Total Quality Management’s Critical Role in Resolving Delay Issue of Construction Projects Submission
Bibliographic record
Abstract
Despite considerable advancements and innovations in the construction industry, it continues to grapple with challenging obstacles that potentially impede the timely delivery of projects.Among these impediments, project delays are particularly detrimental.Over the past decades, the industry has seen the design and implementation of various intelligent methodologies aimed at alleviating this issue, with Total Quality Management (TQM) being a notable example.This study was conducted to investigate the beneficial impacts of TQM on addressing delays in diverse construction projects.Three research methodologies were employed in this study: 1) a quantitative approach, 2) a qualitative cross-sectional descriptive approach, and 3) a numerical analysis, which explored the role of Building Information Modeling (BIM) in facilitating quantity takeoff with increased accuracy and reduced time.The latter thereby mitigates the delay in construction projects due to the substantial effort, cost, and duration required to estimate the quantity of construction materials.The numerical analysis was carried out using the REVIT software tool.The findings from these three methodologies demonstrated the substantial importance of TQM principles for project managers and senior engineers.These principles can aid in streamlining project delivery and reducing delays.Furthermore, the implementation of TQM resulted in a reduction in quality costs, improved client satisfaction, decreased remedial work, mitigated delays, and fostered a closer relationship between suppliers and subcontractors.In addition, the use of BIM technology was found to enhance the accuracy of construction project cost calculations.Consequently, it reduced the time and cost of estimation and minimized errors, thereby contributing to resolving the delay problem in construction.
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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 | 0.022 | 0.051 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.010 | 0.005 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.007 | 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 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".