 Hello everyone, welcome to this course on supply chain digitization. This course is taught by faculty from IA Mumbai including myself, Professor Priyanka Varma and my colleagues, Professor Sushmita and Professor Deva Brathadas. So in the last session, we have discussed about different technologies that gets covered under industry 4.0, we are going to extend our discussion on that. If you remember this figure in the last session, we have discussed primarily about the 3 pillars of industry 4.0, which includes IIoT that is industrial internet of things platforms and we have focused primarily on internet of things and then we have seen about the different type of IoT and their usages in detail. We also have discussed about cloud computing and the different models of cloud computing and how it can be implemented and this is further extended to the strategies of integration across the horizontal system and across the vertical system. We are going to extend our discussions to the remaining pillars of industry 4.0 and the next and very important pillar which we are going to start is cyber security. So as we understand the meaning of cyber security, we understand that IoT helped us in capturing the data, the cloud came computing helped us in sharing and analyzing the data in a collective format whereas horizontal and vertical system integration helped in deciding the right way of integrating the overall processes and cyber security is the next element in the as per the pillars of industry 4.0 which will be responsible for taking care of the security of the data which is getting shared between the different partners in a given supply chain. So let us see that what do you mean by cyber security and what are the different elements attached to it. When we talk about cyber security, we see that cyber security plays a very critical role. The most important role is about the risk assessment and we understand that there is a large amount of data which is shared between the different partners in different formats. So there can be a security threat. It is very important that the system helps us in understanding the security threat and accordingly the right type of risk assessment can be done so that the security threat can be handled in a better way and in advance as well. The second when very important role that cyber security plays is about design and the end point security. When we talk about design, it means the design of the security and here this means that how the security features are integrated into the design and development of the industrial systems. It is very important that we consider the security features such as encryption, authentication and access controls into all the related hardware, software and the network connect which is getting installed into the system. So design security takes care of these issues whereas when we talk about the end point security. The end point security is something about the last points which includes your sometimes your sensors, sometimes your actuators and sometimes your control devices which we use it for capturing different type of data. So it is very important that we take care of their security and how that needs to be taken care. So this is done through installing proper type of anti-virus software and so on. So here the design is all about the overall security system and the end point security is all about the last points from where the data is getting captured. So how the security element is taking care at these two elements gets covered under cyber security parallely. The next is continuous monitoring. The most important role of cyber security as a pillar of industry 4.0 is to ensure that the data which is getting shared between the different partners is continuously monitored and as and when needed the safety steps which are required to be taken is planned and accordingly the strategy for the same can be executed. Similarly, this gets connected with the detection of threat. The continuous monitoring if it is done properly the threat detection can be identified and accordingly the steps for managing these threats can be accordingly planned. This is again linked to responding to the incidents that is identified and again this is the next and very important role of the cyber security pillar. And lastly the all the activities which are done while data sharing needs to be having a compliance as per the industry standards there has to be regulation for the same. So this also needs to be followed. So we understand that cyber security takes care of all the security related issues with respect to the data which is being shared and also it helps in identifying the potential cyber security threats and accordingly acting on it as per the requirement. When we talk about the technologies which are associated with the cyber security depending upon where this needs to be installed these technologies can be selected. Some of the technologies we already discussed while discussing about the role of cyber security. So these technology may include like firewalls, encryption, the endpoint protection, inclusion detection system, inclusion prevention system, cloud security and next generation antivirus. There can be more than this as well but these are some of the well known technologies which are being used while implementing the cyber security in any industry 4.0 digital framework. Continuing to our 9 pillars of industry 4.0 our next and very important pillar is big data and analytics and here we can see that once we have got our data the next step is to analyze the data. But let us try to understand what is big data and how we can analyze this particular big data. When we talk about big data, big data are large and complex data sets. It can include some structured data, some type of semi-structured data or unstructured data parallely. This data can be collected from various sources and that is why it introduces lot of variable type of data in your overall data set. So here we can see that the data which has been captured is large in size as well as it is complex in nature and of different type and this all together brings the complexity for analyzing this data. So here when we talk about big data analytics it is actually referring to the different advance analytical techniques that is being introduced in today's time and these can be appropriately used for analyzing this particular big data. The big data analytics plays a very critical role because when we talk about taking a real-time decision making this can be done very effectively with the help of big data analytics. Now very importantly there are four characteristics of big data and this is important