Caroline I think we're gonna be diving into the topic of logistics today. And i think what people think about applying ai. Logistics are often thinking about tracking where trucks are tracking our inventory levels. But for you folks who've worked on some ai. Applications leveraging nlp for logistics and supply chain purposes obviously rolls royce of massive industrial organization. You have a lot of supply chain needs. Can you go a little bit into what the problem was. What you guys have kinda built to help your cause. They're sure dan absolutely so. Yeah i mean just a little bit of context rolls royce overseas a hundred year old company and as a result we are also company that manufactures highly complex and very high value physical things and as a result. We have a very complicated supply chain structure. We also have certain parts of our supply chain where there are very few companies around the world who can actually make what we need from all supply chain so they're all points in our supply chain weather arrow some quite quite high levels of risk in terms of sourcing strategy. Because we don't have an infinite number of supplies you cannot and on top of that we have or certainly did have a real challenge with the presence of what can really only be described described as incredibly dumb unstructured data across our supply chain so information held in pediatrics information on contracting terms on supply standards or capabilities or even designs on top of that an awful lot of our supply. Chain information is either engineering drawings or it's mathematical notation tabulated information and those are three categories off of data which is very hard for existing kind of simplistic optical character recognition tools to actually extract reliably. So over the last couple of years we've been working to develop a natural language processing computer vision capability that she allows us to extract intelligence from all of those kind of dumb unstructured data sources. So that we can use not intelligence to be much more adaptive much more flexible much more responsive in managing risk across supply chain and clearly given the challenges that we faced in some of our markets globalization being one of them over the last twelve months with the covid nineteen pandemic having that increased capability for managing supply chain risk flexibly being able to understand using machine intelligence to actually understand induce scenario modeling across ask supply chain that has helped to support our business in making very significant financial savings in terms of its response to the pandemic but also broadly in terms of ensuring businesses fit for purpose as we become out to the pandemic in terms of managing complex supply. Chain starches for the future hansard. There's so many ways that this can come into place we we've done a lot of One of the sectors where we do our ai. Opportunity landscape research. Every year is chain. Logistics looking at everything in terms of matching loads two vehicles for for transportation to inventory prediction an and arrival times and whatnot. Nlp in this space is interesting is novel and i can imagine so many ways that it might be leveraged can imagine you reading news and information about your various suppliers. Maybe it's the weather in the area. Maybe it's something about them. Having a bad quarter whatever the case may be and i can see that maybe being factored into what production volumes you think they may be able to do or what arrival times you might be able to expect or i could even say a system that just puts the most important news in front of a human analyst who can put it in broader context. Because of course there's so much context for that dumb data to actually speak to business needs on how specifically is nlp sort of working here. And what's maybe an example of where this is starting to be able to inform our processes inform our prediction. Or whatever it's doing yeah. I you're absolutely spots on that donna. Interestingly some of these cases or those areas that you've just mentioned there are exactly where we want to take capability next cohort of the work that we've been doing for rolls royce as a global entity in the first phase of development of this capability. We have been working with those tricky types of data that exists in manufacturing and engineering supply chain unstructured data sets. So those would let me give you an example much of what we communicate while supplies is communicated through drawings engineering drawings. So these these two d drawings which then need to be rendered into three day geometry's in order to be able to understand way to no debate able to understand how much waste material might be generated from from making components. Nfl which the supplier that component of will reimburse us for of course because they charge us for the bulk weight of the material and then the machine material they reinvest because they resell out secondary market so for us to understand for example you know how much is something away how much we're gonna cost to ship and transport How much rebate should we be guessing from from the waste material. That's been resolved by the supplier on. We need to be able to render a to d draw ring into its three d geometry now most of the during dumped exist in cat they only exist to droids so using a combination of competition and alpay. The nlp helps to extract the numerical information. That geometrical information is written around the drawing and the computer vision helps to manage the actual rendering of the drawing itself. You can actually turn your community station into three d rendering virtually and that of and allows you to couch might understand always questions about white about pricings about costing about justice about waste material might be generated for manufacturing