DELIVERABLE-DRIVEN AGILE MANAGEMENT: A NEW PARADIGM FOR EFFICIENT CONSTRUCTION PROJECT CONTROL
Bibliographic record
Abstract
In the field of construction project management, an approach focused on planned activities, such as the one commonly practiced using tools like MS-Project, often limits the flexibility and efficiency of managers.This article proposes a paradigm shift towards management that is centred on deliverables, work packages, and milestones, thus allowing for greater agility in decision-making while adhering to quality, budget, and time constraints.By emphasizing the evaluation of outcomes and performance rather than merely tracking activities, managers can better tailor their strategies to the realities on the ground.The integration of the Last Planner System principles and Lean Construction methodology is also recommended to enhance coordination and efficiency in construction projects.These approaches encourage collaborative planning and waste reduction, promoting smoother and quicker project execution.Furthermore, the use of earned value analysis is highlighted for a more accurate assessment of project performance.Rather than just comparing planned with actual figures, earned value analysis offers a more nuanced perspective by integrating the real added value of the work performed.Finally, the article emphasizes the importance of visual modelling, especially spatiotemporal ones, for project monitoring and management.These tools allow for a clearer and more intuitive visualization of work progress, thus facilitating informed decision-making by managers.
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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 | 0.009 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.007 | 0.004 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".