for us to understand so that we can accordingly act on that. These four characteristics of big data are referred as volume, variety, velocity and veracity. Let us try to understand each characteristics one by one. So when we talk about the volume of the data this volume is actually the data which has got enormous magnitude and it is not your data which can be analyzed in a routine way that we do day in day out. This is a large volume of data and for analyzing this type of data the traditional tools are not possible. The next type of characteristics is the variety as we already referred that variety is a integral element of big data as we are capturing different types of data from different sources. So your type of data can be some video files, some audio files, some text files and so on. So big data generally comprises the combinations of different types of data and that is why this characteristic is very important and it brings complexity into your big data as the data being captured is of a different variety. The next characteristic is velocity. As we understand that big data is actually it requires immediate or near immediate processing so that you can utilize or you can realize their maximum point from it. So here the velocity means that the data is continuously being generated and that needs to be analyzed. The last one is veracity. Again if you refer to this term veracity refers to the degree of quality and reliability that can be attributed to a data set. So with the big data it is required that the data that has been considered is reliable and the quality of the data is also appropriate. So then only the analysis that is being done on that can be properly trusted. The next question that comes is how can we analyze this data? There are different ways of analyzing the data but as we always follow our first step starts with the data visualization where we have lot of different ways of analyzing the data in terms of presenting this data in appropriate form of tables, graphs or charts. This we have also seen in our previous sessions that how a given data is presented. So we can apply some appropriate data visualization tools to see the data in the first step. The next step is about using appropriate machine learning algorithms depending upon the purpose of the business statements. You can use a combination of descriptive, predictive, prescriptive and other type of analytics methods over here to analyze the data. The last one is as is related to the real time analytics as we all understand that big data needs to be analyzed on a real time basis and for that the appropriate approaches are there and this gets covered under real time analytics. So far we have covered IoT, cloud computing, horizontal and vertical system integration, cyber security, big data and analytics. The next pillar is again very important and that is called as simulation. So let us try to understand what is simulation? When we talk about simulation, simulation is nothing but it is a way by which we can replicate the any industrial process, system or environment. So the benefit of simulation is that you are not expected to work on a real situation instead of that create a model or replicate the real life scenario in form of a mathematical model which can be analyzed subsequently and it can help you in analyzing the complex scenarios particularly in the virtual environment. The benefit of analyzing this model in a virtual environment will be immense and that will help you in ensuring that the right method or strategies are selected for the real time solutions. You can play with your simulation model, you can test it in different scenarios, you can check the model with different parameters, optimize it and accordingly take the right decision. So that is why simulation is again playing a very critical role in taking or in planning for the real time situations by working on the hypothetical model which has been developed by replicating the original scenario. A very interesting way of doing this is through digital twin and this is very famous and the proper way of doing simulation in industry 4.0 with the help of digital twin. Our next pillar of industry 4.0 is augmented reality or also called as AR in short. So augmented reality and virtual reality are often used in today's time let us try to understand what is augmented reality. So when we talk about augmented reality this is nothing but it is a transformative technology and importantly it is characteristic is to enrich the real world experience. What it does is that it overlays the digital information on to the physical surroundings. So this is some experience which we see in different scenarios in our today's experiences we will try to see this in more detail. The feature of augmented reality are integration, interactivity and real time when we talk about integration it means that how AR is seamlessly it is merged with the virtual and real world. So that is one of the critical characteristic of augmented reality in terms of interactivity it helps the user to enhance his engagement through the interactive digital elements and thus giving a better experience. And the last and very important feature is about its action on a real time basis. So any dynamic adjustments can be done based on the user's environment or its requirements. So these are like some critical features of augmented reality these features can be appropriately selected by the user while devising their own digital adoption plan. About its application it has got its huge application in gaming, education, healthcare, manufacturing and maintenance. As we all know in gaming we all must have used this augmented reality technology and similarly it is playing a critical role in education by giving some type of interactive test book and so on. In healthcare again it is gaining attention because of the possibility of developing some medical training simulations and some surgical assistance. And in manufacturing and maintenance we know that there are certain AR guided assembly instructions or some equipment maintenance support which can help in doing some complex task in the manufacturing and the maintenance environment as well. Talking about the challenges of augmented reality the privacy concerns are there similarly sometimes there are certain hardware limitations. So even though you wanted to implement it but because of the availability of the technologies or the hardware limitations the implementation of these technologies become little difficult. The next challenge is about interoperability means that the seamless integration across the various AR platforms and devices can be challenging sometimes