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The 4 levels of automation pyramid in Industry 4.0 and much more

We previously discussed The automation pyramid and new business models in Industry 4.0, now, in this post, we will be discussing its four different levels.

Level one: actuators and sensors – connecting what should be connected

Sensors and actuators are the first layers of the automation pyramid. We would say ‘things and devices’ in an internet of things, other technologies and systems can also be imagined here.

The first layer consists essentially of items and components that can discuss, locate, and define by sensors and link to them as data carriers. The basis of IoT is in reality almost. Link the links to senses, connect digital to physical bridges, and lay foundation for next levels. The theory is most important of all.

Level two: internal services and systems – manage and monitor

The linked sensor layer and mainly the data are focused on a set of services and systems that allow for new ways of organizing and managing the value chain.

This includes applications such as energy monitoring and systems and asset conditions monitoring and management, such as equipment, structures, utilities, and so on. In other words, mostly we track and control. Even though we consider the next step: for one purpose we monitor – to develop, learn and create new skills. Although it is part of the third stage, here you can imagine applications in which you can find ways of making money based on results, for example.

Level three: connection – connect for capabilities and new applications in Industry 4.0

Adding to this the additional layer of networking not only linking properties but also monitoring and data systems. Then come to IoT and IP service models that include smarter applications and functionality such as preventive/predictive maintenance, asset tracking, etc.

These maintenances, monitoring, and other applications initially concentrate on internal operations. However, obviously, certain can use and provided as additional revenue sources in a customer ecosystem setting. For instance by offering maintenance contracts that could generate new income or sell service to your devices. While minimizing costs for yourself (service) Services Services Initially (less downtime).

Level four: ecosystems and new services – transformation

Finally, the fourth layer helps you to draw on your skills and services and to transform data a myriad of services, depending on context.

These may include applications that allow users, particularly when developing services in data ecosystems and potential partners to customize their commodities and sell advanced services. They may also provide new revenue streams. You can also start developing new service. Which mean a major turnaround in your core business by adding data and intelligence from your and other smart systems to fully new customer segments. And we can also see industrial IoT data exchanges and platforms for monetization here. In this process, technology restricts your vision and creativity rather than something you can do.

Again, however, it seems simpler than in person. The ability to link assets and exploit IoT from less paper and conventional structures to cross the complexities of IT and OT incorporation in the first layers. And to track and management of everything from resources to structure and beyond is a significant step for many already.

Design Principles in Industry 4.0

Industry 4.0 is often resumed in six so-called design principles, essentially serving as part of the Industry 4.0 vision and making the guidelines clearer for companies who want to understand, identify and implement Industry 4.0 projects.

These design principles have been a tremendous amount of theoretical work so that you might find other terms, maybe four rather than six design principles. They are fundamentally very easy – and they should allow us to illustrate what Industry 4.0 ultimately means.

The reasonably well-established principles of Industry 4.0 are: 1) Interoperability (now and then also known as connectivity), 2) openness of information (even virtualization or digit organization), 3) decentralization (equally decentralized/autonomous decisions or autonomy),4) capability in real-time, 5) technological assistance and service orientation (human-machine int), and 6) Modularity

A look at these Industry 4.0 design principles in some more detail:

Connectivity, Interconnection, and Interoperability in Industry 4.0

You have to link to real things, people, norms, work processes (man and machine), and so on in order to switch to smart manufacturing, smart factories, or connected industries. And to link all the data and networks you need. They all have to communicate and link.

You must link IT and OT and you need the possibility of connecting persons, data, computers, etc., to equipment such as machines, which can be connected, through sensors and other devices. It’s mostly about the Internet of things and the Internet of Services, the Internet of people, services and things, the Internet of everything, regardless of the name you want. In fact, this is about everything.

Interoperability often includes communication, to speak to one another with many (truly many) norms such that data from different sources can be exploited. (Why 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).

It also means that connected devices, connected communication technologies, linked staff, connected data, connected people and machine collaborators, machinery and interoperable single and complete information, security and data layer, etc. Interoperable and interconnecting and related with vertical and horizontal integration in more than one way.

Virtual entities, virtualization, and Information transparency

The openness of information (or virtualization) may be a little more difficult to clarify to a friend because it doesn’t concern information transparency.

Information transparency essentials are ability to simulate and generate virtual copies of physical elements of world by creating digital models. Fed by all of these data which you receive through sensors and interoperable and interoperable ‘things.’

