Manufacturing

If We talk to any process expert in the manufacturing industry, we see transparency as a major challenge & one of the major prerequisites for process optimization. Transparency not only increases visibility but also forms the basis on which all key decisions are taken. In simple language, it translates to, the more information available about manufacturing processes, the more accurate decisions can be made and the easier it becomes to optimize the manufacturing process. Each machine or device generates a large amount of data which if integrated into the graph model describing each physical component and device in one knowledge graph, allows a very user-friendly and complete process monitoring. It will also help in understanding which processes to optimize, increasing yield and reducing defects in production. Graph technology is a data representation method that can be used to optimize the manufacturing process. By using graph technology, manufacturers can model the complex relationships between various aspects of the manufacturing process, such as materials, equipment, and production steps. Manufacturing process optimization is a crucial aspect of the manufacturing industry as it helps to improve efficiency, reduce costs, and increase product quality. Knowledge graphs can play a significant role in optimizing the manufacturing process by providing a structured and visual representation of the knowledge and relationships between various aspects of the process. On other hand, RDF, or Resource Description Framework, is a standard for modelling data as a graph, making it well-suited for use in manufacturing process optimization. By using RDF, manufacturers can represent their data in a way that is flexible, extensible, and can be easily integrated with other data sources even from the semantic layer easily.

Predictive maintenance is a proactive approach to maintenance that uses data and analytics to predict when equipment is likely to fail so that maintenance can be performed before a failure occurs. Knowledge graphs can play an important role in predictive maintenance by providing a structured way to store and represent complex, interrelated data about equipment, maintenance history, and operating conditions. With knowledge graphs, data from various sources such as sensors, equipment logs, and maintenance records can be integrated and linked together to form a comprehensive understanding of the equipment and its behavior. This data can then be used to build predictive models that can identify patterns and trends that indicate an increased risk of failure. For example, a knowledge graph could link together information about operating temperature, vibration levels, and maintenance history to predict when a particular component is likely to fail. In addition to helping predict failures, knowledge graphs can also support decision-making by providing a clear and concise representation of the information needed to make informed decisions about maintenance. For example, a knowledge graph could be used to show the impact of different maintenance strategies on equipment reliability, cost, and safety. In summary, knowledge graphs can provide a powerful tool for predictive maintenance by enabling the integration, analysis, and visualization of complex and interrelated data. By using knowledge graphs, organizations can improve the efficiency and effectiveness of their maintenance processes and reduce the risk of equipment failure. A knowledge graph can be used as a digital twin of a machine or even of an entire production facility. Using a graph, organizations can model each component of a machine, its parameters, relationships to other components, and even alternative parts that could replace it. Each piece becomes a node in the graph with a semantically defined relationship to the others. Running an analysis of this digital model allows companies to identify what could go wrong and proactively intervene.

A data digital twin with knowledge graphs is a virtual representation of a real-world system or process, in which the data and relationships between data entities are modeled using a knowledge graph. This allows for a comprehensive and interconnected view of the system or process and can provide valuable insights into its behavior and performance. In manufacturing, a data digital twin with knowledge graphs can be used to represent the design, production, and quality control processes of a product. The knowledge graph can be used to model the relationships between various components of the product, such as design specifications, production processes, and quality control measures. By using a knowledge graph to represent the digital twin, manufacturers can leverage AI and machine learning algorithms to analyze the data and identify patterns and relationships that may not be immediately obvious. This can provide valuable insights into the product and help improve efficiency, reduce waste, and enhance quality. A data digital twin with knowledge graphs can also be easily updated as new data and information become available, allowing manufacturers to stay up-to-date on the latest developments and improve their processes over time. In Industry a data digital twin with knowledge graphs can provide manufacturers with a powerful tool for optimizing their operations, improving product quality, and increasing efficiency. By Providing an abstraction layer for data. Since data are heterogeneous, up to 80% of the data scientist’s time is spent on retrieving data, rather than on their core tasks. Our approach provides a single interface to access all data in a uniform way;• formal modelling of data, information, and knowledge in a knowledge graph. Data, information & knowledge are modeled according to the problem domain, rather than the technical solution or representation of data. Exploration and querying tools allow data scientists to acquire data, information & knowledge about domain concepts such as part, machine, operation, etc., and allow a common understanding of the domain between data engineers, production engineers, and date scientists; reasoning about knowledge. As knowledge is made explicit and formalized in a knowledge graph, this opens up possibilities of (automated) reasoning to generate new knowledge. While there are several possibilities (e.g., ontological reasoning, translation to Bayesian networks, etc.)

Supply chain visibility refers to the ability to track the movement of goods and materials through the supply chain, from the manufacturer to the end consumer. Knowledge graphs can be used to support this process by storing and analyzing large sets of data on the movement of goods, suppliers, and the relationships between them. In supply chain visibility, knowledge graphs can be used to represent the complex relationships between products, suppliers, transportation, and other related entities. Graph databases can also be used to store and analyze large sets of data from the supply chain, such as inventory levels, shipping information, and production data. This can help companies understand the flow of goods through the supply chain, identify bottlenecks, and make decisions about where to invest in improvements. Graph databases can be used to integrate data from various sources such as sensor data, electronic records, and external databases. Knowledge graphs can be used to support the implementation of SCOR by storing huge multi-dimensional data. Using knowledge graphs in SCOR can help companies more effectively analyze and understand complex data sets, identify potential issues in the supply chain, and make more informed decisions about supply chain operations. This can help to improve supply chain efficiency and support compliance with regulatory requirements