Collaborative data science platform and services for the manufacturing industry
The goal of the research project “Networked and integrated application of industrial data analysis for value-creating, competence-oriented collaboration in dynamic value-added networks” (AKKORD) is to develop a modular, data-driven reference toolkit that supports companies in the design and integration of dynamic collaborations for industrial data analysis.
This toolkit covers the service areas “Collaboration & Business Models”, “Analysis Modules & Configuration” and “Competencies & Recommendations for Action”. It develops its full cross-company and cross-functional potential through the semantic networking of data through a powerful data back-end system that manages the heterogeneous data and embeds it in the modular system.
The solution modules support the actors in dynamic value-added networks. The modules include procedural models, tools for professional and technical data analysis, accompanying integration, training and consulting services as well as enablers to build and consolidate competencies for data analysis in companies. The focus is on (a) the design of networked offers and (b) the provision of “best practices” including the implementation of demonstration and pilot applications. Success factors and design recommendations contribute to the targeted development of collaborative business models, ranging from an overview of adaptable business models to specific design recommendations for application scenarios.
Within the scope of the AKKORD project, an integrated, data-driven reference building block for industrial data analysis is being developed and implemented as a collaborative service platform. Solution components such as software modules, recommendations for action or consulting services for collaboration and business models, competence development and assurance as well as analysis and networking of data are developed, tested, and validated.
Industrial data analysis provides manufacturing companies with innovative opportunities for the continuous optimization of products and processes as well as for the initiation of new business models and collaborations in value adding networks. However, external support is crucial for the successful application of data analysis in industrial companies, which often lack the required data science and machine learning skills, strategies, and implementation experience. Furthermore, the implementation of modern analysis technologies within these companies is resource-intensive and strategically oriented services by external service providers often have not been developed sufficiently yet. Thus, the extensive potential of data analysis often cannot be leveraged.
The overall objective of the AKKORD project is to realize the integrated application of industrial data analysis for competence-oriented collaboration in dynamic value adding networks. In order to achieve this, the following sub goals are targeted: enabling value adding collaborations within networks, realization and simplification of integrated and networked data analysis, development of data analysis competencies, and creation of a comprehensive and networked database. Companies are considered as socio-technical systems to address people, technology, and organization equally.
Scientific and Technical Objectives
In order to achieve the objectives of the research project AKKORD, the following scientific and technical objectives are pursued:
- New collaboration opportunities and business models
- Competence development and assurance in value creation networks
- Integrated and networked analysis of industrial data
- Creation of a comprehensive and networked database
- Integrated, modular reference tool kit for industrial data science
Within the AKKORD research project, an integrated, data-driven tool kit for industrial data analysis will be developed and implemented as a collaborative service platform. Solution modules such as software modules for data analysis, recommendations for collaboration within networks and for business models, competence development, as well as analysis and integration of data are developed, tested, and validated. Publication of project results and prototypical implementation of application scenarios ensure the dissemination and applicability of the results.
- Industrial Engineering (with example applications in the automotive industry at Volkswagen Group): Analyzing, comparing, and optimizing assembly work plans in order to extract best practices, to increase efficiency, and to reduce costs.
- Quality Management (with example applications in the white goods manufacturing industry at Miele): Integrating product quality data, service data, order data, and customer feedback from different data sources and departments to get better and more comprehensive insights into product quality and issues, spare demand, etc. and to further improve product quality and customer satisfaction.
- Integrated Data Analysis (with an example application in the lighting industry at ERCO): Analyzing price quotes, offers, and orders from the past to predict future demand and the success of offers to customers for more accurate demand forecasting and better production planning.
- Business Model Development: Analysis of business potential and business planning.
- Competence Development: Analysis of skill gaps and up-skilling in an integrated online collaboration and training platform.
- Data Acquisition and Data Integration: Creation of a comprehensive and networked data basis for industrial data analysis.
- Implementation and Roll-Out Strategies
- German Research Center for Artificial Intelligence – Deutsches Forschungszentrum für künstliche Intelligenz GmbH (DFKI), Saarbrücken, Germany
- Institute of Production Systems – Institut für Produktionssysteme (IPS), Technical University of Dortmund, Germany
- Chair of Didactics in Technology – Lehrstuhl für Fachdidaktik in der Technik (FdT), Technical University of Kaiserslautern, Germany
- Chair of Virtual Product Development – Lehrstuhl für Virtuelle Produktentwicklung (VPE), TU Kaiserslautern, Germany
Application Partners in the Manufacturing Industry
System Development Partners
- Arend Prozessautomation GmbH, Wittlich, Germany
- CONTACT Software GmbH, Kaiserslautern, Germany
- mosaiic GmbH, Munich, Germany
- NEOCOSMO GmbH, Saarbrücken, Germany
- PDTec AG, Karlsruhe, Germany
- RapidMiner GmbH, Dortmund, Germany
April 1st, 2019 – March 31st, 2022
Funding Grant Number:
BMBF FKZ 02P17D210
Funding / Project Sponsor (Fördermittelgeber):
Project Management Agency (Projektträger):