Data Analytics

When data is intelligently analyzed and used, it not only enables faster and more precise decision-making processes, but also forward-looking planning and maintenance (advanced analytics, predictive planning and predictive maintenance).

They thus help in the development and control of the strategy as well as in the continuous optimization of operational processes - for example by reducing operating and quality costs. Data analysis also provides the basis for a better and earlier understanding of customer requirements and decisions, resulting in higher responsiveness, better data quality and competitive advantages.

By combining data analysis with learning systems, processes can be automated comprehensively. Particular potential is offered by the development of digital twins of products and processes.

Furthermore, an enormous number of potential correlations can be tested and analyzed using advanced analytics tools, which enables a much deeper and more precise understanding of the core processes.

By adding sensors and software, intelligent products and services (Smart Products & Services) can be developed and additional revenues generated within the framework of new business models. The retrofitting of plants and processes will also become increasingly economical the better the data is used.

It is crucial that comprehensive analyses of various processes, components and manufacturing systems are carried out as close as possible to the data source. This accelerates control and intervention processes and at the same time relieves the global IT and process network through decentralized analysis and monitoring circuits.

Both embedded analytics, in which actions are executed automatically by the IoT systems, and big data analytics, in which interventions are permitted and different views can be analyzed by changing algorithms, are used.

The correct handling of Big Data is derived from the corporate strategy, current and possible business models as well as the desired use cases:

• What is the overall objective to be achieved?
• What contribution can Big Data make to corporate strategy?
• What contribution can Big Data make to process optimization?
• Are new business models possible as a result?
• Which use cases bring immediate benefits?

Typical use cases are:

• Support of product strategies
• Increase in capacity utilisation at individual locations/plants
• Reduction of part tourism
• Total optimum from production and supply chain costs
• Improvement of delivery time and speed
• Improving production availability through predictive maintenance
• Improvement in global cooperation (engineering, production, logistics)
• Identify basics for Smart Products
• Creating a decision-making platform for new business models

In a second phase, the architecture required for the implementation of the Big Data Use Cases is created. Depending on the use case, different process-related and organizational changes have to be made, which have to be planned as well as the technological architecture. The focus is on the following architectural issues, among others:

• Where is the need to build a Digital Twin?
• What role do cloud services play? Which operations are to be carried out in Edge Devices?
• What level of analytics competence is required in-house?
• Do systems have to be digitally retrofitted?
• Which (cyber)security requirements have to be considered?
• How must the processes be designed according to the use cases?
• How must IT and management systems be changed?
• Is the organizational structure to be adapted?

The first step before the rollout is the proof of concept of one or more selected use cases. An initial verification of the corresponding analysis models is usually carried out using a digital twin. With this method, binding results based on productive data can already be achieved, decoupled from productive operation, and necessary adjustments can be made. After reviewing the results, a decision is made about the productive use. All necessary data interfaces and IT services have been tested in Digital Gemini and allow smooth integration into productive operation.

After successful proof of concept of the use cases, the Big Data Cluster is built and transferred from the PoC architecture into a productive architecture. This phase includes the creation of the ETL/ELT processes, the implementation of the algorithms as well as the setup of the visualizations and the frontend for the end users.

If the data cluster is productive, the next step is to transfer the operation to a service provider on the basis of an operating model, connect smart devices and integrate them into the Data Lake. The end result is a Data-as-a-Service (DaaS) or Analytics-as-a-Service (AaaS) solution that consistently delivers IT services on demand.

The further development and development of additional services takes place in a collaborative cloud, which gives both internal and external developers the opportunity to contribute their development services on an app-based basis and to make further services available.

At the beginning of the project, an understanding of the underlying processes and existing data structures is created in order to understand the quantity structure of data sources, types and volumes as well as their significance in business processes.

If the required data is already available and accessible in sufficient quality, a first proof-of-concept is carried out - either with offline data or in a test environment. First hypotheses are created and then checked in a second step by analyzing existing data.

If there are no patterns in the existing data, either extend the scope or perform a retrofit to obtain additional data from the existing processes. The prediction model is then created with relevant features and the prediction quality is validated using historical data.

If the forecasting quality is sufficient, the proof of concept is transferred to a productive environment. The necessary ETL/ELT processes for the automated provision and preparation of the data are set up and integrated into the existing IT landscape.

Subsequently, frontend and report processes are created for the end user before the system goes live and is validated in the real environment. The project ends with the handover of documentation and training of users and experts.