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Ravi can row a certain distance downstream in 8 hours and can return the same distance in 12 hours. If the stream flows at the rate of 4 kmph , then find the speed of Ravi in still water? Ques 6. A man speed in still water is 15 kmph , while river is flowing with a speed of 3 kmph and time taken to cover a certain distance upstream is 3 hours more than taken to cover the same distance downstream.
Find the distance. Ques 7. Raghu rows in still water with a speed of 4. Find his average speed for the whole journey , if the river is flowing with a speed of 1. Labels: quant. Formula 1: If the ratio of downstream and upstream speeds is D: U, then ratio of time taken will be U : D. Question: A boat running upstream takes 8 hours 48 minutes to cover a certain distance, while it takes 4 hours to cover the same distance running downstream.
What is the ratio between the speed of the boat and speed of the water current respectively? Solution: When 8 hours 48 minutes is written using mixed fractions,. Therefore, the ratio of the time taken becomes Which on solving gives, Now, as the ratio of time is inversely proportional to that of Upstream and Downstream speeds. Now, the ratio of speed of boat and the speed of stream will be. How far is the place? Solution: Take out the formula book.
A man rows a certain distance downstream in X hrs and returns the same distance in Y hrs. Question: Ramesh can row a certain distance downstream in 6 hrs and returns the same distance in 9 hrs. Businesses with comprehensive Industry 4. Digital transformation, although being academically looked upon and despite the existence of numerous digital transformation frameworks and roadmap strategies, which are developed by numerous people, has no universal definition nor clear industry-wide approach.
The same goes for the implementation of the Industrial Internet of Things. Just like digital transformation and the Industrial Internet of Things, adoption of Industrie 4.
However, Industry 4. In essence this means that in Industry 4. That is pretty unique. So, just like digital transformation, Industry 4. Yet, as opposed to digital transformation this vision and reality is far more studied, documented and standardized despite the mentioned need to work in the context of the individual business as well. In the Industry 4. One such maturity approach looks at the information and actual operations and manufacturing systems perspective with autonomous machines and systems as true Industry 4.
In this gradual approach, whereby each stage builds upon the next one and adds more value, we move from data to information to knowledge to wisdom and action from a data perspective. Indeed, the good old DIKW model. A second maturity approach revolves more around the business as such and corresponds with what you would typically see in any project. What do we want to achieve and what do we have today assess , where do we want to go and what are the missing links to get there called the methodological analysis in Industrie 4.
While some organizations have more or less consistent and holistic strategies with regards to Industry 4. A lack of strategy proves to be one of the major challenges as it does in so many business areas. There is a big gap between those companies that have a long-term strategy and the rest. Changing demands play a role as well as do financial and competitive reasons. Anyway, in the scope of Industry 4.
And quite often they are not, leading to fewer results than hoped or even failure. In a Industry 4. The fast pace of disruption in Industry 4. However, the survey suggests leaders have a long way to go.
Two-thirds said they have no formal strategy to address Industry 4. Cyber-physical systems CPS are building blocks in Industry 4. Cyber-physical systems are combinations of intelligent physical components, objects and systems with embedded computing and storage possibilities, which get connected through networks and are the enablers of the smart factory concept of Industry 4.
Simply put, as the term indicates, cyber-physical systems refers to the bridging of digital cyber and physical in an industrial context.
This might still seem complex but, then again, cyber-physical systems are complex. So, if you want to understand Industry 4. Cyber-physical systems in the Industry 4. Looking at Industry 4. Cyber-physical systems essentially enable us to make industrial systems capable to communicate and network them, which then adds to existing manufacturing possibilities.
They result to new possibilities in areas such as structural health monitoring, track and trace, remote diagnosis, remote services, remote control, condition monitoring, systems health monitoring and so forth. In the original definitions, going back over a decade, IP addresses where not specifically mentioned in cyber-physical systems.
In , Professor Edward A. On his page on the Berkeley website , Professor Lee links to cyberphysicalsystems. For the German Industrie 4. Cyber-physical systems also include dimensions of simulation and twin models, smart analytics, self-awareness self-configuration and more.
Hopefully, the essence of the concept, context and reality of the evolution towards cyber-physical systems has become a bit clearer now. Note: there is a difference between cyber-physical systems and cyber-physical manufacturing systems or cyber-physical production systems CPSS where we move from the technological component to the far more important process and application dimension.
