metaphactory supports customers in their digitalization strategy towards intelligent engineering and manufacturing by allowing them to extend data silos with domain-specific models, connect individual data islands, and analyze the integrated data, thus transforming passive data into new, smart knowledge. It generates business value by significantly increasing transparency, reducing redundancy, improving internal processes, and reducing the time required to find and analyze information and reach smart business decisions.
Flexible self-service data access for domain experts
Unified data hub for information distributed across multiple, heterogeneous data sources, regardless of data type or format
Domain experts leverage domain ontologies and intelligent query construction mechanisms (SPARQL) to access and analyze data in the knowledge graph
Configurations generation to meet customer requirements
Improved management of data across multiple subject matters incl. product configuration and operating & maintenance data, error information, and customer data
Integrated design process across all components and technologies, thus helping create constraint systems and design flexible solutions on demand
Assurance of product information integrity
Reduced efforts for data management by consolidating and connecting product knowledge, and providing high-quality data on product relations for customer-facing applications
Guaranteed consistency of information across tools - data integrity dashboard based on expert-defined rules to derive and quality-check product relations and identify data quality issues
Understand and mitigate financial risks
Highly agile analysis of risks by combining internal and external data, and leveraging search and analysis interfaces to support arbitrary information needs
Improved transparency over customer, partner and competitor data through a unified view over complex networks of comapny relations
Collaboration across various business units and hierarchies
Unify corporate data silos to provide transparency over informal communities, as well as formal organizational hierarchies
Information on people, projects and organizations is stored in the knowledge graph and makes finding experts easy
Prescriptive advice for complex rule frameworks
Time spent on understanding and following processes is reduced by integrating rules, regulations and restrictions in the knowledge graph
Non-conformance costs caused by process violations are reduced by automatically providing contextualized information and descriptive advice for various scenarios
CASE STUDY: TURBINE SPARE PARTS MANAGEMENT AT SIEMENS ENERGY
SMART AND TARGETED MAINTENANCE OF SPARE PARTS OF LARGE GAS TURBINES
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
» Download complete case study "Siemens Energy significantly reduces manual effort for spare parts management of large gas turbines with metaphactory and Amazon Neptune"
CASE STUDY: MATERIALS SCIENCE KNOWLEDGE GRAPH AT BOSCH
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 recent 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.
Leveraging semantic technologies and the metaphactory platform, the Industrial Knowledge Graph has become an integral element in Siemens' 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: