 Some nice slides to start things off just to kind of put things in a bit of context. Accenture talks about this idea that, you know, the massive growth in complexity that's happening at the moment. We have a perfect storm of everything is getting faster and more complex. The markets are speeding up. Products that must be produced and moved into the supply chain and quality controlled are becoming more complex and we're starting to see combinations of services and products coming together. The workforce that are producing these products or controlling these products have new expectations in terms of what they expect from their employers and the organizations that they're working in. And the economy is changing with so much disruption coming in from other sectors now from Amazon entering the healthcare market and so on and so forth. There's a huge amount of challenge and changes in the technology in the market at the moment and in parallel a lot of changes in the technology landscape. This slide is probably out of date and you all know just how much data is being generated now in the last two years, more than in the history of, in our history of mankind, but a massive amount of complexity and data to be collected and analyzed. And again, the compute power growing exponentially from the slow adoption of mainframes from the 50s onwards to now what we're seeing a burst in excitement around quantum computing and Accenture and IBM and others have started to really do some interesting work in the quantum computing space. But again, the adoption rate of AI compared to some of the older technology has been quite startling. So Accenture, our take on industry 4.0 is we refer to industry X.0 and essentially what we're talking about here is the idea that companies can transform the core of their business through linking better research and development and design of new products through the production of the process or the product and the aftercare support of the product. So it's not just about manufacturing and driving efficiency and manufacturing. It's also about linking all of the silos within companies, which in some industries has been done better than others. Aerospace being one that's worked very well and others less so. And when you have those kind of IT architecture to transform that core and link up these parts, these key parts of the organization, adding in digital enablers like data analytics, like advanced visualization tools to really drive value out of that data that you're collecting in your architecture. And while industry 4.0 and a lot of the messaging around that focuses on how, you know, digitization can help drive acceleration and efficiency in the supply chain. Accenture's point of view is very much just also an above the line point that we need to keep focused on around supporting and the launch and development of new services and experiences that are wrapped around the product. So it's not just about making a widget. It's about how the organization can be ready to wrap other things around that product and really drive top-line growth through innovation and growth. And there's just a little bit more detail. There's lots of different strands to this around, you know, clearly here the issue of analytics, cybersecurity, workforce and talent and so on so forth. So there's really a lot in this that I don't have time to go into in massive detail at the moment. But yeah, I think we have some interesting perspectives on this idea that the factory of the future and the supply chain of the future will be smart, connected, living and learning. And then this is the idea of the digital factory and the many dimensions to this and how do you kind of work through all this and where to start. We have these conversations a lot with companies at the moment where they're playing all the right notes but not in the right order necessarily and they're doing some of all of these things but that's not necessarily all of them. And they're often looking for benchmarking. So, you know, we're looking at the ITOT interface, analytics, process life, product lifecycle management. An area that's very close to my heart as well is that's often forgotten in talks about industry 4.0 is this issue of quality control, the bar tree, the QC lab, the metrology lab, making sure that's efficient, it's not a bottleneck. How do you get the workforce digitally enabled, automation, security and so on. So, there really is a huge amount to this that needs to be tackled and different companies are at different stages along this curvature. And yeah, we would argue that really it's in the combination of multiple technology. It's not just about data analytics but it's about better capture of data, industrial internet of things, working with the core IT architecture like your MES systems and your SCADA's and PLC's but also then trying to bring in, you know, increased sensorization and a more kind of agile approach to adding greater data capture on your factory floor. And combining these like robotic automation, AI and advanced analytics with IoT is really where the value can get unlocked. And the kind of, you know, use cases we see for these, this combination is looking at things like predictive asset maintenance and I'm not going to again dwell on any of these in any great detail but we see huge appetite for this in the industry at the moment especially in discrete manufacturing where and also in areas like oil and gas where there's very heavy utilization of assets and downtime and an asset can have huge negative implications on the supply chain. So, trying to use advanced analytics and machine learning to improve productivity and quality within the supply chain. Again, looking at quality analytics and ensuring this idea of the consistency of the product quality, not just about driving lower driving down cost of good souls but ensuring that you're having, you know, zero defect and inspiring towards zero defect manufacturing as a key KPI. And looking at, yeah, this idea of combining these technologies to boost edge analytics, I think Guilherme was going to talk about that in a little bit more detail. This is just a case study of something we've done again in the food and drinks industry where we've worked again with a company here, Okado, to combine some of these technologies to help reduce waste and increase speed to release of products and hence increase shelf life of the product and drive out about 5% cost reduction from their from their cogs. Another case study from the automotive industry, this is a company that a sub supplier into the automotive industry that makes some of the key sensors that are used in the autonomous vehicle. Obviously, right first time zero defect is critical in a space, you know, a failure of a sensor in these technologies is going to be, you know, potentially huge issue for both this company and their customers. And they had a very complex chemical manufacturing process involving minimization process where they had some critical tipping points where they had nonconformity and they didn't really understand that. So we worked with them to really understand the data they were collecting and collect more data that they weren't collecting to achieve a 19% reduction in nonconformity. Again, touching on the idea of zero defect manufacturing and quality. And then this is an example of something we've done with Schneider Electric where we've created a digital twin of their facility to help them build new offerings and be more flexible in the offering that they can bring to their customers. This is just some research actually from a couple of years ago