Impact of Building Information Modeling on Just-in-Time Material Delivery
Bibliographic record
Abstract
The purpose of this research is to evaluate the impact of Building Information Modeling (BIM) on Just-In-Time (JIT) material delivery, with a focus on how the use of BIM can help improve efficient implementation of JIT and reduce the cost of material management.Previous research and a number of case studies have addressed the positive impact of BIM on construction at large but none focused on JIT in construction.This paper presents a methodology for selecting reliable material vendors.It also utilises the integration of BIM and scheduling software to generate quantities of material and its required delivery time to improve the flow process and improve its reliability to minimise related delays and productivity losses arising from idle and non-productive time of equipment and labour on jobsites.4D visualization is utilized to support coordination and timing of JIT material deliveries in an effort to minimize congestion on job sites.Case studies on JIT material deliveries are presented and the impact of BIM implementation on reducing the cost of material management is evaluated.The joint effect of BIM and JIT on quality control, elimination of waste, reduction of inventory buffers, and on relationships with material vendors are evaluated by analysing and comparing data from case studies.The paper also presents a methodology based on multi-attribute decision criteria for modelling the selection criteria for material vendors.The model can assist in ranking vendors not only based on cost and quality but also on their reliability in delivering material on time.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.011 | 0.034 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".