Self-service kits to scale knowledge to autonomous teams - concept, application and limitations

Alexander Poth, Mario Kottke and Andreas Riel
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Alexander Poth: Volkswagen AG
Mario Kottke: Volkswagen AG
Andreas Riel: G-SCOP - Laboratoire des sciences pour la conception, l'optimisation et la production - CNRS - Centre National de la Recherche Scientifique - UGA - Université Grenoble Alpes - Grenoble INP - Institut polytechnique de Grenoble - Grenoble Institute of Technology - UGA - Université Grenoble Alpes, G-SCOP_COSYS - Conception Systémique: Homme, Environnement, Technologies - G-SCOP - Laboratoire des sciences pour la conception, l'optimisation et la production - CNRS - Centre National de la Recherche Scientifique - UGA - Université Grenoble Alpes - Grenoble INP - Institut polytechnique de Grenoble - Grenoble Institute of Technology - UGA - Université Grenoble Alpes

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Abstract: In large organizations, it is not trivial to spread knowledge to all teams. Often, individual teams need to handle similar topics and re-invent the wheel. Another scenario is that a group of people with a common role (for example "guild" in Spotify model) has to distill their practices to make them shareable. Trainings should have empower participants so to apply the learnings easily in their daily businesses. To realize this, the proposed Self-Service Kit (SSK) approach can be used in the context of a holistic methodology that fosters team autonomy while leveraging knowledge spread and sharing throughout a large organization. Such a methodology is presented and instantiated in an enterprise context in facing the mentioned challenges.

Keywords: computer science information systems agile learning organization efiS® framework; computer science; information systems; agile; learning organization; efiS® framework (search for similar items in EconPapers)
Date: 2023
New Economics Papers: this item is included in nep-ger and nep-ppm
Note: View the original document on HAL open archive server: https://hal.science/hal-04188951v1
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Published in Computer Science and Information Systems, 2023, 20 (1), pp.229 - 249. ⟨10.2298/csis211112048p⟩

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Persistent link: https://EconPapers.repec.org/RePEc:hal:journl:hal-04188951

DOI: 10.2298/csis211112048p

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