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Different solutions and products are designed to help medium-sized - and often large - machine builders meet the challenges of the modern world. However, it often remains unclear what actually lies behind these terms and what advantages these technologies bring with them.
The Internet of Things (IoT) refers to the independent communication of all types of devices over a network. In the industrial context, it predominantly refers to the interaction of sensors, machines and evaluation systems that independently perform a number of tasks. For example, with the help of the automatic evaluation of such networked systems, information on the current plant status - key figures such as OEE - can be generated. In addition, problems can be detected at an early stage and, in the best case, automatically corrected. But where is this data stored and processed? The answer: cloud and edge computing.
The history of the term cloud computing goes back to the early seventies, as a representation of networked computer systems. This gained attention in the early 2000s when Amazon launched EC2, the first widely perceived 'cloud solution'. Essentially, cloud computing describes the on-demand execution and storage of data on servers run by an external service provider.
The type and manner of services here can vary greatly. This makes the term "cloud" vague. A closer look is often necessary to assess what a solution labeled in this way actually entails. Often, the scope of a cloud solution extends from classic server hosting to applications where capacity requirements can be flexibly adapted to current needs and circumstances. These additional resources can be billed and paid for based on usage, rather than having to keep them on standby and administered at all times. This is particularly relevant when capacities are not needed all the time or only at certain times and can lead to significant cost savings.
However, there are a few things that should be considered when using cloud computing. It should be noted that by using a cloud solution, control over the infrastructure is taken out of one's own hands. This creates dependencies, including long-term financial dependencies, on an external provider. Therefore, the cloud strategy should be chosen carefully. Particular attention should be paid to choosing a trustworthy and stable provider, as failures or, in the worst case, the discontinuation of the business line can lead to significant problems. The issue of data protection must also not be ignored here. For example, the storage of personal data in non-European countries is generally not permitted.
When processing real-time data, cloud computing, due to the size of the data volume and the requirement for a short evaluation time, reaches its limits. This is where edge computing comes to the fore.
Edge computing, as the name suggests, is computing at the edge of the network. The idea here is to perform data processing closer to the point of origin of the data. This allows data streams to be accelerated, latency to be minimized, real-time data to be transmitted without problems, and information flows to be better controlled. In this way, edge computing helps to reduce the load on conventional systems and shorten response times to events.
In numerous cases, the successful processing and transmission of data is only made possible through the use of edge computing. Although real-time transmission of data is essential for many applications, even with modern connections the available bandwidth is not sufficient for remote transmission of such large amounts of data. In addition, most of this real-time data has little potential for later reuse.
This is where edge computing comes into play. The immediate evaluation and selection of data can significantly reduce the amount of data to be forwarded. In addition, required applications can be deployed directly without having to go through the cloud first. This can also reduce the steps required for GDPR compliance, for example, by ensuring that certain personal data never leaves the company and, accordingly, the company's internal access control.
The combination of cloud and edge computing enables a number of different applications to run. One variant, for example, is to compile information that allows plant operators to view in detail how the performance of one or more plants is behaving. In this way, any sources of problems can be quickly identified.
Other interesting use cases can be found in the automation of plant maintenance. For example, a series of sensors could monitor the production process of a machine. When a problem occurs, it is transmitted to an analysis application running either on an edge server or in the cloud. This application can then measure the severity of the disorder and make decisions depending on stored rules.
The technologies also offer numerous uses in the event of a non-critical disruption that slows production. The application can find the ideal maintenance time, depending on stored production times. Based on this, create a maintenance order and display the necessary steps on the HMI or a mobile device. Cloud and edge computing can also be successfully applied beyond problem solving. For example, efficient integration with ERP systems can regulate the procurement of materials and spare parts and optimize production parameters using statistical methods and machine learning.
The growing IoT market brings with it the increasing relevance of cloud and edge computing. However, these represent only a cross-section of the possibilities that machine builders must deal with in the age of digitalization in order to remain competitive. Added to this is the constant development and versatility of the topics surrounding digitization. Developing solutions tailored to the needs of an individual machine builder and its customers is therefore not a trivial task and presents numerous challenges.
For these reasons, pragmatic minds and industries GmbH deal with issues of this kind. So that we can support machine builders at eye level and develop productive solutions together with them.