Stochastic Method for Forecasting Project Time and Cost
Bibliographic record
Abstract
This paper presents a new method for forecasting time and cost of construction projects at completion and/or at any intermediate time horizon. The method is designed to overcome limitations of current applications of earned value method in forecasting project cost and durations. The method adopts recently introduced extensions to EVM and the project ratios technique for progress reporting. It introduces modifications and developments that allow more accurate and practical results. Unlike the current applications of EVM method, the proposed method uses simulation to generate stochastic S-curves based on past performance achieved by contractors. The method enables the user to assess the uncertainty associated with forecasted project cost and duration at completion so that appropriate corrective actions can be taken, when needed. A numerical example is presented to demonstrate the use of the proposed method and to illustrate its improved forecasting accuracy over current methods. The results obtained by the developed method demonstrate the effectiveness of (1) using project ratios technique in forecasting project time and cost comparing to that obtained by traditional EVM method; (2) measuring the status of critical activities only, is particularly useful in forecasting project durations; and (3) accounting for uncertainties involved in the forecasting process provides flexibility in modelling forecasted project time and cost.
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".