Profit-point analysis: A tool for general contractors to measure and compare costs of management time expended on different subcontractors
Bibliographic record
Abstract
One of the trends in construction today is the increasing use of specialty contractors. As a result, projects are becoming more complicated and fragmented, more coordination is required, and overhead costs of the general contractors are increasing relative to the direct costs. Better ways of controlling job-site overhead costs are needed. This paper presents profit-point analysis (PPA), a method for analyzing how indirect staff time of a general contractor is actually spent on a project. Profit points are imaginary points where a general contractor and subcontractors are interfaced. The PPA is a method of analysis on these points, which adapts activity-based costing from manufacturing to construction. This new method, illustrated through a case study, yields valuable information for managerial control; for example, the different amount of supplemental support from the general contractor required by different subcontractors.Key words: overhead costs, cost analysis, profit points, activity-based costing (ABC), management efficiency, evaluating specialty contractors.
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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 | 0.006 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.009 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 0.002 |
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".