Selection of New Projects Considering the Synergistic Relationships in a Project Portfolio
Bibliographic record
Abstract
Multiple internal conflicts and external emergencies can occur when an enterprise implements a project portfolio (PP), making the PP inevitably deviate from the enterprise’s strategic objectives. As a means of project portfolio change (PPC) that aims to align the PP with strategic objectives, adding new projects can compensate for this deviation. Furthermore, the synergistic relationships in the PP can significantly impact the achievement of the enterprise’s strategic objectives. Therefore, this study presents a procedure for the selection of new projects that considers the synergistic relationships in the PP. First, the deviation between the PP and the enterprise’s strategic objectives is identified. Second, the synergistic relationships between candidate new projects and the projects in the PP are analyzed, based on which a model of new project selection is built. Third, by comparing the model simulation results of the attainment of the strategic objectives of several PPs, the new projects that can best achieve these strategic objectives are added to the PP. This procedure is illustrated using a numerical example showing its applicability and efficacy. For academia, this study provides a theoretical framework for the selection of new projects. Moreover, the straightforward procedure can help manage PPs in business practice.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".