February 14, 2017

THYSSENKRUPP IMPLEMENTS FIRST USE CASE FOR INDUSTRIAL DATA SPACE

thyssenkrupp, one of the first members of the user association, has implemented the first use case for Industrial Data Space with an information system for truck logistics. The target: To optimise the loading and unloading times of trucks and adapt them flexibly to transport routes and traffic disruptions. Another milestone has been reached with this first use case that has resulted in a practical application, proving that Industrial Data Space can work well as part of a company’s daily routine.

thyssenkrupp Steel Europe handles roughly 20,000 trucks per month. With around 30 minutes available to load and unload each truck, any delays in arrival can quickly throw the entire schedule into disarray. The company aims at achieving greater flexibility with Industrial Data Space. They have introduced a solution that enables trucks en route to the company to continuously report their position in line with defined rules, for example using an app that drivers can download to their smartphone. Based on positional and traffic data and the routes the driver is likely to take, an algorithm immediately recognizes any holdups and the computer system then automatically suggests a new loading slot for the delayed truck without any human involvement.
“Our application is only a first example of what can be achieved with Industrial Data Space. There are more applications to come from other companies and that will happen rather quickly”, says Dr. Reinhold Achatz, Chief Technology Officer at thyssenkrupp and Chairman of the Industrial Data Space Association

← NDUSTRIAL DATA SPACE: WORKING TOGETHER FOR CLEAR DATA GOVERNANCE REGULATIONS CONSISTENT DATA USE AS A COMPETITIVE ADVANTAGE →

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MONDRAGON ASSEMBLY EGOKIA

MONDRAGON ASSEMBLY - an international firm specializing in the development of automatization and assembly solutions for various sectors. It operates 6 production plants around the world and is part of the largest cooperative group in the world | IKERLAN Technical research center and member of IDSA

CHALLENGE:

EGOKIA solution allows collaborative learning and knowledge sharing between geographically distributed plants, integrating headquarters in Basque Country and domain experts. Thanks to the data space, data, and AI assets can be securely shared throughout different corporate sites and a new knowledge-based economy emerges.

SUCCESS:

Deploys an architecture based on IDSA connectors, linking Mondragon Assembly’s data and AI platforms from different corporate sites. With this new reliable data space, we extend the lifecycle of artificial intelligence models, enhance their capabilities, and facilitate trusted access and use of its assets by EGOKIA participants.

BENEFITS:

NEW BUSINESS MODEL: A new business model based on AI services and a knowledge-based economy. RELIABLE AI SOLUTIONS: A "standardized", flexible, and secure solution to break data silos to share and complete AI assets and processes between trusted organizations and production plants. Measurable benefits can be listed as: Up to 30% quality on models thanks to the application of federated learning techniques, reduce inversion and time to market on IA-based solutions or lower communications loads. STANDARIZED DATA SPACE – The organization is ready to take part in the knowledge economy with other entities, domain experts, and technology providers.

COMPONENTS:

Data Space connectors 8.0.2 Metada Broker 5.0.3 Identity Provider 1.6.0

Increased visibility of order delivery process

VTT | Wärtsilä | Uwira

CHALLENGE:

The use case revolves around expediting and enhancing transparency between a focal company of a manufacturing ecosystem (OEM) and its supplier in their production order management processes. The existing communication methods have proven inadequate in facilitating efficient and adaptable exchanges between the two entities. By facilitating transparency and fostering a more seamless integration between the IT systems of both companies, the management of product orders can be streamlined, resulting in heightened efficiency and a reduction in errors.

SUCCESS:

The objective is to facilitate a reliable and seamless flow of data between the OEM and its supplier by establishing a dedicated data space. The developed solution will be seamlessly integrated with pre-existing IT infrastructures of both companies and the solution is grounded in the framework of the IDS reference architecture.

The OEM can selectively disclose data from their production environment to the supplier without the fear of data leakages. Respectively, the supplier receives only the data that are essential for them and there is no need to locally extract meaningful insights and valuable information from big data sets.

BENEFITS:

The established data space solution has the potential to significantly enhance transparency between the OEM and the supplier. This gives rise to several potential benefits, including:
Effective Communication: Transparent communication helps in conveying accurate information about product specifications, requirements, changes, and timelines.
Risk Management: Sharing information about potential risks, such as changes in demand or design modifications, allows suppliers to proactively address these issues. Furthermore, in times of disruptions, such as supply shortages or unexpected market shifts, transparent communication helps both parties respond more effectively.
Cost Efficiency: Transparent communication allows both parties to work together to find cost-effective solutions.

COMPONENTS:

IDS connectors - serve as the communication interface linking the two companies and their existing IT systems. With IDS connectors, a data provider can define the rules and conditions (usage policies) under which data are shared with a data consumer.
DAPS - enables connectors to authenticate themselves using X.509 certificates. After successful authentication, the DAPS issues OAuth2 access tokens for connectors, who need these tokens to access the services and data of other connectors.
Metadata Broker— stores information about the data end-points offered by the participants. The broker provides a query interface for connectors and can hence be considered as the search engine in this use case.

LEARN MORE:

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