A competitive tendering strategy model and software system based on fuzzy set theory
Bibliographic record
Abstract
Choosing an appropriate amount of margin, or markup, to add to the estimated cost of a construction project is one of the most significant decisions facing a contractor in preparing a tender. A competitive tendering strategy model that uses techniques of fuzzy set theory to help in setting margin on construction projects has been developed. The model provides recommendations of margin size based on an analysis of the company's objectives in tendering and the corporate, project, and competitive environment at the time of tendering. The use of fuzzy set theory allows assessments to be made in qualitative and approximate terms, which suit the subjective nature of the margin size decision. The goal of this model is to help a company achieve its objectives in tendering. The model has been implemented in the form of a prototype software system named PRESTTO. The competitive tendering strategy model is described and a sample project is presented to demonstrate its use in practice. The main conclusion of the paper is that fuzzy set theory can be applied successfully to model the margin size decision.
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".