Info openness and virtualization are not feasible without interoperability. ince information needs to be placed in context and systems contextualise, integrating information from other sources as well. You may claim that you need knowledge from cyber environment (virtual, digital) and physical environment in Industry 4.0 cyber-physical lingo (aware of context in space and place).

Finally, we are talking about context-conscious knowledge. In essence, this means two things.

(1) that information is not data, that DIKW model is remembered so that analytics and transformation from data to information are important.

(2) context knowledge also means that is different not only in actual context in which it is obtained and enriched but also in its reach, meaning real-time information, etc. The better way to describe it to a friend is possible to suggest that almost everything practically copy.

Autonomy, Autonomous decisions, and Decentralization

As previously pointed out, Industry 4.0’s main objectives is to provide computers and cyber-physical systems with autonomous decisions.

Only then will the agility and versatility needed to overcome uncertainty, meet customization needs, increase definition of intelligent factory. Its position in an integrated environment, improve data analytics required and improve the various logistics, to meet the pace requirements. In our article Logistics 4.0, we dealt more extensively with this element of autonomy and (semi) autonomous decisions and intelligence.

It is a pretty omniprésent aspect of decentralization, rather than just Business 4.0 and Logistics 4.0. Internet of things, fog and edge computing, intelligence that travels to the edge through a host of technologies like building automation, blockchain, etc. Indeed, the IoT as such is de facto decentralized. It is a distributed reality that we are talking about. However, the movement of autonomous production systems to phenomena, independent warehouse decisions (smart shelves or robots), and many other problems basically reflect in the scope of Industry 4.0 and Logistics 4.0. And appliances (self-driving vehicles, shutting controls off or switching them on, taking decisions in production, predictive maintenance, you name it).

Decentralize and independent decisions are not only crucial in technologies and cyber-physical systems in Industry 4.0. But also in human aspects. Because not all decisions can fully automate and human planning, and decisions remain key (e.g. collaborative robots).

The end of the debate on decentralization and autonomy is definitely far from over from a human and decision-making angle. The mantra in Industry 4.0 was that decisions should be taken “higher” in the event of problems, and so on. In reality, however, this is not always feasible or even desirable.

Real-time capability in Industry 4.0

If you are striving for greater machinery autonomy and cyber-physical systems. You are doing so to increase efficiency and satisfy the demands of an increasingly real-time economy.

In its broader context of collaboration and ecosystems, advanced analytics, IoT, and information, and production systems in a smart production environment are all concerned with the development of real-time capabilities.

Transparency of information and virtualization cannot accomplish interoperability.

Therefore, it is only reasonable that the overall development and ‘smart factory’ operations require real-time capabilities at the data level. That transforms it into actionable information and actions/decisions and at the process levels and the functioning of these operations. The IoT is all relevant in that perspective. Which also touches upon the aforementioned design concepts and data on decision aspects discussed previously. And is able to rapidly replace assets in the event of failures.

In addition, in the last two design concepts, service orientation, and modularity, a real-time capability is important.

Service Orientation and Technical assistance

The orientation of service links to the service economy, the Internet of Services, and the apparent fact. That manufacturing needs to be more personalized than what the business chooses to manufacture for services through value-added services. In one way, it transforms customer-oriented manufacturing and evolves customer requirements.

However, service orientation connects to build de facto data-driven, intelligent new services for manufacturers to pursue new service-based revenue, models. In addition, technical assistance is a key principle and particularly maintenance. Because IoT and data analysis simply allows services and maintenance to . Many businesses have simply applied intelligence and IoT to the equipment that they offer and modified their model of operation.

Aid systems also have to serve ‘internal customers:’ all potential workers who have to be able to decide. The changing competencies also involves, supported by various systems to do so. And here we encounter contact between humans and machines.

Lastly, the service factor also concerns the creation of new data-driven. But also based, as a service model, models for machines.

Modularity in Industry 4.0

Modularity means something depending on how you perceive it. The different modules inside the larger, intelligent factory ecosystem or simply when agility and versatility become a consequence.

This is protected, among other items, by breadth of versatility and agility in view of consumer demand. The ability to plan the unexpected, and the growing demand for personalization.

You may argue that modularity has something to do with changing demands of consumers, regulators, market conditions, and other elements. That transformation and versatility, from static structures, awkward modeling, and linear development and planning into an environment. Locally without hierarchy, the modules manages.

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