Case Studies for the Planning and Monitoring of Unit- and Fixed-Price Contracts Using Project Scheduling Software
Bibliographic record
Abstract
Scheduling software used for construction projects is generally designed to plan and monitor activities and resources. These types of software allocate resources to the different activities and allow for resource levelling, costing and cash flow calculations. These features are well-adapted to subcontractors who manage their own human and material resource, and allocate them to the activities of one or a multitude of projects. General contractors and consultants do not generally have full control over project resources and, therefore, most software does not directly address their needs for monitoring unit- or fixed-price contracts. In addition, subcontractors' schedule structures do not necessarily follow the logic of the Bill of Quantities which makes it more difficult to monitor financial progress and cash flow. This paper exposes the problems and limitations associated with the existing scheduling software and presents three scheduling solutions using MS-Project and Excel individually or in combination. These methods have been applied to several case studies in irrigation and fisheries mega-infrastructure projects in Morocco and Burkina Faso. The methodology and proposed solutions are validated through their applications on these mega-projects.
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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 | 0.004 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".