Next, we take a deeper look into the Internet of Things and its place in Industry 4. Before doing so we summarize some key characteristics of cyber-physical systems as they are related with the Internet of Things:. As promised, time for the Internet of Things. The presence of an IP address by definition means that cyber-physical systems, as objects, are connected to the Internet of Things.
An IP address also means that the cyber-physical system can be uniquely identified within the network. This is a key characteristic of the Internet of Things as well.
Cyber-physical systems are also equipped with sensors, actuators and all the other elements which are part of the Internet of Things. Cyber-physical systems, just like the Internet of Things need connectivity. The exact connectivity technologies which are needed depend on the context in both.
The Internet of Things consists of objects with embedded or attached technologies that enable them to sense data, collect them and send them for a specific purpose. This data as such is just the beginning, the real value starts when analyzing and acting upon them, in the scope of the IoT project goal. All this applies to cyber-physical systems as well, which are essentially connected objects. There are more similar characteristics but you see how much there is in common already.
Moreover, the new capabilities which are enabled by cyber-physical systems, such as structural health monitoring, track and trace and so forth are essentially what we call Internet of Things use cases.
In other words: what you can do with the Internet of Things. Some of them are used in a cross-industry way, beyond manufacturing. Below are two examples of CPS-enabled capabilities we tackled previously and how they really are IoT uses cases. Track and trace possibilities in practice lead to multiple IoT use cases in, among others, healthcare, logistics, warehousing, shipping, mining and even in consumer-oriented Internet of Things use cases. There are ample applications of the latter with numerous solutions and technologies.
You can track and trace your skateboard, your pets, anything really, using IoT. Structural health monitoring is also omnipresent, mainly across industries such as engineering, building maintenance, facility management, etc. With the right sensors and systems you can monitor the structural health of all kinds of objects, from bridges and objects in buildings to the production assets and cyber-physical assets in manufacturing and Industry 4.
The new capabilities, of which we just mentioned two and which are possible thanks to CPS in the Industry 4. What is a core enabler of smart logistics and so forth? You can perfectly compare this with the Internet of Everything view of connected objects, people, processes and data as the building blocks of smart applications.
It is another key similarity between the CPS view of industry 4. To conclude: in fact, you can call cyber-physical systems the albeit advanced things in the Industrial Internet of Things in manufacturing.
RAMI 4. Even if some EU countries use different terms such as intelligent factory, future industry, digital production or smart manufacturing, the European Commission EC is also intervening. The 3-dimensional RAMI 4. An overview of the ongoing acceptance and leverage of Industrie 4.
If you are looking for some examples of Industry 4. Click on a place on the map and read more about the specific case for now only German examples. What are some of the key aspects you need to know about RAMI 4.
First, know that there are two documents which laid out the foundations of Industry 4. The hierarchy dimension consists of 7 aggregation levels , being 1 the connected world, 2 the enterprise, 3 work centers, 4 stations or machines , 5 control devices, 6 field devices sensor and actuators and 7 products.
In the pyramid that shows Industry 3. The hierarchy dimension is what we covered several times in our articles on ubiquitous connectivity and digital transformation but in a different scope of hierarchy with smart products and smart factories as part of this connected world.
It also about technologies where we similar decentralizations all across the board IT and especially OT and about the ubiquitous interaction of participants across hierarchy levels, whereby the product is seen as part of the network. The life cycle and value stream dimension, as the term already describes, covers the various data mapping stages across relevant life cycles in RAMI 4. The idea: the more data early on, the more value later on.
The third dimension, the architecture layers, consists of 6 components: business, functional, information a , communication, integration and asset. Bring all three dimensions together and, on top of a nice visual, you have a 3D service-oriented architecture. After this introduction to RAMI 4. These were established in the report in which the Industrie 4. Despite the fact that there is a difference between horizontal and vertical integration the goal is the same: ecosystem-wide data information between various systems and across all processes, using data transfer standards and creating the basis for an automated supply and value chain.
Horizontal integration refers to the integration of IT systems for and across the various production and business planning processes. In-between these various processes there are flows of materials, energy and information. Moreover, they concern both the internal as external partners, suppliers, customers but also other ecosystem members, from logistics to innovation flows and stakeholders. In other words: horizontal integration is about digitization across the full value and supply chain, whereby data exchanges and connected information systems take center stage.
