Hello, everyone. welcome to this conference 42, conference, virtual conference. good morning, good evening, to all the folks who are attending across various parts of the world. Today we are discussing about the role of artificial intelligence and enhancing the supply chain. Resilience and how we can leverage, AI to transform supply chains to better handle complexity, data challenges, and disruptions. About me, I'm a seasoned professional in the technology space with about 19 years of experience doing technology, roadmap, development, strategy, and program management. I'm part of various projects implementing global supply chains, three pillar integrations, driving efficiencies within the manufacturing, supply chain, distribution, and logistic space, and also leveraging artificial intelligence wherever possible and applicable within the life sciences industry, retail industry, consumer goods industry. Yeah, let's talk about the complexity of the data, right? in today's world, modern supply chain, there are so many data points which we need to manage. If you take an example of any manufacturing industry or distribution industry, in order to do your forecasting, you need to have several data points within these interconnected systems. The way the traditional systems connect is basically have one set of data or multiple set of data. But then, in order to really drive the forecast, you need to have all your item related data, location related data, time related data, point of sale data. There are variety of datas which you need to gather, the complexity and the data overload has really become. enormous today, right? Not just that. if you really talk about the global disruptions, for example, the COVID 19 has struck us in such a bad way that we cannot just rely on shipment data in order to generate your forecast, right? there are so many disruptions happening within the industry, which is causing, the impact of about 184 million dollars per organization, right? And that is exactly where AI is coming into the play, right? AI can offer, resilience and then bring that efficiency, operational efficiency back into the game, right? Because AI has capabilities to quickly adapt to the needs, to the dynamic needs, and then, manage your time and the real time data, right? that is where AI is coming and making a pivotal role in order to leverage and then improve the resilience. Yeah, let's talk about the role of AI in supply chain management, right? agility is one of the biggest one, stay agile in the current ecosystem, staying agile in the current dynamic needs of world is very important. And AI is really offering a lot of solutions because AI has capability to really take that data to enormous amount of data and then, respond back to that market fluctuations and then unexpected events, right? Agility is a big one, right? Cost production, but by predicting cost fluctuations and cost related raw materials, storage, logistics, AI enables proactive cost management, this is again connected to the data, right? within the supply chain, a lot of times we procure the material from various parts of the world, wherever there is a availability, abundant availability of the raw material, we try to procure that material. Not just that, even from a distribution and storage standpoint, we try to, distribute and store it in different distribution center locations. Of the same organizations, right? It is very important for us to maintain all the data, but at the same time, proactively, how we can leverage AI to do the cost management of that supply and optimization. as I said, the cost production is one predominantly one at the same time, supplier optimization is, a lot of times all these suppliers are like, rank. Supplier one, supplier two, supplier three. But at the same time, the supplier also is trying to expand their footprint to produce as much material as they can and then supply as much material as they can to the variety of needs across the globe, right? How we can leverage AI? To improve the continuity and then stability in the supply network, right? a lot of times there are so many organizations who are supply constrained, because of variety of reasons. And it is important to really have resilience within the supply chain network and then ensure that, continuity and stability is maintained. I can talk a real example of my, organization and experience, right? where, we are trying to produce a product and then it is via constraint. Product, obviously there is a regulation. it's a life sciences industry. There is a regulation and then you cannot just procure it from One supplier, because, because of the regulation, you have to, make the product. And the product is approved only for those suppliers. So you are really constrained to get the material only from that supplier, which is putting back your supply chain and risk, right? AI can help you streamline that to gather all the data, do the assessment, then see if we can optimize our supplier. Supplier network, right? And that's forecasting. this is a big one, right? any point of time, in order to improve your forecasting, some organizations are spending millions of dollars to bring, one point to two point efficiency in their forecasting capability, right? And demand planning is always a challenging aspect within the supply chain because Demands can be, so much. It can be volatile. It can be intermittent. It can be seasonal, right? so it is important for us to play this card of having enhanced forecasting is very important. And then that happens only if you can analyze larger data sets. And analyzing larger data sets is only possible using AI, right? Compliance and fraud detection. as I was talking in the supplier optimization, the same thing applies here in compliance and fraud detection, right? you are, your supplier is certified only to supply one kind of material. And then it is important for us to keep that compliance and quality from a regulation standpoint. If you don't do that, then, you might run into a situation where, FDA or any auditing body is doing the audits, and then you might end up closing your business, which is risk to the, to the nation, to, to the industry, right? So it is very important to, keep an eye on compliance and fraud detection for sure, right? The market