that Accenture did in the manufacturing space. So we surveyed companies from around the globe in a number of industries, about 450 respondents. And, you know, a key thing that kept coming up is this idea of rapidly increasing complexity. So increase complexity less time, other issues around workforce unprepared, focus on quality, lack of visibility, wanting to maximize capex and acid utilization. These are some of the key problem statements that the companies are looking at globally and are common across many industries. And what we've seen some interesting stats around the adoption of Industry 4.0 thinking is actually happening quite aggressively in India, also quite a bit in Europe, a little bit of a lag from the US, you know, at this data point at least. And also another interesting perspective is the idea that certain industries are adopting this more aggressively. So automotive being kind of the currently the industry that's probably most aggressive in adoption of digital manufacturing. And yeah, this is the the some of the knowledge that companies feel is likely to have the most impact in their productivity, in their quality metrics, in their ability to innovate. This idea of the manufacturing control tower, which can also dovetail with the supply chain control tower and how companies just have visibility and what the hell is happening in complex manufacturing environments and complex manufacturing supply chains. And you can see analytics and process monitoring really high up there in terms of technology that's already been implemented. So a lot to do, a lot done, but I think it's fair to say 30% adoption is probably still a bit modest than you would argue, given the amount of data that's being collected in manufacturing, which is I think widely known to be the manufacturing that one of the largest sources of data collection globally of any subsector, a lot more to be done and actually making use of this data. Just some perspectives on the life science sector because it's it's close to my heart and it's a very important sector for Ireland, given the amount of GDP of Ireland that's dependent on the export of pharmaceutical products and medical devices. Right now, the medtech industry is probably a bit away a bit ahead of the farm industry closer to automotive in terms of adoption of industry 4.0 and probably a bit of the learning because medtech is discrete manufacturing, a little bit like automotive, they probably have taken some learnings from aerospace and automotive. So a recent survey just this year that we've done of senior executives from the life science sector would say 56% of companies in the medtech feel they're completely ready for digitization and manufacturing, 42% somewhat ready. And if you look at pharma a little bit less, you know, slightly more not ready at all, but quite a few, the majority feel they're only somewhat ready. So more to be done there. And if you look at pharma, this is the urgent burning platform that they're faced with. I'm not going to drain this slide, but very briefly, if you look at pharma 10 years ago, the best selling drug in the world was Lipitor. You had to manufacture that by stringing 33 carbon atoms together. Last year, the best selling drug in the world was Humira. You have to manufacture that by stringing 6428 carbon atoms together. So this is the point that again, products are becoming more complex. The other point I mentioned earlier, they're coming at companies more quickly. So 75% of new drugs for cancer were fast tracking last few years. So companies have much less time to optimize their manufacturing process. And they're often launching more complex products with suboptimal manufacturing processes. And really doing it the old way that I did before without digitization is probably not sustainable. They're also focusing on producing more personalized products, more precision medicine that are, you know, so factories instead of making one drug or making maybe several drugs, and that creates the challenges. And there's other issues there as well around outsourcing, combining drugs with devices and services, and actually making products on a patient by patient basis, all of which is creating huge complexity for companies. And we started to do some nice work in the space with a global pharma company, initially more on the supply chain side, where we've used a kind of a fast approach to create with the Accenture Insights platform, which is part of Paul Paratti, who was earlier part of his group, and looking at segmenting the company's product family and helping them facilitate management, optimization of inventory across multiple product types, saving about 30 million so far from inventory transformation. And this again is some more data from that recent study that we showed where, again, this idea of combining technology, not just analytics on its own, but a variety of technology across big data, digital twin, robotics, AI, assisted reality or extended reality, that companies can increase up to 6.3 billion in market capitalization by helping to bring new products to market and save up to almost 100k per employee in terms of manufacturing costs. However, MedTech companies would say that the big obstacles they have is this idea that they have, well, they have great ideas and lots of things to do. They have maybe poor maturity of their ecosystem partners, that they have to bring more companies with them on along the journey. And something that comes up quite often with companies, they have great potential to do data analytics, but they really have a challenge in finding the people to do this, you know, a number of other points as well that are also up there on the board. This is a project we did here recently with a client where, again, not to go into a lot of detail, but highly complex manufacturing line for medical devices. You know, they had the ability to do root cause analysis at a point in time, but they hadn't really systematized this and had a high dependency on their engineers and subject matter experts. And so we worked with them to create an analytics base table, do exploratory analysis, build predictive models, a series of predictive models, not just one, and then built a kind of an easy-to-use visualization tool. So the result was, you know, a more standardized method to find root cause, user-friendly, and essentially directing workers and engineers to the probable cause with the potential to save almost 7% or possibly higher per production line on that site. And that's just the approach. Again, I'm short on time, so I'm not going to go to this in detail, but again, the approach of taking a, you know, a short kind of discovery process of looking for proof of value industrialization and scaling across the site. And very briefly, just a bit of a, you've seen some of our folks understand here, Accenture has really doubled down on the area of innovation and digital, and we have now got our global innovation hub here in Dublin. It's the largest innovation center in Accenture's network globally, so we're very proud of it. And doing some really nice work in projects relating to data analytics, and in particular in the area of manufacturing and supply chain. So we're doing a project called Project Alchemy, which is around biotech manufacturing. And you've seen, I think, a demo, I'll understand here of our VOR demo. Project Sulus around machine vision diamond is around looking at pharmaceutical label changes or any type of label changes around complex products, and also looking at cold chain technology with Vodafone. So that's it from me. Thank you very much for your attention.