As you can imagine this is not a small task. For starters, within organizations there are still quite some disconnected IT systems. This is a challenge for all organizations, industrial or not. If you start looking at seamless integration and data exchange with suppliers, customers and other external stakeholders, the picture becomes even more complex.
Also keep in mind the life cycle and value stream dimension of RAMI 4. Nevertheless, it is critical for Industry 4. The benefits and drivers for this need for horizontally connected information systems are pretty comparable to those we find in information management, as are the disadvantages if systems are not integrated.
Ask any organization in any industry. These hierarchical level are respectively the field level interfacing with the production process via sensors and actuators , the control level regulation of both machines and systems , the process line level or actual production process level that needs to be monitored and controlled , the operations level production planning, quality management and so forth and the enterprise planning level order management and processing, the bigger overall production planning etc.
Typical solutions and technologies in this vertical integration include PLCs which control manufacturing processes and sit on the control level, SCADA which enables various production process level and supervisory tasks and is de facto commonly used in industrial control systems, MES or manufacturing execution systems for the management level and intelligent ERP for the enterprise level, which is the highest level in this hierarchical picture.
As mentioned previously, the MES manufacturing execution system plays a central role in the first stages of Industry 4. As mentioned previously the opportunities which are offered by Industry 4. As the people behind Industrie 4. The true opportunities of Industry 4. Although that is easier said than done for many companies reaching these stages and goals is a virtually impossible task, certainly now, one of the reasons why they mainly focus on a staged approach or smaller steps as you can read in our article on industrial transformation , it is the true goal: new business models based on data, new ecosystems and new ways to service customers, meet demands in novel ways and create new revenue streams.
These more aspirational goals of industrial transformation mainly revolve around the service dimension of the so-called automation pyramid. Below is a nice example of such an automation pyramid, courtesy of the people at invilution. Indeed, it looks like the vertical integration image above. What we do have now is the growing importance of the Internet of Things.
And Industry 4. And, yes, it also looks a bit like the DIKW pyramid, a model that has existed forever to show the path from data to information to knowledge to wisdom and in some depictions to action , in the end it is all very much related. That automation pyramid is really just a depiction of the implementation of Industry 4. The automation pyramid for the implementation of Industry 4. Do also think about the layers of network models such as OSI and others when looking at them as obviously there is a technological dimension and IT and IoT people will � of course � recognize a lot too.
Just as other aspects of Industry 4. The first layer of the automation pyramid concerns sensors and actuators. The first layer essentially consists of product and manufacturing assets and components which become information carriers as they can be addressed, localized and identified through sensors and are connected. Built upon that connected layer of sensors, actuators and essentially data sits a layer of services and systems that enables the new ways in which the value chain is organized and managed.
Here we meet applications such as energy monitoring and the monitoring and management of systems and conditions of assets such as machines, buildings, infrastructure and so forth. In other words: mainly monitoring and managing, albeit it with the next step in mind: we do monitor for a reason � to enhance, understand and build new capabilities. Initially these maintenance, tracking and other applications are often focusing on internal operations but of course some can become additional revenue sources when deployed and offered in a customer ecosystem context, for example by offering maintenance contracts that could bring in new revenues or be offered as a service with the equipment you sell, while lowering costs for yourself service and support and your customers less downtime.
These could range from applications enabling consumers to tailor the goods they order and sell advanced services to come up with new revenue streams, certainly when developing services within ecosystems of data and possible partners.
But again, it looks easier than it is in practice of course. Going from less paper and legacy systems to simply connecting assets and leveraging IoT, bridging IT and OT integration challenges in the first layer and being able to monitor and manage whatever needs to be monitored and managed, from energy to structures and beyond is already a huge step for many.
There has been an awful lot of academic work into those design principles so you might find other terms and potentially four instead of six design principles. In essence they are relatively simple � and should allow to explain what Industry 4.
These relatively well-known Industry 4. In order to move to intelligent manufacturing, smart factories, or connected industries, you need to bridge things such as real things, people, standards, work processes man and machine and more. And to bridge all that you need data and networks. They must all inter-operate and inter-connect.
You need to bridge IT and OT, you need to have assets such as machines that can connect and communicate thanks to sensors and other equipment and you need to connect people, data, machines and so on. This is indeed mainly about the Internet of Things and, in a broader perspective an Internet of Services, Internet of People, Services and Things, Internet of Everything, whatever name you prefer.
