The use of International Suppliers and the effects it has on Just-In-Time delivery and Industrial Construction Processes
Bibliographic record
Abstract
The use of International Suppliers and the effects it has on Just-In-Time delivery and Industrial Construction Processes Through the advancement of information technology (IT) over the past few decades, supply chains have been able to streamline opportunities to grow their success, through the increased availability of international suppliers, and new process techniques, such as just-in-time management. International suppliers have been effective as they often lead to lower costs, and flexibility in product design. Supply base rationalization of both domestic and international suppliers, has been identified as a key opportunity to streamline supply chain operations. Many supply chains have also re-designed their processes in order to include the just-in-time concept, to reduce the often high holding costs, improve waste reduction, and provide less capital investment. However, construction projects often face many challenges in conforming to these standards and practices. Inherently, construction projects are prone to high waste levels of materials, and in order to reduce these to manageable levels, lean processes have been implemented to support the completion of a project. This combination of these concepts has shown to negatively affect the completion of projects, specifically in areas of quality, cost, and future operational challenges. Through aspects of data analytics, common practices can be analyzed through aspects of supplier selection, delivery system management, material waste, and cost savings/ increases, as well as effective data mining initiatives of current and historical projects. Initial research has also identified the possible advantage of the Internet of Things (IoT) and other innovative technology in order to effectively monitor and track current and future opportunities for growth and improvement within construction projects. Discipline: Supply Chain Management Faculty Mentor: Dr. Joong Son
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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 | 0.009 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.007 |
| Science and technology studies | 0.003 | 0.005 |
| Scholarly communication | 0.012 | 0.010 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.020 | 0.004 |
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".