growth, right? from within the global supply chain, the projected market growth is 14 billion by 2028. With this rapid advancement, it is very important that We keep AI on our radar and then leverage as much as we can, wherever applicable, to bring back that efficiencies into the game. Yeah, let's deep dive into some of the supply chain challenges, right? data deluge, right? The global data is growing at a rapid pace, right? we have, I think the global data sphere is expected to reach about, 175. Serabytes by 2025, right? With supply chains, just supply chains contributing to 25 percent of this growth, right? How do we manage the data? Obviously, this is one big supply chain challenge what we are running into, right? The second supply chain challenge what we are running into is system complexity, right? Today's supply chain often involve about 15 20 interconnected systems for functions, right? if you take a typical example of any industry who, a midsize industry where you have your core ERP, where you are managing your inventory, where you are managing your procurement processes, where you are managing your order management related processes, it could be data coming from e commerce, It could be data coming from EDI. It could be data coming from your vendors or suppliers or, or your customers. So that's all happening in ERP. And then there are a lot of edge applications which are connected, like your demand planning, your supply planning. And then the moment you get into logistics, logistics again has its own set of applications like picking systems. Packing systems, conveyor systems, a lot of applications on the logistics or transportation systems, right? So there is a system complexity as well, right? and the third one is, demand pressures and shorter service level agreements, right? So the demand pressure, obviously, if you see today, everyone is trying to stay in the business. And then the competition has increased in such a way that they want to, deliver their product as quickly as they can, right? So they are expecting same day delivery, next day delivery, four hours of delivery, right? So how do we manage this kind of data, right? there is so much pressure on the organizations to manage all this data and take timely actions, right? And then finally the COVID impact, the COVID impact, there's so many organizations which have gone, bankrupt because of COVID 19. and that has led into so many challenges. there is a resource shortages, there is a manufacturing shortages, there is, a need for, resilience, right? but then, the impact is so much that it takes its own time to come out of that impact, right? these are some of the typical supply chain challenges, I think, which, Which is creating a havoc in the supply chain and a big hole within the supply chain. And, and obviously there are opportunities in some spaces and there are, challenges in some spaces, I think. But overall, these are the supply chain challenges, what we have within the supply chain. Okay, we have seen a variety of challenges in the previous slide, right? Let's AI can help us address some of these challenges, right? Let's start from the data management, right? AI can handle real time data processing, enabling fast and accurate decision making, right? so this is a big use case, which is happening today. we can take an example of, the way the chat GPT have evolved or the way, Gen AI has evolved, within the supply chain. Today, there are so many applications, which, which are trying to. help supply chain with the help of these new technologies which have come in, right? Gen AI, like for example, Gen AI today is capable enough to give you information about, which can, which it can scrape the entire data set from various sources, right? And then it can tell you, saying you are good at these items. You are bad at these items and then, the data maintenance can be done. within that scope, right? And not just that from a DevOps standpoint, the overall the way the cloud infrastructure is built to, which can help to do auto scaling, right? and also there are the whole data lakes, snowflakes, and then, all these various systems which are available are capable enough to really manage the data from multiple sources and then and store them and then, And then consume as needed, right? So data management is a big one, which AI is addressing today, right? The second one is the system integration, right? the AI is supporting, the seamless, data flow and communication across the interconnected systems. And during operations remain synchronized, right? there are a lot of times, in, in a typical core ERP world where, you take example of any big player in the market today, right? where you're trying to gather all the data from one system to another system, it takes about four to six hours. And then you run your processes on that system, which takes about another four to six hours. and then you cleanse it. And then there are so many jobs, hundreds of jobs which are running behind the scenes. And then next day morning, the system is given to the business user to do day-to-day operation. Which is a big delay in the overall, availability of the data today. It is given in less than one hour, the same jobs which are running today because they're all scaled up in such a way that they're pretty much in the cloud, right? and then also the way the data is consumed is happening more of targeted refreshes, right? As it is all getting into the cloud. More of microservices model. So the data can be collected and the data can be, or transmitted in a much more synchronized way, right? Predictive insights, right? AI, obviously, as I said in one of the previous slides, right? Improving one point efficiency, two point efficiency, right? Would save millions of dollars to the organization. Today, AI can help you identify the anomalies, right? AI can help you identify the data patterns, right? It can see that you had a sale last month, but you did had a sale Same time last year, right? That does not mean that you will have a sale next year, right? That's just one anomaly, right? So and then not just that right? I mean considering your situations within the Demand or supply like for example, if we talk about safety stocks, right? how do you plan your safety stocks? Are you really red green