Industry 4.0 glossary
Agility is the ability of a subject to quickly change position. Thus, an agile company is able to adapt to changes in its environment to increase both overall satisfaction (both customer satisfaction and employee satisfaction) and its sales revenue. For many years, we have noticed that customers are increasingly switching to customized products. This observation forces industrials to adapt to their environments and this has become a real challenge, breaking in the process the mass production. New technologies such as collaborative robotics make it possible to switch from one production to another in a matter of moments, its adaptation to the environment, particularly through its ease of programming and use, allows companies using this technology to be more reactive, to gain in competitiveness and to increase their customer satisfaction rate.
If industrial agility was once and still today limited by the rigidity of productive systems, the emergence of industry 4.0, driven by new technologies, the evolution of the internet and the reception of customer data, brings flexible robotic solutions to respond efficiently, relevant and scalable to the needs of consumers who are becoming more and more specific and elusive.
This agility, particularly driven by the industry 4.0 (collaborative robots, Big Data, IoRT, Machine Learning…), allows to design an agile productive system, able to be quickly reviewed in order to adapt to the different products that will meet the market demand (example: multi-referenced packaging lines). Industry 4.0 aims to provide unparalleled adaptability. The combination of collaborative robots, demand management and real-time data offer the potential to significantly increase competitiveness and responsiveness within the industry sector.
Artificial Intelligence brings together many ideas and theories. Globally, one can say that Artificial Intelligence gathers the whole of the capacities and techniques making it possible to carry out tasks requiring logical reasoning usually specific to the human being.
The concept of Artificial Intelligence has been the subject of numerous works, including one that particularly brought this idea to the fore in the 1950s, led by Alan Turing. The latter set up a test, called Turing's Test, which aims to determine the capacity of a machine to hold a human conversation. In order to carry out this test, a human is required to have a written discussion with an unknown interlocutor, who is none other than a machine. The degree of artificial intelligence is measured by whether or not the human discovers that he or she is talking to a computer. Although artificial intelligence is not new, it has been applied only recently, in contrast to the first machines, the computing power of computers of our era are capable of analyzing and processing much more information than before.
Today, Artificial Intelligence enables many applications: commerce (predictive analysis, chatbots), medicine (preventive analysis, imaging, early cancer detection, etc.), industry (predictive maintenance, vision, process optimization), cybersecurity, etc.
"Big Data" is a term that emerged in the late 1990s to refer to a large amount of data whose volume is such as a specific processing is required to derive value from it.
Big Data is a subject around which many recent technological concepts revolve. In fact, technological developments have led to the emergence of a significant mass of data, formed in more or less continuous flows, generated by a set of devices (computers, smartphones, machines and sensors of all kinds) which is itself increasingly important. This quantity of data, often raw or poorly structured, does not allow any analysis on its own. It is thus necessary to carry out a logical structuring to determine both which data to analyze and according to which process. Once this has been done, Big Data concerns many processes and uses, such as monitoring, home automation, connected objects in all their forms, predictive analysis, predictive maintenance, etc. If Big Data forms the foundations of a company, data analysis and the actions that result from it are the building blocks.
Cloud Computing is a concept closely related to the Internet, often imaged by a cloud. This idea of cloud represents an infrastructure composed of hardware and software that allows to provide services remotely, which can then fall under IaaS (Infrastructure as a Service), PaaS (Platform as a Service), SaaS (Software as a Service), RaaS (Robot as a Service)… The cloud is also a flexible and an available alternative to traditional data storage. It allows for increased flexibility, is cost-effective and is constantly updated.
A collaborative robot, or cobot, is a robot with a design allowing it to work alongside a human operator on a production line. Its sensors (cameras, LiDAR, etc.) enable it to achieve such a level of safety that it is no longer necessary to dedicate a space bounded by safety barriers and prohibited to humans. The removal of these physical barriers offered by cobotics makes it possible to optimize the space gain within a plant. Cobotics is also flexible in the way it can be used within a plant, there are several levels of collaboration, allowing each company to customize and improve the ergonomics of each workstation.
The cobot can therefore carry out tasks in a collaborative manner, since it is designed to be integrated into production lines where operators work, allowing them to assist the latter by carrying out the most repetitive tasks or tasks generating the most MSDs (musculoskeletal disorders).
Factory as a Service (FaaS)
Factory as a Service (FaaS) is based on the same principle as SaaS (Software as a Service) and RaaS (Robot as a Service). With a RaaS offer deployed on all of its production lines, an entire factory can be designed as a service with a flexible robotics solution that can respond to variations in demand in a market.
Like RaaS (Robot as a Service), the Factory as a Service (FaaS) allows you to benefit from a connected and modular plant and to manage multi-referenced production lines agilely in order to adapt flexibly to production needs.
Flexibility can be understood as the ability to adapt to demand and the environment.