Interoperability is also about collaboration, the ability to have many really many standards talk to each other so data from various sources can be leveraged why we use Industrial IoT gateways, IoT platforms and talk about IT and OT integration, which goes beyond technology and is about human collaboration too, namely IT and OT teams. Interoperability means connected devices, connected communication technologies, connected people, connected data, people connected and collaborating with machines, machines working with machines, an interoperable unified and holistic information, security and data layer and so forth.
Inter-operating and inter-connecting and in more than one sense connected with vertical and horizontal integration. Information transparency or virtualization might be a bit harder to explain to a friend as it is not about the transparency of information.
Without interoperability, information transparency and virtualization are not possible as the information needs to be put in context and systems are context-aware, combining information from other sources too. In the cyber-physical lingo of Industry 4. Finally do note that we speak about context-aware information. This essentially means two things: 1 information is not data, remember the DIKW model so analytics and moving from data to information and so forth is key here and 2 context-aware also means that the information can differ, depending on not just the actual context in which it is gathered and enriched but also in the context of its scope which can mean real-time information and so forth.
The easier way to explain it to a friend is probably to say that there is a virtual copy for pretty much everything. As mentioned earlier one of the core goals of Industry 4. Only then the agility and flexibility needed to be able to deal with uncertainties, respond to demands of personalization, the concept of the smart factory and its place in an inter-connected ecosystem, the required data analytics and the various logistics can be enhanced, meeting the need for speed.
We tackled this aspect of autonomy and semi- autonomous decisions and intelligence more in depth in our article on Logistics 4. Decentralization is not just a given in Industry 4.
In fact, the IoT de facto is a decentralized given as such. We are talking about a distributed reality. However, in the scope of Industry 4. Decentralized and autonomous decisions are not just key in the technologies and cyber-physical systems of Industry 4.
The end of the discussion on decentralization and autonomy is far from over, certainly from the human and decision-making perspective. In Industry 4. However, in practice this is not always achievable, let alone desirable. If you strive towards more autonomy on the machine and cyber-physical system level you do so for increased efficiency and to meet the demands of an increasingly real-time economy. Advanced analytics, the IoT and the information and production systems in a smart manufacturing environment in its broader context of collaboration and ecosystems already are all about the development of real-time capabilities.
Without interoperability, information transparency and virtualization are not possible. Flexibility, predictive maintenance, being able to quickly replace assets in case of failures and the IoT all are important in this perspective which also touches the previously mentioned design principles and the data to decision aspects tackled previously.
Moreover, a real-time capability is essential for the last two design principles, service orientation and modularity. The service orientation is related with the as-a-service economy, the Internet of Services and the obvious fact that manufacturing needs to be more tailored to the demand of customers for services and products with value added services e. Yet, the service orientation is also related with the need for manufacturers and other industries to develop new services that are de facto based upon data, turned into intelligence, and seek new service-based revenue models.
Moreover, technical assistance and, more specifically maintenance, is a core principle as IoT and data analytics simply allow the transformation of services and maintenance. There are plenty of companies who changed their service models by simply adding levels of intelligence and connectivity with IoT to the equipment they sell. And here we also meet Human-Machine Interaction. Finally, the service aspect is also related with the development of new as-a-service-models based upon data but also based upon the evolution towards a Machines as a Service model.
Modularity means many things, depending on how you look at it: the various individual modules within the broad smart factory environment or simply as the end result when it becomes agility and flexibility. You could say that modularity has everything to do with a shift from rigid systems, inflexible models and linear manufacturing and planning to an environment where changing demands from customers, partners in the overall supply chain, regulators, market conditions and all other possible elements causing the need for transformation and flexibility are put in the center.
The modules are locally controlled without hierarchy. Previously in this overview of Industry 4. Most of them are really umbrella terms for several technologies.
We already tackled horizontal and vertical integration, cyber-physical systems and the Industrial Internet of Things as really vast realities with many technologies and components before on this page and elsewhere. We also have literally dozens of articles on other evolutions in the mentioned convergence and application of nine digital industrial technologies as BCG calls them.
Security that spans the physical and digital domain, the respective processes as well as communication between these areas is a prerequisite for the success of Industry 4. Security that is implemented only in an isolated way is easily bypassed and would be ineffective.
Those that are less typical with typical ones being the integration of IT and OT, additive manufacturing, industrial robots and so forth are probably the ones you are looking at today: IoT, Big Data, the cloud, maybe 3D-printing etc.
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