Bibliographic record
Abstract
Electrical contractors are at high risk, mainly because of the high percentage of labor in electrical construction activities and the fact that a significant part of their work is last in line in a project, which leads to facing schedule compression. The main schedule compression techniques are overtime, overmanning, and second shift. This paper quantifies the impact of overtime on labor productivity for electrical contractors. Several studies have addressed overtime, but they tend to be old and the source of data is questionable. This paper contains both quantitative and qualitative analyses. The qualitative analysis is based on a survey sent to companies around the United States and Canada and analyzes contractors' responses regarding use of overtime on their projects. The quantitative analysis consists of collecting productivity data from different contractors and studying the effect of using overtime on labor productivity. Statistical models are developed and show the behavior of productivity when using overtime. The quantitative analysis further contains macro and micro approaches. The macro approach model projects where productivity for the whole project is tracked, and no specific overtime schedule is used. As for the micro approach, it shows the effect of using a fixed overtime schedule using the Measured Mile Method (MMM) which compares the productivity in unimpeded time to that in impacted time in order to determine how significantly the project's productivity was impacted. The models developed show that as the number of hours per week increases, the productivity decreases. This study will decrease disputes among owners and contractors regarding the price of additional work. Furthermore, the paper presents a scientific method for forward pricing overtime work and aiding in understanding the risks and rewards of implementing different types of overtime schedules. It also offers valuable insight with regards to safety, supervision, worker fatigue, absenteeism, and other factors related to overtime use.
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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 | 0.003 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".