yellow, right? It is giving you AI is pretty much giving you anomalies on where to get, which to get, right? And which customer to fulfill first from an allocation standpoint. And then at the same time, which distribution center should have this product when? AI is giving you all that predictive insights. Which is bringing back that resilience again, right? this is not just like one place, kind of impact here. The overall supply chain has to be impacted, right? from the procuring of the product to the finished good delivery to the customer. So AI is playing, a role right from the data to the integration to the giving the insight and then building the resilience across the supply chain. so far we have seen a lot of, challenges, within the supply chain and other things. Let's dig deeper on the AI applications within the supply chains and how AI can, be helpful. within the scope of the supply chain, right? There are a variety of, use cases there, but I think we are planning to talk about five or six use cases here, functional areas where AI can help, within these supply chain applications, right? Office demand sensing is one critical one. Demand sensing is basically, where you're trying to forecast your, short term, on the product, on the, on the SKUs, which are more of a short term in nature, right? For the longer term and the medium term, there are a lot of applications which are available in the market. But then for the shorter term, where the demand is so much volatile and so much intermittent, it becomes really challenging to, Get to real forecast accuracy, right? And then, and that is where demand sensing is coming into play. And that is where AI is coming into play, right? Where you have, where you need capability to. Plan Short term like I mean it could be a week. It could be a month It could be three months right within the demand control window, right? So so that's where ai is coming into play and then it is helping you to do demand sensing and obviously In order to do this demand sensing, you need a lot of data feeds which will go into the applications. But then AI has capability to consume all this information and then provide you much more realistic, closer forecast accuracy. That is where demand sensing is coming into play. The second one is the cost reduction, right? a lot of times what happens is when you bring a container from Asia, when you bring a container from, any other parts of the world to Rest of the parts of the world, right? So there are a lot of times there is, so much fluctuation in the logistics industry and then also not just fluctuation in the logistic industry, there is fluctuation in the material cost as well, right? It is all about the supply and demand situation, right? So AI has capability. which can help, which can help to take all those inputs and then come back with a cost prediction analysis, which will help, industries to do their planning and budgeting in the right way, right? The other one is supplier network stabilization, right? as I said, I think we did talk through this in the previous slides. Supplier network stabilization is a very key one, right? there are a lot of instances where You want to procure material only from a supplier or you want to, do a dual sourcing kind of thing where you're not putting your industry or you're not putting your organization in risk, right? You need to have some mechanism. Where you can get the material from various suppliers as long as you meet the regulation and the compliance, right? Fraud detection again, this is a critical one We are living in a world where pretty much everyone is relying on Internet and wherever there is access to internet Risk comes into play, right? So it is very important to make sure The fraud detection and cyber security are maintained and AI really play a critical role in doing the fraud detection as well, right? The last one is data and the compliance management, even though it's listed last year. It is very Important that we need to understand without data There is nothing we can do right for a planning function to work for a supply chain function to work And Master data is the critical element in order to make sure the master data is all right. There is so much responsibility on the systems. There is so much responsibility on the business users to make sure that data is maintained. It is all about garbage in garbage out. If you don't maintain your data, all your outputs will be all, wrong. so it is very important to make sure you maintain your data. and AI is really playing a critical role here in maintaining the data, right? Today, the way the industry has revolutionized, if there is data disconnects, or if, if we take an example of safety stock, right? if AI can take that decision or AI can take the data point and then can give you back a decision saying this is how the safety stock should be configured, right? When you were selling this product, last year, this much quantity, this year, this much quantity, you probably will sell this much quantity next year. So in order to maintain that much inventory, you need to have X amount of safety stock within your system. And all of this is maintained based off of the master data. So it is very important that you maintain, Your master data accurately so that all your systems can give you relevant outputs. And that is where AI will play a critical role. So we have seen how AI can make an impact on the supply chain, right? quantitatively, your forecast accuracy. AI powered models improve forecast accuracy by up to 60 percent supporting the demand planning function and the stock management. It also helps you in inventory cost reduction, right? with the help of the demand sensing, it reduces your inventory cost by 20 to 30% to having better inventory optimization. It also helps you in improve decision making. 