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digitalization, digital opportunities, digital expertise, digital readiness, digital transformation, digitalization is opening up new horizons, industry 4.0,  Business Intelligence, Make better decisions in plant operation. Plant optimization by using predictive maintenance, decisions based on real-time monitoring, reduced out-of-service times – the digital transformation around Industry 4.0 offers the possibility of never-before-seen efficiency for manufacturing companies.<BR>

Business Intelligence

Business Intelligence: Make better decisions in plant operation

    Developments in the fields of artificial intelligence and machine learning are progressing at unprecedented speed. At the same time, data usage is growing exponentially. It is not just the amount of data that is changing but also the understanding of how it can be used profitably. Plant optimization by using predictive maintenance, decisions based on real-time monitoring, reduced out-of-service times – the digital transformation around Industry 4.0 offers the possibility of never-before-seen efficiency for manufacturing companies. Welcome to the age of operational business intelligence!

    Operational business intelligence means advanced analysis of real-time data to offer users extremely fast response times in current production. The underlying data are collected during daily plant operation. And these data come in large volumes. Analysts at IDC expect data volumes of 175 zettabytes globally by 2025. In comparison: According to estimates, if digitized, the set of all the words ever spoken by humanity would amount to just 42 zettabytes.

    Digital transformation: How data is changing industry

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      Data is becoming a key resource for the economy

      Data is becoming a key resource for the economy. digitalization, digital opportunities, digital expertise, digital readiness, digital transformation, digitalization is opening up new horizons, industry 4.0,  Business Intelligence, plant operation, artificial intelligence, machine learning, technological innovation
      It isn’t just the sheer quantity but primarily the content that ensures that data and Big Data analysis tools will become a resource for social prosperity and participation, a prospering economy, protection of the environment and climate, scientific progress and governmental action in the digital age, states the Paper on the basic points of the German federal government data strategy. In it, the ability to use, link and evaluate data responsibly and autonomously is considered the basis for technological innovation, knowledge creation and social cohesion.

      The amount of data and data growth are not just a promise for the future for companies but also a real challenge in terms of cloud solutions and networking. Systems are required to keep pace with this.

      Paradigm shift: Data quality rather than data quantity

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      Until a few years ago, the assumption was still “the more data, the better.” This is not entirely incorrect because data are really vital for digital business models, artificial intelligence and thus for innovative products and services. They are a decisive catalyst in using operational business intelligence in industrial applications and future-ready projects in B2B business.


      Of course, it is also important from a company’s view to use data over the entire value-added chain and – in the case of information concerning customers and consumers – even beyond. However, in terms of the volumes of data that are simply exploding, it is becoming clear that quantity alone will just not do.

      Smart analyses turn Big Data into Right Data

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      Electrons are shot at one another in the world’s most powerful particle accelerator, CERN’s Large Hadron Collider (LHC) in Geneva. When this happens, about 150 million sensors provide data 40 million times per second. After initially picking out the few important events, about 100 collisions per second remain that are interesting for science. 100 in 40 million.


      We are familiar with the problem of “junk” data in a considerably more mundane context— on the social networks of the Internet for example. Anyone who systematically tracks what is discussed and exchanged because they hope to glean knowledge that can then be monetized need to expect a similarly small amount of actually useful information due to the large amount of “noise” being uttered.

      The Big Data paradigm that has dominated the discussion about the value of data for many years will now be expanded with the question about the actual benefit of Big Data analyses. Additional terms such as Right Data or Smart Data indicate that the question of quantity has been pushed to the background in favor of quality. Consequently, it is more about collecting and using for analytical purposes only those data that are really necessary. The real art here is knowing which is which.

      You want to learn how Voith can increase your Business Intelligence?

      Leave your information below – our experts are happy to help.

      Operational business intelligence solutions: The range of B2B services from Voith

        OnCumulus: Cloud-based industrial data platform

        With OnCumulus, Voith is offering its B2B customers a cloud-based platform for the Industrial Internet of Things (IIoT) and thus a centralized, reliable information hub for industrial data. The IIoT platform is based on highly standardized Open Source technologies, it meets highest security requirements, can be scaled, is flexible and can be expanded at any time. As such, it enables companies to significantly increase their business intelligence for industrial applications.

        OnCumulus.Suite: Intelligent data visualization

        The OnCumulus.Suite provides companies with options to visualize data using simple-to-operate tools such as Cockpit and Analyzer. Customers can access their data virtually in real time and take the first step towards optimizing their operational processes with personalized dashboards.

        OnPerformance.Lab: Analyses and remote support

        The OnPerformance.Lab supports hydro powerplant operators in reducing maintenance and repair costs as well as downtime by means of analyses and remote support. Our expert team combines expertise and experience with the latest data analysis to improve plant maintenance and operation.

        OnEfficiency and OnCare: Increasing plant efficiency and productivity

        In addition to OnCumulus.Suite, Voith offers its customers sector-specific applications and extensions to existing platforms and services including OnEfficiency and OnCare. You can find details on this and all other Voith products in the “Data-based intelligence with Voith OnCumulus” brochure.

        Do you want to find out more about our OnCare and OnEfficiency solutions?

        Our experts will be glad to help.
        Contact us!

        Acoustical monitoring: Plant operation security using AI

          In 2018, Voith installed at the Budarhals hydro power plant in Iceland the acoustical monitoring system OnCare.Acoustic. It detects noises that deviate from normal conditions. This reduces the probability that the power plant’s machines will face unplanned downtime. In addition, the continuous analysis of machine data assists in optimizing operation and planning maintenance work in a targeted manner.

