizational culture and human-centered approaches in successful smart factory transformations.

Overall Assessment

Summary

The speakers showed strong agreement on the importance of workforce development, the implementation of AI and digital technologies in manufacturing, and the need for scalable and accessible smart factory solutions. There was also consensus on the role of government support and the importance of addressing workers’ concerns about job displacement.

Consensus level

High level of consensus among speakers, with complementary perspectives from government, large corporations, and different industries. This broad agreement suggests a shared vision for the future of smart manufacturing and highlights the importance of collaborative efforts between government, industry, and workforce to successfully implement and scale smart factory technologies.

Different Viewpoints

Approach to workforce transformation

speakers

– Gan Kim Yong
– Roland Busch

arguments

Government programs for worker reskilling and training

Need to reduce anxiety about job losses from automation

summary

While both speakers address workforce transformation, Gan Kim Yong emphasizes government-led programs for reskilling, while Roland Busch focuses on reducing worker anxiety about job displacement.

Unexpected Differences

Overall Assessment

summary

The main areas of disagreement revolve around the specific approaches to workforce transformation and the methods for upgrading existing manufacturing facilities to smart factories.

difference_level

The level of disagreement among the speakers is relatively low. Most speakers share similar goals and perspectives on the importance of smart factories and workforce development. The differences mainly lie in the specific strategies and focus areas each speaker emphasizes based on their experience and sector. These minor disagreements do not significantly impact the overall discussion on the importance and implementation of smart factory technologies.

Partial Agreements

All speakers agree on the need to upgrade existing facilities, but they propose different approaches: Busch emphasizes plug-and-play solutions, Shah focuses on using data and AI, and Hart suggests focusing on incremental benefits.

speakers

– Roland Busch
– Anish Shah
– Stephanie Pullings Hart

arguments

Making technologies easy to use and plug-and-play for wider adoption

Using data and AI to upgrade existing equipment cost-effectively

Focusing on incremental benefits when upgrading legacy facilities

Similar Viewpoints

Both speakers emphasized the importance of addressing workers’ concerns about job displacement and the need for comprehensive training and reskilling programs to support the transition to smart factories.

speakers

– Gan Kim Yong
– Roland Busch

arguments

Government programs for worker reskilling and training

Need to reduce anxiety about job losses from automation

Both speakers highlighted specific examples of successful smart factory implementations in their respective industries, demonstrating tangible benefits in productivity, quality control, and efficiency.

speakers

– Padraig McDonnell
– Anish Shah

arguments

Lighthouse factories improving productivity and sustainability

AI applications for quality control and efficiency in auto manufacturing

Key Takeaways

Smart factory technologies like AI, digital twins, and automation are driving major productivity and efficiency gains across manufacturing industries

Workforce transformation and skills development are critical for successful implementation of smart factories

Government support and policies play an important role in enabling smart manufacturing adoption

Scaling and accelerating smart factory adoption requires making technologies easy to use and focusing on incremental benefits

Clear leadership vision and purpose-driven organizations are important for driving smart factory transformations

Resolutions and Action Items

Companies should invest in training and reskilling programs to prepare workers for smart factory technologies

Governments should provide support and incentives for companies to adopt smart manufacturing

Technology providers should focus on making smart factory solutions easy to implement and use

Manufacturing leaders should develop clear visions and strategies for smart factory adoption

Unresolved Issues

How to effectively upgrade legacy manufacturing facilities to smart factories

How to attract top talent to manufacturing careers

How to ensure safety and disability inclusion in smart factories

How to address potential job losses from increased automation

Suggested Compromises

Focus on incremental upgrades and benefits when transforming legacy facilities rather than complete overhauls

Use ecosystem integrators to help smaller companies adopt smart factory technologies

Pair younger and older workers to bridge technology gaps in the workforce

We want to solve the big challenges, which is not only climate change scarce resources, but it’s also labor shortage Shortage of skilled labor. We want to produce localization that you have more smaller factories smaller lot size Shorter cycle times so that that’s the idea and that’s a lot of technologies which behind which are serving these frontier technologies

speaker

Roland Busch

reason

This comment framed the discussion by outlining the key challenges that smart factories aim to address, providing context for the subsequent conversation.

impact

It set the stage for a more focused discussion on how smart factories can address specific issues like resource scarcity and labor shortages.

