A requirement-driven approach for competency-based collaboration in industrial data science projects

• Syberg, Marius ^[1] ; West, Nikolai ^[1] ; Schwenken, Jörn ^[1] ; Adams, Rebekka ^[2] ; Deuse, Jochen ^[1]
  1. [1] TU Dortmund University
  2. [2] NEOCOSMO GmbH
• Localización: International Journal of Production Management and Engineering (IJPME), ISSN-e 2340-4876, ISSN 2340-5317, Vol. 12, Nº. 1, 2024, págs. 79-90
• Idioma: inglés
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• Resumen

  • The digitization of learning resources has led to an increase in specialized collaboration platforms across various fields, including the need for manufacturing companies to develop and maintain expertise in Industrial Data Science (IDS). This paper presents an approach to integrating collaborative and competency-based needs specific to industrial data analytics into a functional collaboration platform. We define the unique requirements of IDS projects and translate them into platform features. These features are then implemented and tested in an online platform within a research project, validating their effectiveness in a dynamic value network setting. The platform’s primary innovation lies in its tailored design for IDS project practitioners from diverse domains, ensuring sustainable integration of data analytics in industrial settings. The initial version of this collaborative platform is currently accessible online and undergoing validation.