and may be because of this it can lead to certain compatibility issues and lastly sometimes it can also lead to safety concerns. So these are the certain challenges related to augmented reality. Going forward our next pillar of industry 4.0 is autonomous robots or and we know that these are very important pillar reason being it has got huge attention in today's time where many activities in supply chains can be done in a very effective way with the help of autonomous robots. So let us try to understand that what is autonomous robot in terms of the role most importantly the expectation from the autonomous robot is that they can perform a given task or they can complete the given job independently. It means that the human intervention or the control that is required is not on a continuous basis but just by simple monitoring this can be done in a effective way. So autonomous robots are expected to perform a task or complete the job in a independent way. When we talk about the components of autonomous robots it has got a sensor, a control system and actuators. The sensors are something which is actually capturing the requirement or finding out the related data and this can be done by having sensors such as cameras, some ultrasonic sensor or a gyroscope which is playing the role of collecting the data from the environment. When we talk about the control system what it does is that the data that has been captured through the sensors are used in taking decisions based on certain programming or certain algorithms. And lastly the actuators means the action, here the mechanisms or motors are there which allows the robots to move and interact with the world depending upon the requirements. So we can see that the autonomous robots have got these 3 components. The stage 1 is actually used for collecting the data, the stage 2 that is the control system is actually about analyzing the data and taking decisions based on that and the last stage actuators is nothing but the action thing which is depending upon the requirement the autonomous robots are instructed to do that particular task. Talking about its application, it has got huge application in manufacturing, warehousing and logistics, healthcare, aerospace and agriculture. Let us try to understand a simple application in manufacturing. So robots are importantly used in today's time during the vehicle assembly where the welding process are required to be done with precision and this intricate job can be done very effectively with the help of autonomous robots at the automobile assembly point. Similarly, in logistics and warehousing when we are aiming for making a modern warehouse the robots can play a critical role for ensuring that your order fulfillment efficiency is maximized. These autonomous robots can easily navigate warehouses for retrieving and for transferring goods and ultimately helping you in improving the overall efficiency. In a similar way they can also be applied in healthcare, aerospace and agriculture. The last pillar of industry 4.0 is additive manufacturing and again this is one of the very interesting technology advancement that has happened and is also it is going to significantly impact the way the manufacturing is going to change. Additive manufacturing is also referred as 3D printing. Reason being in this process the designs which are developed typically these are the digital designs they are transformed into models with the help of 3D printers. Now the product which are made using 3D printers are the objects which are created layer by layer. So instead of the traditional manufacturing where the materials are removed, here in additive manufacturing the intricate designs are formed based on the digital designs that is required but in a layer by layer manner. So there are some very famous techniques which are available under additive manufacturing like fuse deposition modeling, stereolithography, selective laser sintering and there are many more. So here just for reference we have discussed few of them and but depending upon the type of the machining requirement and the type of the material these techniques will change. In terms of the process of additive manufacturing the first stage is about 3D model development. Now in this process a 3D model is sliced into thin cross sectional layers specially by using some specialized software. So we make the model of the product which are required to be manufactured and a 3D model of it is done and typically the it is sliced into some thin cross sectional layers. Now this becomes the guiding path for the 3D printer we can say. Here in the 3D printer what it does it tries to build that particular object but in a layer by layer manner and as the name suggests it is additive it is additive. So the layer by layer printing of the final product happens and this is based on the design or the slices of the design which is created in the 3D model step. The next step is about the finding out the right AM technology which will depend upon the material to be used whether you are using a plastic, metal, ceramic or composite depending on that the right AM technology or additive manufacturing technologies are selected over here. Once the product is made you need to process that products that is covered into the post processing step. In post processing step we do methods like curing, polishing or assembly which will depend upon the application or the product nature. Most importantly as we can understand the benefit of this additive manufacturing is it is useful in developing some intricate and customized geometries which are not possible in that additional manufacturing. Finally when we talk about its application it is gaining huge interest particularly in the field of aerospace where light weight products are required to be manufactured in automobile where intricate and complex designs are required to be done in healthcare again for developing some customized products for the patients requirements in the construction industries as it can help in saving time and efforts. And finally in art and design particularly in making jewelries of intricate design complexity and different shapes and size. So we can see that so far we have covered all the 9 pillars of industry 4.0 and we have gone into the details of each pillar we have tried to understand their roles and their different techniques which are available under these methods and also how they need to be executed, what are their challenges and what are their possible applications. So with this we will be closing the discussions on industry 4.0 technologies and hope to see you all soon in the next session. Thank you everyone have a nice day.