Hi, everyone. Good morning. My name is Karthik Swachhanna. Today I'm excited to talk about hyper automation in SAP production planning in Con 42 DevSecOps 24. I would like to just briefly give a couple of details about me. So I'm a senior SAP delivery lead with my interest leading to a digitalization of supply chain process. My focus is on changing the way companies operate and utilize technology. So tomorrow's vision can become today's reality. It's been a 15 year passion that has taken me around the world and continues to deliver results with the most competitive markets. I have received Titan Business and Innovation Award, Global Leadership and Business Transformation Award, and I have been judged in many conferences. Now going back to the topic of Hyper Automation and SAP Production Planning, below are the table of contents. We'll go through the key components, benefits, case study. impact on production planning, technologies powering hyperautomation, challenges and ethical considerations, the future of SAP, PP and hyperautomation. So going back into the introduction of hyperautomation. Hyperautomation is more than just process automation. It integrates cutting edge technologies like AI, machine learning ML, robotic process automation, RPA and process mining to create a fully automated intelligent production planning system. Before diving into the details, let us consider this. Gartner predicts that the market for hyper automation enabling software will grow to 860 billion by 2025. 860 billion is too much. Underscoring its transformation potential. So how does hyper automation revolutionize SAP production planning and what does it mean for manufacturing operations? So let us explore the key components of Hyper automation. So the key components are RPA, AI, ML, and process mining. Hyper automation integrates several powerful tools. Let break, let's break down the key components. Robotic process automation. So RPA automates repetitive rule based tasks such as data entry and order processing. It not only accelerates process by up to 70%, but also reduces human error significantly. We all are worried about human errors. Artificial intelligence. AI leverages production data to provide predictive insights. For example, AI can forecast demand with up to 95 percent accuracy, enabling better production alignment. The next one is machine learning, ML. ML models adapt dynamically to changing production requirements. These models optimize production schedules, improving efficiency by around 20%. The next one is process mining. This technology identifies inefficiencies within the workflows. For example, process mining has been shown to reduce production cycle times by up to 15 percent by pinpointing bottlenecks and suggesting improvements. So the next topic is what are the benefits of hyper automation? Now let us discuss the tangible benefits, efficiency gains, accuracy improvements, and real time insights. So on efficiency gains by automating manual process, hyper automation, Reduces lead time by 25 to 40 percent and improves resource utilization by 20 to 30%. Accuracy improvements. Forecasting becomes more precise with accuracy improving by 15 to 40%, while inventory levels can be reduced by up to 25%. Forecasting is one of the key components in manufacturing. In any production facility, The demand is always generated by doing a forecasting. Salespeople will go about doing forecasting and they will give the demand to the manufacturing, leads and the manufacturing leads will, pass it on to the floor operatives where they will try to meet the demand by manufacturing the required product. Real time insights. IoT devices and advanced analytics provide real time monitoring. For instance, Downtime can be minimized by 25 to 45 percent and detection and response times are reduced by 50 percent. Ultimately, hyper automation drives faster, more accurate and flexible production planning, allows organizations to adapt to shifting market demands. So these are the benefits. Now going into a high level case study. So let's look at a case study to see hyper automation in action. A leading automotive manufacturer faced challenges with a complex production ecosystem by managing the resources and on time deliveries. Their solution included RPA to automate scheduling and reduce manual workload, AI for predictive maintenance which achieved a 90 percent accuracy in predicting equipment failures, ML to optimize production schedules based on historical data, process mining. To identify workflow inefficiencies and reduce unplanned downtime by 35 percent. The results a streamlined production planning, enhanced resource management and higher on time delivery rates. This case demonstrates how hyper automation can drive measurable impacts. So going back to the impacts on production planning. So on the efficiency side, automation reduces lead times and Optimizes production schedules, accuracy, AI and ML enhances forecasting, reducing errors and aligning production with the demand. As we already discussed, forecasting is related to demand and insights will provide analytics on how to dynamically adjust and do a predictive maintenance. while we do, while, the