Utilizing big data and machine learning for predictive modeling in the b2b supply chain - Ascent Conference Utilizing big data and machine learning for predictive modeling in the b2b supply chain - Ascent Conference

Utilizing big data and machine learning for predictive modeling in the b2b supply chain

Kenneth Goodwin, Senior Managing Principal & Founder @Jeanensis Capital Markets
Main Stage
Ascent Conference 2020

[00:00:01] Hello, good day, How’s everyone doing today? This is and good one is a. a pleasure to be speaking at the conference this afternoon. My topic will be on utilizing big data and machine learning or predictive modeling in the B2B supply chain. Let me just first start off by giving you a bit of background. My firm, which is tremendous capital markets or Genesis of the GinĂ©s, is basically we are a capital advisory firm. We focus on artificial intelligence, red tech, as well as the block chain industries. So we’ve been working in that industry for a period of time. And several firms that we’ve been concentrating on deal would be to be business, cross-border trade. So things have moved on. So let’s first start off with the idea of imagine a borderless trade system.

[00:01:04] What would that entail domestically, but also internationally? Now, essentially, this starts with a quote by Dr. Peter Drucker. Dr. Drucker once said that you don’t just look at the actual operations and you don’t just look at the actual efficiency of the operations, which you do concentrate on is the actual processes and the actual systems themselves. So within those particular comments, what you’re going to find is is maybe three elements to concentrate on. When we talk about the idea of using data management data systems for voice trace system. The first one is data analysis that we’re going to focus on. The second one is the use of block chain and digital technology, also DLT. And the third area is artificial intelligence with a focus and emphasis on not just machine learning and deep learning that we’re concentrating on, but we’re also concentrating on what we call third wave A.I. and third wave A.I. tends to be just a little bit background. It tends to be a little bit more advanced. And it looks at it from a standpoint of a cognitive approach where that algorithm acts in a way where the probes and it has a memory feature to it. That’s very significant for data and analytics. But also at the same time, it’s also very it’s very significant for predictive modeling. Next, please, Kevin.

[00:02:43] OK, Kevin. Next, please. Yes, so the world is definitely changing, as we can see, the world is gravitating, is changing drastically.

[00:02:54] Covid-19 has definitely brought on this digital transformation. And one of the areas that it’s really focusing on is the use of big data. And within that big data function, we focused mainly on natural language processing. Now natural language processing, at least with the efforts that we had at geneticists. So our clients pretty much operate businesses around here. And the focus has been on mainly on the use of data where they can smooth data out, but most importantly, being able to tell a story with the data that’s ascertain and that’s very significant. Then beyond that is another focus on cost specification management. Specification management looks at raw data at its core, and it also be able to track and measure and monitor particular data. This is a focus and do a specification management. It allows for a smooth operation as smooth processes. For example, when I did some work at Eataly Acardo, known as Seven-Eleven, they use what they call a specification management process and essentially what that was added to your do a 7-Eleven in Japan, the idea was that you as a consumer, you’re able to go into the store and when you go into that store, you’re able to purchase an item and then purchasing an item destock. Cloke is able to ascertain certain data from you that could be in terms of barcodes, that could be in terms of when you purchase that and that can be with prices and so forth. Now, the reason why that was very important in terms of specification management, what that did was that particular raw data was was being used at the very granular level. And that level includes the actual store operations, but also in terms of the distribution centers. So what you find at the store we use in data that I do ascertain from consumers that was use within distribution production as well as the accounting feature. And that’s very significant in terms of how data is being used, how it’s been analyzed and how it’s being processed. The better way to to use this data information is what we know and as to block chain system and block chain in itself. It is a distributed technology system that pretty much allows for efficiency, the speed to occur. The key with the block chain system is that there’s a trace and mechanism to that particular system. So that allows for not only data to be transparent, but most importantly allow for data to be a little bit more efficiently. And then finally, we want to touch on A.I., particularly between machine learning and deep learning, but also third wave I and I. My emphasis has been on third wave A.I. because generally that’s the next step in terms of having a memory that allows to memorialize that data, but most importantly, being able to use that data and predictive modeling. And of course, the features that have been used for that is the APIs, the apps, as well as the cloud, which is going to be used to store that particular data. So if you utilize this all in one particular system, you are able to not only to address big data, but you’re also able to execute the block chain as well as ADD is going to actually move the BTB process for most of the businesses around the world.

[00:06:33] Kevin, next, please. Next slide, please.

