An Index to Assess Project Management Competencies in Managing Design Changes
Bibliographic record
Abstract
Design changes, whether voluntary or imposed, are common and inevitable in oil and gas projects. These changes are significant sources of cost growth and time delays in projects. As a result, identification of the factors contributing to design changes concerns a number of researchers and professionals in the industry. One of the main factors is project management competency, which significantly contributes when dealing with design changes. This study aims to develop an index for assessing the competency level of a project management team through identifying and rating the main skills and characteristics attributed to team members. The data was acquired through a questionnaire survey along with a series of interviews and brainstorming sessions with practitioners in the industry. The Project Management Competency Index provides a common forum for all project participants to assess and rate the competencies of a project management team. Knowing the composition of a PM team, the team members' background and work experience, and their skills and characteristics constitutes an important step in evaluating and monitoring the performance of a PM team handling design changes at different points during project execution. This is crucial for selection of an effective team able to control and manage all issues related to project design changes. The PMCI, when combined with other key factors, can also greatly improve the predictability of design changes. The result of this study forms part of the authors' ongoing research which focuses on developing a predictive model for pattern recognition of the impact of design changes on project performance.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| 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".