75% or 73% of professionals report faster, more accurate decisions due to ai, right? Because there is real time data, which is getting available, so it is much easier and quicker to make improved decisions. And then all of this would lead to, carrying cost reduction, right? You don't need to carry too much material in your warehouse or in your distribution center. So obviously some of these autonomous decisions are helping you reduce your carrying costs. That means you are getting into more of a lean supply chain model, which would help you reduce your carrying costs. So we did see the impact on the overall supply chain, but then there is also impact overall on the broader enterprise operations item in each organization, would have variety of, enterprise operations, right? So there is a procurement team. There is a planning team. There is a military planning team. There is an auto management team or customer service team. There's a manufacturing team distribution scene, it team cyber protection and then threat detection teams. There is a, Risk mitigation teams, a lot of teams, operating and then within the ecosystem of enterprise resources and enterprise operations, right? AI is playing a broader role and then it is really helping around, around your inventory planning. Obviously, it helps your inventory, optimize your inventory levels, reducing excess by 20 to 30%. It is very important that to manage your inventory, excess and obsolescence is a big item where You need to have right inventory of the right product, right? A lot of times, all of this would run into a situation where you are doing the right segmentation. Only when you do right segmentation of your inventory, that is when you will have optimum inventory levels, right? having the right product, at the right time to the, then only you'll be able to give it to the right customer. So inventory planning is one, one critical one. The second one is order planning, right? Order planning is something. Which is very important, right? when an order comes in, it could be an EDI order. It could be an e commerce order. It doesn't matter, right? At the end of the day, the order comes into the warehouse. Now, someone needs to Allocate the order, someone needs to plan the order and someone needs to take it and pack it and ship it out to the customer, right? All of this life cycle would be really critical for any function to operate, right? So order planning is very critical from a, from an overall, AI standpoint, right? It, AI helps you, increase your order fulfillment accuracy by 95%. There are a lot of warehouses. Who are leveraging a lot of autonomous picking models and autonomous packing systems where The picking can happen a robotic system can come and pick up the product and then drop it into the right Location so that the packing system can pick it and then pack it and then So put it at the FedEx doc or the UPS doc, right? So order planning is also one critical thing. Not just that, giving visibility, giving visibility to the customer service team, right? we are living in a world where we are operating with the help of a lot of contract manufacturers and so on and so forth, right? A lot of times where a product is moved from one place to another place, it is sourced from Asia, it is sourced from Mexico, so it is sourced internally within. But in the, country, right? So there is a movement of the product from one place to another place. And it is very important for the customer service team to have visibility of where that product is really right now, right? the container could be sitting in the dark. But then the customer service has no visibility to that and then we will not be able to really do proper order planning. AI is really playing a critical role where you can do your supply sensing and then you can have real visibility of the data and eventually, customer service can have visibility of the data and then they should be able to allocate and then do scheduling of those orders, right? will it really play a critical role in a B2C industry? Yes, it will. Both B2B and B2C industry order planning. as long as your volume of the orders are critical, AI will definitely play a role in order planning. Capacity planning is other big aspect, right? a lot of times when a product is planned or when a product is manufactured, you need to ensure all your resources which are going into the plant or into the capacity are required. accurately measure, right? a lot of times you run into a bottleneck situation where one equipment is loaded heavily, one resource is loaded heavily, and all of this is very data critical thing, right? So AI will come into play here and then it will help you understand what is your resource modeling is, understand what is your, Equipment modeling is understand what is your material modeling is and do an intersection of all of this data and then come back with a capacity planning model, which is more optimized. And then in turn, it will help you boost your production and resource utilization. Finally, the cyber security and threat detection is a big one. Obviously, you can control all of that within the supply chain with the help of AI systems and make sure your supply chain is. managed in the right way and is stable and cost efficient way. Okay. We got to the conclusion slide. So overall, I know we went through the challenges, what applications will be able to help within the AI space. We didn't go through all that. And then, conclusion, right? AI definitely will help you enhance the resilience and efficiency, right? I mean, once that efficiency and resilience is maintained, it will help you, keep up your position. There is so much competitive advantage, which you need to maintain and sustain your position, right? definitely adopting all of these capabilities, leveraging some of these capabilities, what AI offers definitely makes you gives you that competitive advantage in the market and in the industry, right? And then the last one, the future potential as AI evolves, its role is resilience and maintaining that agility and cost management will continue to grow with these applications and predictive maintenance also can be done. So that's one critical one. And the takeaway, finally, is AI is essential for developing adaptable, robust supply chains that meet the challenges of the modern global economy, right? So AI is part of our day to day has become part of our day to day and it is important for us to leverage and, and use it in a right way wherever applicable. With that, I would like to conclude and, would also like to, thank, conf42. com and, for giving me this opportunity to share about AI and, it's, intricacies, right? I'm happy to take any questions if there are any. Thank you for the opportunity. Appreciate it.