Capacity allocation and downsizing decisions in project portfolio management.

This paper aims to gain insight into capacity allocation and downsizing decisions in project portfolio management. By downsizing, we mean reducing the scale or size of a project and thereby changing the project's content. We first determine the amount of critical capacity that is optimally allocated to strategic projects with deterministic or stochastic workloads for a single-period problem when the impact of downsizing is known. In order to solve the multi-period problem, we have modeled the behavior of the portfolio in subsequent periods as a single project for which the return on investment can be estimated. Secondly, we investigate how the scarcity of resources affects the (expected) value of projects. The independent (expected) project value is calculated under the assumption of unlimited capacity; in contrast, the dependent (expected) project value incorporates the resource constraints. We find that the dependent project value is equal to the independent project value when the return on investment of the portfolio is sufficiently low. In addition, we determine the relation between the return on investment of the portfolio and the value of a project and conclude that the impact of resource scarcity on the value of a project cannot be fully captured by the common financial practice of adapting the discount rate with the estimated return on investment.Project portfolio management; Downsizing; Stochastic workload;

Finding common ground when experts disagree: robust portfolio decision analysis

We address the problem of decision making under “deep uncertainty,” introducing an approach we call Robust Portfolio Decision Analysis. We introduce the idea of Belief Dominance as a prescriptive operationalization of a concept that has appeared in the literature under a number of names. We use this concept to derive a set of non-dominated portfolios; and then identify robust individual alternatives from the non-dominated portfolios. The Belief Dominance concept allows us to synthesize multiple conflicting sources of information by uncovering the range of alternatives that are intelligent responses to the range of beliefs. This goes beyond solutions that are optimal for any specific set of beliefs to uncover defensible solutions that may not otherwise be revealed. We illustrate our approach using a problem in the climate change and energy policy context: choosing among clean energy technology R&D portfolios. We demonstrate how the Belief Dominance concept can uncover portfolios that would otherwise remain hidden and identify robust individual investments

Novel approaches for managing platform-based ecosystems

Digitalization challenges existing organizations and industries. The associated advancement changes the way organizations and their customers interact. This has increasingly fostered the emergence of platforms to facilitate such interaction. Online platforms are software or hardware infrastructures that serve as a foundation and facilitate the interaction between multiple parties (e.g., between organizations and users). Organizations create platforms as part of a larger ecosystem. One major challenge concerns the design of platform-based ecosystems so all participants benefit from their participation. The management of associated relationships with other ecosystem participants is consequently a key challenge and demands according foresight. Platform-based ecosystems are subject to research in the field of information systems. Thus, scientific literature addresses many corresponding research questions and provides valuable insights for both research and practice. However, organizations face numerous challenges when engaging in ecosystems. Such challenges are, e.g., to develop new ecosystems, to incentivize participants to participate in the ecosystem, to cooperate with other participants, and to monitor the ecosystem. In this respect, this doctoral thesis provides a brief overview of platform-based ecosystems and the respective participants therein. Further, the thesis addresses four key challenges in the context of platform-based ecosystems, and proposes novel approaches in order to overcome the challenges. The basis for the novel approaches stems from five research papers. The first and second research paper address the challenge of determining design options when developing new ecosystems via blockchain-enabled initial coin offerings. The papers feature a taxonomy and derive predominant archetypes by drawing on real-world cases. The third research paper addresses the challenge of incentivizing users to participate in platform-based ecosystems. The paper proposes an approach to model financial incentives concerning platform adoption. The fourth research paper proposes an approach to analyze organizational cooperation patterns for the purpose of innovation integration. The developed approach incorporates taxonomy development and enables organizations to determine cooperation characteristics to align the cooperation decision with the cooperation objectives. The fifth research paper addresses the challenge of monitoring customer sentiment on online platforms. The proposed design science research artefact includes a detector of negative sentiment such that organizations are able to identify when a negative sentiment develops, and intervene before users spread the sentiment, e.g., through comments. Each research paper answers a stand-alone research question in the realm of platform-based ecosystems and derives a theoretically founded and separately evaluated research artefact. The artefacts draw on underlying, well-established research methods that allow answering the respective problem statements. Since the problem statements are motived in a practical context, this thesis bridges the gap between a practically oriented problem and a theoretically founded solution. As a result, the derived insights contain a contribution for both, research in the field of Information Systems and practice audience, and encourage the engagement of both domains

Dynamic order acceptance and capacity planning within a multi-project environment.

We present a tactical decision model for order acceptance and capacity planning that maximizes the expected profits from accepted orders, allowing for regular as well as nonregular capacity.We apply stochastic dynamic programming to determine a profit threshold for the accept/reject decision as well as an optimal capacity allocation for accepted projects, both with an eye on maximizing the expected revenues within the problem horizon.We derive a number of managerial insights based on an analysis of the influence of project and environmental characteristics on optimal project selectionand capacity usage.Capacity planning; multi-project; Order acceptance; Stochastic dynamic programming;