production planning, all while I'm on, the manufacturing, floor, I always see that the plant maintenance guys used to, do some maintenance in manufacturing. The middle of the production line stopping it So that they will stop the entire manufacturing process and do some maintenance and you know They will take the test downtime and they will discuss in their You know tpm regular meetings on how can we do or How can we manage our manufacturing even more efficiently by reducing the downtime? So these are some of the insights that we can get by using the hyper automation what are the key points? Equipment failure prediction, accuracy is 90%, unplanned downtime reduction is 35%. Now, technology is powering hyperautomation as we say RPI. it automates MRP runs. MRP is one of the key component. MRP is nothing but material requirement planning. We will try to monitor how much material is required to. Produce a finished good and how can we plan so that we will not have stock deficiency during our manufacturing or Producing a particular component or you know any end product, right? So by using RPA it reduces manual data handling by 70 percent and minimize errors by 90 percent so AI It predicts and enhances maintenance scheduling. So we, there is always a maintenance, usually every manufacturing productions facilities will have an eight hour or 12 hour shifts and they will have manufacturing, sorry, maintenance down times, and they will schedule it and do some maintenance. They might apply oil or tighten some screws or, some kinds of, maintenance, right? By using the ai, we can predict what can be the maintenance schedule. And by using ML, we can do some adapt, adaptive learning for better production scheduling and anomaly detection. There, there will be always some anomalies like, if you are preparing a food or, if you are trying to, make a car or, any kind of manufacturing facility, there will always be some kind of anomaly that will be detected by using this ML. We can do an adaptive learning and learning on the way on the go so that we can understand what these Assemblies are by using process mining we can identify process inefficiencies what is causing process inefficiencies and how can we use this process mining for our continuous improvement. So statistics say that process mining can reduce production Cycle time by up to 15 percent. So coming to the challenges and ethical considerations We always have challenges like any disruptive technology hyper automation faces some challenges So when I always speak to the steering committee or, when I speak with the vice President of manufacturing or, CEO of, any company who has a manufacturing facility and who is the head of, manufacturing, they always say that we always have concerns about data privacy. And, they always have concerns about integration. Why? Because, on one side we are going to use machines on one side. We are going to use. Software so software and hardware combination in integrating both of them is a very complex process as we all know there is something called as manufacturing Engineering systems or mes where it can adopt or Integrate into any kind of different software technologies and make sure that the manufacturing is going smooth without causing any kind of issues So then that is One way where we can see that there is an integrate, sorry, integration with complex manufacturing systems is always an big issue and multiple challenges are faced at that end. And on the other hand, there is something called something with data privacy, always there is, data about customer or a vendor or, some kind of financial data. so most of these executives, right? 31 percent of them, are worried about the sensitive production and customer data often involved in, the, Manufacturing facilities. So these are a couple of the challenges. So now what is the future of SAP PP and hyper automation looking ahead hyper automation said to become a corner store of industry 4. 0 Which is the latest? Advancement in AI and ML will unlock new possibilities in production planning from self optimizing systems to fully autonomous manufacturing execution. With a projected market growth of 18. 7 percent CAGR, as I said before, initially reaching 26. 5 billion by 2026. The adoption of hyper automation technologies is not just a trend, it is a necessity. Even in our company when, where I'm working, they are moving to Smart Factory where everything is, controlled by iot, internet of Things. there is a, manufacturing, head who is more, investing, the company's resources into this. hyper automation where we can integrate multiple technologies so that he can stay ahead in the competitive fast evolving manufacturing landscape. So now the last and final, conclusion. Hyper automation integrates cutting edge technologies to transform SAP production planning. It drives sustainable efficiency gains, cost reduction, and better decision making. Organizations need to implement hyper automation because they can transform their operations, turning tomorrow's vision into today's reality. I always thank you once again to all the steering committee and all the chair committee for giving me this opportunity. Thank you.