[00:06:37] So in the air of data proliferation, let’s concentrate on big data a bit as data becomes more proliferated and as data is being more used, what we’re finding is that this tends to be more platforms that are being created and particularly APIs. So which are finding is that the tech is basically changing us. So as you’re finding that there’s more APIs that’s more reliant on particular data. Now, the reason why that’s very significant and to give you a prime example of that, is that it’s allowing for different types of products to be produced. Those for those products could be based off of what we codify, define decentralized finance. Seifi and that’s mainly in the blotchy space, but also how it’s being used across different platforms. For example, there is a product right now called the Brewery Project that really essentially allows for multinationals to work together on a data platform. Data that is the ultimate goal is to solve between anti bribery and anti corruption. However, what are the key elements that have occurred as a result of covid-19 is that the platform has the digitization of the platform has exposed a lot faster. So what happens now is that these firms, particularly firms, large cap firms, are on a platform such as ABN InBev, such as such as a state, Loida, LVMH, all these large firms are now asking the question, how is our supply chain is being impacted? So essentially what’s happening here is that these firms are starting to look at not just data analytics, but they starting to realize that they have to go down to their particular raw materials and levels that they hadn’t had before. And that’s a part of specification management. For example, Estee Lauder makes cosmetic goods. So there’s the beauty side of that. That’s the just of substantial products that they make about InBev, make brewery and beers and alcohol, as well as LVMH, make leather goods. So the goal there is to to harmonize where you can identify families at a very local level, be able to work with those particular Formosan on boredom, but also allow that to to track and trace and measure where the raw materials, ingredients are coming from. And in doing so, you’re able to address some of the issues that may happen with the other companies, as mentioned, and providing more material and providing the final product, such as leather goods, such as alcohol, such as cosmetics, that in a sense, what it does, it mitigates the risk that may occur with these particular firms that could be anti bribery and corruption and so forth. Next, please, Kevin. So let’s move on to the blog change system. What’s significant in that particular model that I mentioned with data analytics, the blog change system has grown significantly. It’s one of the better platforms that the priviledge a platform that allows data to be transparent and data to be used nowadays. What we here with the blog change system, when we got to be to be transaction’s is that the biggest challenge is what they call a centralized government block chain currency. And that’s really very significant because with that particular centralized digital currency, it allows for global payments and remittance and sediments to occur a lot more faster being it is a centralized currency. But most importantly, that allows for a sense of stability and a stable coin. So all of this could be done in what they call particular smart contracts and in doing them with particular smart contracts with the use of data. These these B2B LogCAP firms are able to to do multiple things one day to have a peer to peer lending opportunity where they can do peer to peer online among themselves. Most importantly, they can do the particular global payments and remittance with ease. And also they can have e-commerce feature where they’re allowed to sell all the products and services via the block chain system. Now, there have been some applications of this being done before with success and ease, at least in the area of trade finance and essentially the trade finance procedure. At least, you know, we tend to take anywhere between two weeks, about one or two weeks at a bank level due to the level of documentations that are being done about the use of block chain technology as actually Expedia, that transaction transactions almost two to three days. So that’s the benefit of having block chain with the use of data. Of course, there’s other areas that block chain is well known and recognized for. That includes recordkeeping. We see a lot with securities, at least the securitization of assets. But most importantly, block chain is very significant in the process, in the process of actually doing a digitized platform that’s going to allow for B2B firms to operate globally with ease.

[00:12:14] Next, please, Kevin.

[00:12:17] So that leads us to the third portion of our discussion, which is artificial intelligence and A.I. has developed at least the investment of A.I. has really progressed significantly and has gone beyond at least Vontae level of third wave A.I. But when you look in terms of predictive modeling, a lot of the predictive modeling is basically using deep learning machine learning technology that allows for data to be smoothed out, but most importantly, to tell a better story with third wave, a third wave A.I. is allowed for not just the deep learning, the machine learning to occur, but most importantly, it’s allowing for that data to be to be memory. So there’s a memory feature to that. But most importantly, it’s a probing mechanism. And then that program mechanism, it allows for that algorithm to kind of look at the data that that occurred recently and be able to to probe and see what it would be like in the future. Now, that’s very significant because that’s allowing for not only a solution, but is also given an opportunity for for for the for the consumer, for the business to react and to produce and to create. So that wave A.I. is significant in the process, along with block chain as well as with data analytics that would allow B2B firms to be able to to execute cross-border deals and trade with ease. Next, please, Kevin.

[00:13:54] So in conclusion, one of the key things that we need to focus on in terms of using predictive modeling, what goes in, data goes in, is just as is as as important as data going out.

[00:14:10] So it’s really depend on the quality of data. At the end of the day, the quality of data will determine how well that predictive modeling will work. However, this other tools that could work where that put that data could actually be used to help businesses to operate on a global scale. Most importantly, we touched on the use of data analytics, but mainly specification management and the tools. A specification management will be used to attract information at the raw material level. Not having that ability to do that allows for the systems to track and trace not just data, but also trends. So there is a level of analysis that is being done. And then of course, moving all along to scale beyond specification management is natural language generation. So being able to use that data and smooth it out and tell a nice story as to how those operations and that information is being used now in order for that to occur, the platform for the curve, the ideal platform is a block chain or distributed ledger technology system. And then using a blocking system, it allows for kind of parties to be able to see each other to share information, data with a level of transparency, but most importantly, to be able to to make decisions. At the end of the day, with the block chain technology, you want to be able to apply where you make a decisions and you allow to mitigate the risk. One of the prime examples that I talked about was the use that is being used now and the BRURIAH project where you have multiple of large cap firms. They have multiple products, but they have very similar issues and challenges. And in addressing those issues and challenges is the use of data management, but also a mixture of block chain technology. And then finally, towards the end of this is how do we incorporate a level of artificial intelligence that is being incorporated at the data analytical level in terms of the algorithms that are being produced using deep learning and machine learning, but most importantly, being able to use a memory function that allows for data analytics to occur as well as appropriate feature. Now, having that program feature is able to allow for a predictability factor where now you can make a better decision. So as you move along the scale and artificial intelligence, you’re able to able to not only just predict, but you’re able to make final decisions and faster decisions and technology. I want to thank you for the opportunity for this discussion is an ongoing discussion that’s being half an industry. But thank you again for the opportunity and I look forward for future of future discussions. Thank you very much.

[00:17:10] Next, please, Kevin.

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