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Siemens Energy application engineers leveraged metaphactory's low-code approach to build a smart turbine spare parts application that allows turbine service engineers to save thousands of hours on manual effort.
Gas turbines of Siemens Energy are used worldwide in different environments and with customer specific configurations. Managing a broad variety of spare parts and configurations for each turbine is a challenge. metaphactory and Amazon Neptune enabled Siemens Energy to build a Turbine Knowledge Graph and visualize the connections between similar parts across the entire fleet of large gas turbines.
The results
- Shorter time to market of the business solution through rapid application development
- Efficient identification and management of spare parts, resulting in higher productivity and yearly time savings of up to 1,500 hours in the first year already
- Increased business user and customer satisfaction
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Siemens AG is one of the leading providers of factory automation solutions and components as well as Manufacturing Execution Systems and Product Lifecycle Management Software. To support human manufacturing planners and line operators in their daily tasks and increase the autonomy of production machinery, the research group for "Semantics and Reasoning" of Siemens Corporate Technology initiated a Manufacturing Knowledge Graph leveraging metaphactory. The goal was to test the feasibility of Smart Manufacturing Planning and Smart Manufacturing Execution concepts utilizing semantic technology. For this, the team created a Manufacturing Knowledge Graph to capture heterogeneous data sources and expert knowledge and built an AI-based knowledge graph application to automate the allocation of suitable production equipment.
The results
The feasibility studies conducted proved that:
- The number of possible plans and configurations that human planners need to evaluate can in some situations be reduced from approx. 1,400 to as few as 40.
- The realization of low-volume orders is feasible and can become affordable by enabling machines to act autonomously.
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Aiming to support engineers in developing and introducing new materials for production, Bosch is building a knowledge graph that exposes material science knowledge and supplies high-quality answers about existing materials in a timely manner.
In a paper presented at the ISWC 2019 Industry Track, Bosch describes how the knowledge graph integrates information stored across multiple sources, and how metaphactory is used to satisfy complex query needs through a user-friendly interface for standard keyword search and semantic-based faceted search.
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Leveraging semantic technologies and the metaphactory platform, the Industrial Knowledge Graph has become an integral element in Siemens's strategy towards intelligent engineering and manufacturing. It powers various business use cases, including gas turbine maintenance, building automation, risk management, factory monitoring, and internal R&D management.
To learn more about Siemens's Industrial Knowledge Graph, have a look at the following material: