Developing Improvement Planning Phase in Project Management Maturity Models
Bibliographic record
Abstract
The suggested cycle of project management maturity model generally include phases of evaluation, planning, improvement and finally cycle repeat which is largely based on a cycle known as Deming or PDCA. “Improvement Planning Phase” is the most important phase requiring development among these models that it is not discussed much. The major criteria for prioritization and planning in this phase were investigated in a research by authors. At first, the literature of subject is reviewed, by doing a series of interviews with project management consultants then prioritization criteria is identified, and eventually it is continued with more analysis on each of these criteria by distributing the questionnaire. The most significant criteria can be mentioned as current maturity level, desired maturity level for each process, relative balance between maturity levels of different processes, relations (predecessor & successor) between processes, the impact of each process on success, resources and organizational effort required for implementation, the role of organization (employer/contractor) and the acceptance of organization in different processes. Finally, according to the criteria a model was developed for “improvement planning phase”. A model is regarded as an improvable point in these models if it pays attentions to all criteria in addition to the relative importance of each criterion and importance of each process compared to each criterion in form of a specific procedure.
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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 | 0.004 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".