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          Accordingly, microphones detect ambient noise from the machines that is stored and then analyzed using algorithms. In the first project phase, the system collected all the data, compared it to other power plants and learned which noises corresponded to normal machine behavior. In the second learning phase, it was sufficient for OnCare.Acoustic to recognize deviations from the typical noise pattern in real time. Once a deviation had been detected, the system issued a warning and informed one of the operator’s available service technician.

          Three important points for intelligent data processing and the advantages of artificial intelligence in industry can be deduced from this example of operational business intelligence in Industry 4.0:

          Knowledge of the parameters for the intelligent analysis of states and deviations is not primarily IT technical knowledge or project knowledge but rather domain knowledge. This is gained through years of experience in erecting, operating and maintaining machines and plants.

          1. A trained system does not need all the available data but only those data that provide real knowledge. Learning how to differentiate these data is the goal of machine learning algorithms. The need for new data drops with increasing operating time and as the algorithms know more.
          2. The more advanced a project is, the less manual work is needed. Conversely, the longer the system works, the more autonomous the algorithm can act based on the trained knowledge and consequently the more reliably any deviations can be detected.

          It becomes clear how important it is to work with high-grade data in companies so as to monetize their value in digital business and earnings models and get a grip on the growing amount of data.

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          The challenges and future potential of operational business intelligence

            digitalization, digital opportunities, digital expertise, digital readiness, digital transformation, digitalization is opening up new horizons, industry 4.0,  Business Intelligence, plant operation, artificial intelligence, machine learning, technological innovation, B2B business, Industrial Internet of Things,<BR>The challenges and future potential of operational business intelligence.

            Data quality is crucial

            digitalization, digital opportunities, digital expertise, digital readiness, digital transformation, digitalization is opening up new horizons, industry 4.0,  Business Intelligence, plant operation, artificial intelligence, machine learning, technological innovation, B2B business, Industrial Internet of Things, big data,
            The technologies for collecting data along the entire value-added chain are there and they work: Sensors and actuators in machines and plants, edge technologies for fast local processing, networks for transmitting data to centralized computer centers, data storage facilities, databases and algorithms. They all have the necessary maturity. Isolated and integrated solutions from various vendors, interoperable systems, open standards and all interfaces link system worlds together.


            Despite this, many companies complain that data for evaluations are contradictory, incomplete or obsolete. This often leads to management decisions based on insufficient information because the spectrum of available data has not been exhausted. In addition, the characteristics of the data used are sometimes inadequate. The bottom line: the potentially immense benefits of Big Data analyses for manufacturing companies are only available if the quality is adequate.

            Challenges in data-driven business models

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            Challenges in data-driven business models The fact that the technologies needed for well-founded intelligent data analyses are mature and available does not mean that they are also ready for use by all companies. As a result, it may seem obvious that this is exactly where investment needs to get off the mark. However, this is not the rule. According to experts, it is primarily the lack of technical understanding that hinders many companies on the road to data-driven business models of operational business intelligence. A targeted assessment or E-learning platforms for digital readiness in industry can help.

            Alongside the lack of technical understanding, the complex system landscape required for valid data analyses often slows the productive use of data. IT employees are often faced with the task of linking fully developed, heterogeneous systems that were not conceived to be open or interoperable.

            You want to learn how Voith can increase your Business Intelligence?

            Leave your information below – our experts are happy to help.

            The way to a sound data strategy

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            The good news for companies on the road to a sound data strategy is that you already have most of the data you need. Master data and metadata, transaction data, increasingly even data from machines and plants in networked production – all of this is the raw material that can be upgraded into data-driven business models in an operational business intelligence sense.

            Digitizing and automating plants and processes

            Digitizing and automating plants and processes. digitalization, digital opportunities, digital expertise, digital readiness, digital transformation, digitalization is opening up new horizons, industry 4.0,  Business Intelligence, plant operation, artificial intelligence, machine learning, technological innovation, B2B business, Industrial Internet of Things,  digitization of business processes over the entire value-added chain. Business Process Management, BPM, Digitized processes, automation
            To truly benefit from data, consistent digitization of business processes over the entire value-added chain is required. Data can only provide meaningful information if they completely cover and represent a company’s business activities. This task is handled by Business Process Management (BPM). BPM is concerned with the integration of processes and applications to make the data available. It brings an additional important benefit from the company’s point of view. Digitized processes offer the option of automation, for example, in the form of Robotic Process Automation (RPA) in which robots carry out activities that were previously performed manually. Automation tasks, in turn, are the basis for efficiency increases at the company because they replace expensive, mistake-prone manual processes with the work of IT-assisted systems.

            “The democratization of IT”

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            While companies have to think about the technologies, databases, cloud storage, network technologies and algorithms, it doesn’t need to be a headache. They are available and can be adapted to individualized needs at little cost.


            Here, too, the requirements are shifting in the direction of what many experts call the “democratization of IT.” The technologies are so accessible that they can be adapted in engineering departments. Development and extensive integration work by IT departments is becoming less necessary.

            And even algorithms do not need to be programmed anymore, merely configured and trained. Even this is a task that can be performed in engineering departments using existing domain knowledge. In this way, data processing in companies becomes operational business intelligence that comes from current processes and can be performed in engineering departments and allows for analysis virtually in real time based on transactional data.

            Do you want to increase your operational business intelligence?

              Our experts would be glad to help you. Contact us!

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