Data Integration- Many manufacturing companies are in the process of digitizing manufacturing processes and entire value chains. They all need a workable concept. Some are still at the beginning, and others are already in the middle of it: Many manufacturing companies have successfully launched their Industry 4.0 initiative. Without data integration, however, Industry 4.0 is inconceivable. But what is essential in terms of data integration on the way to the intelligent factory and what is not?
Successful integration of all data streams is the foundation for essential adjustments to production, for example, to increase the overall system effectiveness or production effectiveness. Companies need to work in three areas: data, data integration, and data analysis to take advantage of the possibilities and numerous advantages of the digitized output. Because the core of Industry 4.0 initiatives consists of information, its integration and its evaluation.
Classic Industry 3.0 has an excellent need for optimization in terms of using data to generate knowledge. Production systems of this generation show a high degree of fragmentation in data silos. They are hardly networked so that a holistic view of processes is not possible. It is estimated that less than one percent of a company’s unstructured data is currently being analyzed. This is why internal and external data are often not considered and linked – although the share of unstructured data is up to 80 per cent!
What Is Unstructured Data?
A typical example of semi- and unstructured data are logs, sensor and video data. On the other hand, structured data includes SAP data, among other things. At the moment, only a tiny part of unstructured data is evaluated because
There is enormous potential in the data of manufacturing companies. However, its use is challenging for many organizations. The difficulties can be solved with suitable applications:
A significant challenge for the systematic use of data in production is to merge the scattered data sources so that, in the end, an optimal data flow is created that supplies all-important systems with the correct data at the right time. The following levels can be identified for this:
The various levels of integration for data from digital products are also reflected in the characteristics of the evaluation processes for this information: the journey begins with the classic evaluation of historical data. It ends with advanced analytics with forecasts for future developments, including recommendations for action or orders to processes or other participants in the value chain. After overcoming the world of data silos, it is time to generate knowledge through analysis and distribute it to the right people in the company. So-called advanced analytics, therefore, form the heart of many Industry 4.0 applications.
As part of a booming Industry 4.0 initiative, powerful modern technologies ensure that competitive advantages are realized, and new perspectives for future production scenarios are possible. McKinsey assumes that Industry 4.0 applications can reach a value of 3.7 trillion US dollars per year by 2025. Manufacturing companies benefit from the following advantages when implementing Industry 4.0 projects:
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