Analysis and Design of a Project Portfolio Management System
Bibliographic record
Abstract
The paramount importance of project portfolios for business drives managers to search for highly efficient support tools to overcome complex challenges of their management. A major tradeoff is to acquire tools able to produce a convenient portfolio project prioritization process, on which business investments are decided. However, by using existing Project Portfolio Management Systems (PPMS), many concurrent projects in a portfolio are usually prioritized and planned in the upstream life-cycle phases according to financial criteria, and overlooking the portfolio alignment to enterprise strategies and the availability of resources, although their importance. In this paper, we propose a conceptual formalization of PPMS with respect to a double portfolio prioritization process that performs two levels of selections according to both: i.) Strategy alignment, including returns on investment, size, and total cost; and ii.) Execution capability, as the organization should be able to manage and deliver the selected projects' outcomes. The advantage of our PPMS framework is twofold. First, it is useful to be customized by designers to fit organization needs. Second it is built with respect to the double prioritization phase process, as an end-to-end process that guarantees optimal portfolios generation. Further, the proposed PPMS system and its identified functionalities are validated through an implementation of a prototype tool.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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 source (direct Gemma or distilled Codex), 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".