Assessment of the Effect of Changing Activities' Start Times on Cash-flow Parameters
Bibliographic record
Abstract
Cash flow modeling is crucial to contractorsto sustain business. Contractors carry out multiple activities within a single project wherein the change of the start times of the activities have varying effect on the values of periodical negative cumulative balances and the other cash-flow parameters. Thus, changing the activities' start times leads consequently to changes in the value of the maximum negative cumulative balance and other cash-flow parameters as well. Schedule-driven cash flow models typically are generated to identify the effect of activities start times on projects' cash flow parameters. In this paper, Monte Carlo simulation technique has been employed to generate schedules and their associated cash flow parameters. The activities' start times are assumed to follow uniform discrete probability distributions with the minimum and maximum values representing the early and late start times respectively. Further, the proposed simulation model considered the stochastic nature of cash-in and cash-out transactions by incorporating the effect of 43 qualitative factors. Three scenarios are defined; each scenario incorporates a different number of qualitative factors. Advanced sensitivity analysis is performed to measure the effect of changing the start times on cash flow using the correlation coefficients. Finally, the proposed simulation model help practitioners identify the activities that highly affect the cash flow and provides a metric to measure the strength of their impact.
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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 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".