Transformasi Kinerja Kontraktor melalui Inovasi Rantai Pasok pada Proyek Pengendalian Banjir dan Pembangunan Embung di Kabupaten Dharmasraya
Bibliographic record
Abstract
Infrastructure projects require coordination and high efficiency in implementation. Innovation in supply chain management was identified as one of the key factors to improve contractor performance, which includes time, cost efficiency, and quality of work output. The dam construction project and flood control facilities/infrastructure in Dharmasraya district has recently been seen to be quite active, but nevertheless, the problem of delay in completing work seems to be a coloring in the reports of these projects. This is due to the lack of planning and material management from the implementing contractor. This is quite concrete evidence that there is a problem with the performance of the contractor executing the project. The purpose of this study is to identify the dominant factors and factors that affect the construction supply chain in the Flood Control Facility Development and Infrastructure project in Dharmasraya district, and determine the relationship between the supply chain (factor X) and contractor performance. This study uses a quantitative method by distributing questionnaires to respondents. The selected respondents are parties related to the Batanghari Hilir flood control infrastructure development project in Dharmasraya district for the 2019-2022 fiscal year. The results of this study are found to be 6 factors that affect the construction supply chain in the Embung Development project and flood control facilities/infrastructure in Dharmasraya district. The 6 factors are material factors, procedural factors and price changes, financial factors, process factors and work plans, work implementation factors, and procurement and payment factors. The SCM factor that has the most dominant influence is the material factor. The practical recommendations resulting from this study can be used by contractors and other stakeholders to optimize project performance through effective supply chain innovation.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.026 | 0.005 |
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".