Siddhartha Sampath, Esma S. Gel, Karl G. Kempf, John W. Fowler
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A generalized decision support framework for large-scale project portfolio decisions
Project portfolio selection (PPS) is a complex problem faced by major companies whenever there are multiple funding opportunities with insufficient budget to fund them all. In this paper, we present our work on a PPS decision support tool that has become a fundamental part of the project portfolio decision process at Intel Corporation across its largest product and market divisions. The paper builds on a previous publication that outlines the decision support tool's bicriteria optimization model by providing a solution procedure that is capable of solving real-life PPS problems within time frames acceptable to decision makers, as well as providing further details on the data collection and the decision-making process. We also report on various analysis and visualization tools that have been built to allow decision makers to interact with promising solutions provided by the decision support tool. One of the contributions of the paper is to present a typology of the important dependencies between projects that needs to be considered, and provide details on how they are incorporated in the decision support tool's optimization engine. We discuss important implementation details on the decision-making process and the agents involved, and conclude by describing real-life experiences on how the framework can enable intuitive decision-making when choosing portfolios that best satisfy the organization's business goals.
期刊介绍:
Decision Sciences, a premier journal of the Decision Sciences Institute, publishes scholarly research about decision making within the boundaries of an organization, as well as decisions involving inter-firm coordination. The journal promotes research advancing decision making at the interfaces of business functions and organizational boundaries. The journal also seeks articles extending established lines of work assuming the results of the research have the potential to substantially impact either decision making theory or industry practice. Ground-breaking research articles that enhance managerial understanding of decision making processes and stimulate further research in multi-disciplinary domains are particularly encouraged.