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Scaling agile on large enterprise level with self-service kits to support autonomous teams

Wybrane pełne teksty z tego czasopisma
Identyfikatory
Warianty tytułu
Konferencja
Federated Conference on Computer Science and Information Systems (15 ; 06-09.09.2020 ; Sofia, Bulgaria)
Języki publikacji
EN
Abstrakty
EN
Organizations are looking for ways of establishing agile and lean delivery processes. In this paper, we propose a particular way which based on self-service kits (SSK's). The SSK approach can be used to share expert knowledge in an agile and scalable way to the teams by offering them approaches, methods and tools with background information about the addressed topic. An SSK is provided as a digital bundle of artifacts that help solving an issue related to agile teams. Built upon the pull-principle, it supports team autonomy during teams' delivery procedures. An SSK addresses generic as well as domain specific topics. As all SSK's share a common structured approach to supporting an agile organization, they help systematically scaling expert knowledge. This leverages establishing best practices elaborated by experts in a large scale organization in a native agile manner. As an SSK is structured as a ``how-to'' guide including templates for learning by doing, it helps emphasizing quality aspects too. We demonstrate an example of the systematic application of the SSK approach as well as its scaling in the Volkswagen Group IT.
Rocznik
Tom
Strony
731--737
Opis fizyczny
Bibliogr. 48 poz., tab., il.
Twórcy
  • Volkswagen AG D-38440 Wolfsburg, Germany
autor
  • Volkswagen AG D-38440 Wolfsburg, Germany
autor
  • Grenoble Alps University G-SCOP Laboratory F-38031 Grenoble, France
Bibliografia
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Uwagi
1. Track 5: Software and System Engineering
2. Technical Session: 4th International Conference on Lean and Agile Software Development
3. Opracowanie rekordu ze środków MNiSW, umowa Nr 461252 w ramach programu "Społeczna odpowiedzialność nauki" - moduł: Popularyzacja nauki i promocja sportu (2021).
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-8fe19317-1243-4bb0-be92-e1fbc8ee8311
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