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Innovation project management driven by uncertainty : methods and tools

Abstract : Uncertainty is an intrinsic part of every project, in particular of innovation projects. Unclear and changing specifications, lack of experience and skills as well as context influences coming from e.g. stakeholders and legislation are only a few typical sources of uncertainty. In general, uncertainty levels are not homogeneously distributed among individual project tasks. A task’s uncertainty level, however, has a huge influence on how this task shall be managed and executed. In common practice, project managers, project teams and project management tools do not consider this fact systematically, which leads to inappropriate task execution modes with unsatisfying outcomes and negative consequences downstream the project.In this context, this thesis proposes a novel methodology for systematically including uncertainty and context considerations in project planning and analysis from macro- (i.e., project) to micro- (i.e., task) level. It is based on classifying individual project tasks according to the uncertainty they are confronted with. To achieve this, this work first identifies fundamental requirements to management and decision aid tools facilitating the planning, monitoring, and analysis of any kind of projects characterized by a considerable level of uncertainty. Based on a task model that integrates a definition of the input, targeted outcome, the action as well as its context in the form of involved stakeholders, our tool integrates a task classification according to the estimated uncertainty levels of each task’s targeted outcome with respect to its inputs, as well as the task’s actions and context. Context can be taken into account systematically using a novel context classification and measurement framework derived from existing frameworks for capturing project complexity and uncertainty. A modelling language facilitating the practical application of these models using the task and stakeholder network analysis and visualization tools has been implemented.The entire work is based on a grounded field study that has been carried out within the industrial research environment of the Bayer Group over three years, complemented by an in-depth analysis of research literature in the related fields. Expert interviews, workshops and trainings, as well as the active support and accompaniment of concrete corporate innovation projects have been the central practical means of developing and validating the results.
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Submitted on : Wednesday, September 2, 2020 - 12:55:08 PM
Last modification on : Friday, September 11, 2020 - 3:27:58 AM


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  • HAL Id : tel-02928233, version 1



Karolin Gebhardt. Innovation project management driven by uncertainty : methods and tools. Chemical and Process Engineering. Université Grenoble Alpes [2020-..], 2020. English. ⟨NNT : 2020GRALI025⟩. ⟨tel-02928233⟩



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