AI has helped us to reduce the time from eight days to two days in designing the systems and getting them up and running, doubling the output, and I think it also creates a really learning opportunity in the factory as well.

speaker

Padraig McDonnell

reason

This concrete example illustrates the tangible benefits of AI in manufacturing, showing both efficiency gains and learning opportunities.

impact

It shifted the conversation towards specific applications and benefits of AI in smart factories, encouraging other panelists to share their own examples.

This digital transformation requires an investment in capabilities for our workforce. We are on this very very robust journey and have great partners like Siemens, Aviva that we’re working with as well but one of the things that we were talking about in the green room when you do have a large number of employees or even if you have a small number of employees oftentimes we underestimate the impact of change management and it is one of the most critical aspects that we are focused on as an organization

speaker

Stephanie Pullings Hart

reason

This comment highlighted the crucial aspect of change management and workforce development in digital transformation, which had not been emphasized before.

impact

It broadened the discussion to include the human aspect of smart factory implementation, leading to further comments on workforce training and adaptation.

We’ve just launched two Born Electric vehicles. And the questions we’re getting back from customers as well as our investors is how can you afford to do this at such a low price as compared to the quality you’re offering these vehicles? Are you making losses? Are you, is this, I just had a media interview this morning where I was being grilled on that saying, is this an introductory price? And I said, no, we actually have good margins on this. We just have better manufacturing tools that allow us to do it.

speaker

Anish Shah

reason

This real-world example demonstrates how smart manufacturing can lead to competitive advantages in the market, challenging assumptions about cost and quality trade-offs.

impact

It shifted the discussion towards the business impact of smart factories, prompting consideration of how these technologies can create market advantages.

Different countries will have different economies with different considerations, different priorities, and many countries actually have a lot of resources. Sometimes it’s not just about money, but about the concept, about the determination, about the focus on what you want to do. And this, in a way, if you look at it, it’s an investment in the future. It is not a cost that you write off.

speaker

Gan Kim Yong

reason

This comment broadened the perspective on implementing smart factories, emphasizing that it’s not just about financial resources but also about vision and determination.

impact

It encouraged a more nuanced discussion about how different countries and companies can approach smart factory implementation, regardless of their current resources.

Overall Assessment

These key comments shaped the discussion by broadening its scope from technical aspects of smart factories to include workforce development, market impact, and strategic considerations. They helped to create a more holistic view of smart factory implementation, emphasizing both the technological and human elements. The discussion evolved from theoretical concepts to practical applications and real-world examples, providing a rich exploration of the challenges and opportunities in smart manufacturing across different industries and geographies.

How can experiences and knowledge about AI applications in manufacturing be shared across different industries?

speaker

Gan Kim Yong

explanation

This is important for promoting cross-sector learning and accelerating the adoption of smart manufacturing technologies across various industries.

How can we address safety concerns in smart factories, particularly for workers with disabilities?

speaker

Shanti Raghavan (audience member)

explanation

This is crucial for ensuring that smart factories are inclusive and safe for all workers, including those with disabilities.

How can companies in manufacturing attract top talent, especially when competing with high-paying tech companies?

speaker

J. Liam (audience member)

explanation

This is important for ensuring that manufacturing companies can access the skilled workforce needed to implement and manage smart factory technologies.

How can we accelerate the adoption and unleash the productivity gains of smart factories across Europe and the world?

speaker

Isabelle Hartung (audience member)

explanation

This is critical for realizing the full potential of smart manufacturing technologies and improving global competitiveness.

How should companies approach upgrading legacy factories versus building new greenfield factories?

speaker

Liz Reynolds (audience member)

explanation

This is important for understanding the most effective strategies for implementing smart manufacturing technologies in different contexts.

What are the potential failures or challenges to look out for when implementing smart factory technologies?

speaker

Marwan Shikarji (audience member)

explanation

This is crucial for helping companies anticipate and mitigate risks associated with smart factory implementations.