From a business point of view, it is about its ability to adapt to a new demand and/or a new environment. This flexibility can have repercussions on its productive system since, from a robotic point of view, it is therefore the capacity of a robot to adapt to a new situation in order to respond to a modification of its environment (modification of the productive system, of a production line…). Since the emergence of new consumption patterns on the part of end customers, companies have the obligation to review their production systems, the stakes are high and collaborative robotics represents one of the most reliable solutions to meet these needs. In fact, their adaptation to a given environment, their ease of programming and reprogramming, the possibility of making them mobile, the assurance of offering optimal quality are all criteria that promote flexibility in companies.
While automatons have played a crucial role in industrial automation, robots have brought more flexibility since they can be reprogrammed to perform different tasks. That said, traditional industrial robots require a lot of space since they require the installation of a security perimeter, their reprogramming can also be tedious, expensive and are not intended to provide increased flexibility. Unlike them, collaborative robots, or cobots, break down these barriers since they can operate in restricted spaces, alongside humans, in a secure manner. This safety, which is essential, is provided by the use of numerous sensors (cameras, LiDAR, etc.) and artificial intelligence that allow collaborative robots to understand their environment in order to adapt to it (for example: slowing down when an operator approaches the robot's work area). In addition, their programming interfaces are designed for the end user, in a graphical and ergonomic way, not requiring heavy training to use them.
Industry 4.0, or the industry of the future, is a new way of understanding and organizing production tools. Technological advances are profoundly changing our world: increasingly demanding and better informed consumers, who are using new consumption patterns, on new markets (short circuits, products from organic farming, on-the-go consumption, etc.).
The contributions of new technologies (cloud, collaborative robots, Big Data, industrial internet, additive production…), all based on data, can now enable manufacturers to meet these expectations in an efficient, flexible and agile way.
Today, the transition to the fourth industrial revolution is above all digital, in fact, our increasingly connected world opens up a new playground for manufacturers seeking to strengthen and improve the customer experience. Data collection and analysis is a real competitive challenge and even a decisive factor in the sustainability of a company. Detailed access to a wide range of data makes it possible to anticipate needs, organize production, improve existing systems, and develop new products that are ever closer to consumers, etc.
Internet of Robotic Things (IoRT)
The IoT (Internet of Things) has been democratized and has given objects the possibility to connect to the Internet, to the point where they can form a network exchanging data among themselves and through different platforms (e.g. cloud).
Our robots (the Niryo One, for example) can be connected to the internet to communicate through it, which can allow them to feed back the selected information but also to be controlled remotely.
The IoRT (Internet of Robotic Things) is then a new way of designing robots, which then become data entry and exit points, thus multiplying the uses: remote control, advanced simulations, process analysis, real-time performance measurement, predictive maintenance… And all this without ever having to shut down the robots, so as not to harm the company's performance.
Machine Learning consists of the statistical analysis of a data stream to bring out repetitions and derive predictive models.
To be relevant, Machine Learning requires a large amount of data. If this was not possible before, the emergence of Big Data has made it possible to multiply the sources and types of information available, making machine learning extremely relevant for many applications such as home automation, smartphones, and now robotics.
Machine learning and artificial intelligence enable the design of innovative processes that contribute to the improvement of quality and productivity against in a context of continuous improvement.
In the industrial sector, maintenance is a major issue. In fact, it must allow a good efficiency of the productive system by limiting as much as possible breakdowns and production shutdowns.
Corrective maintenance is the repair of failed machines. While the inconvenience is relatively small when there are problems that can be solved quickly, it can be very costly when it slows down production or stops it completely.
Preventive maintenance then consists of anticipating the possibility of breakdowns by planning regular inspections. However, this can be restrictive since it requires the planning of numerous inspections, not all of which result in a real need for repair.
That said, Industry 4.0, driven by technological developments, brings new opportunities. In fact, the quantity and quality of the data continuously collected by the robots can be used to flexibly anticipate maintenance needs: this is called predictive maintenance. Which uses the analysis of the data flows collected by the robots to determine the probability of a breakdown and its impact on the production system. Predictive maintenance then becomes the agile and anticipated response to the problems raised by future failures, which makes it possible to limit the overall cost of maintenance (including the maintenance itself as well as the cost of limiting production due to a failure).
Robot as a Service (RaaS)
Based on the same principle as SaaS (Software as a Service), it is a question of conceiving differently a fundamental element of a productive system: robotics.
Robot as a Service (RaaS) is an emerging concept that takes advantage of new technologies such as cloud computing and allows to design a robotics offer mixing hardware and software as a service.
For the industry, this on-demand robotics system allows to gain flexibility and to benefit from a continuous improvement of its productive system, whether it is for short or long term needs.
Software as a Service (SaaS)
In recent years, the term "SaaS" has become more and more prevalent. SaaS stands for "Software as a Service". It is therefore the design of a software offer as a service.
The software solution is then designed around a formula whose cost varies according to the need: in fact, SaaS allows to bring flexibility to customers since the payment is made on a pay-per-use basis (example: 1, 3, 12 months subscription…) and can also be adapted to the desired functions (different pricing available to benefit from different functionalities).
The appeal to the customer is to benefit from a flexible offer adapted to his needs (functionality, budget, duration), allowing both one-off and long-term needs to be covered, since the solution is intended to be scalable (continuous improvement of service and functions).