Knowledge Graph for Manufacturing

Solution | Product Traceability

Linksphere Knowledge Graph | Product Traceability | Smart Solutions | Disconnected Data Silos | Connected Data | PLM | Product Lifecycle | BI & AI ready

In the manufacturing industry with its heterogeneous system landscapes, a Knowledge Graph enables you to link all relevant product data across all domains. This ensures a complete (digital) thread of information about your product throughout its entire lifecycle.

Imagine that one of your suppliers cancels a component vital to your product and you are forced to use an alternative. This has an impact on the entire product lifecycle and leads to a number of questions which require a large amount of contextual information to be answered:

The example outlined is only one of many. In order to solve such tasks, a lot of information has to be gathered and thousands of decisions have to be made in a company every day. The collection and intuitive use of such traceability information thus becomes a business competence critical to success.

To master this challenge, companies in the manufacturing industry need a solution that relates product artifacts to each other throughout the entire product lifecycle - quick and easy. Companies must be able to seamlessly trace paths, starting with requirements, through components, simulations and tests, up to bills of materials and actually manufactured products as well as sensor data from the field. This not only enables the tracing and evaluation of decisions made with regard to their success, but also allows you to predict potential adjustments in their effects (Predictive Change Impact Analysis).

Linksphere provides you with a low code big graph platform to link these artifacts. The Linked Data Layer (Knowledge Graph ) of such a product traceability solution can be configured easily and quickly thanks to low code functionalities. It forms a semantic layer across distributed data silos and is the basis for a variety of traceability applications such as impact analysis, coverage analysis, project status analysis and the like. In addition, the platform is characterized by a high degree of interoperability, which harnesses the context knowledge contained in the graph for other use cases such as BI and AI.

Knowledge Graph for data insights
BI & AI ready thanks to high interoperability
Low Code Platform for application within weeks
Full Stack from data to action

In our example, the Linksphere Linked Data Layer (Knowledge Graph) visualizes the effects of changes at an early stage and thus prevents costly or time-consuming incorrect decision-making, such as the use of unfavorable component alternatives. The origin of any problems in production or operation can be identified by the comprehensive documentation of a product history over its entire product lifecycle. In this way, applicable industry standards can be met more easily and the demand for information can be satisfied with ease.

Further Use Cases / Applications

  • Impact Analysis
  • Coverage Analysis
  • Project Status Analysis
  • Reuse of Product Components
  • Certification
  • Reengineering
  • Risk Management

Additional Example: Use of Spare Parts

To ensure the required material flow, larger quantities of replenishment are already ordered in production. However, the engineering department is in the process of replacing these components with alternatives.

With the help of a Knowledge Graph, both domains can be linked. Changes in the value chain are thus propagated throughout the company at an early stage and cost-intensive false decisions are avoided.

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Sebastian Dörr
Vice President Sales

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