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Transformasi Kinerja Kontraktor melalui Inovasi Rantai Pasok pada Proyek Pengendalian Banjir dan Pembangunan Embung di Kabupaten Dharmasraya

2025· article· en· W4408088310 on OpenAlexaff
Idris Sardi, Nasfryzal Carlo, Riki Adriadi

Bibliographic record

VenueJurnal Talenta Sipil · 2025
Typearticle
Languageen
FieldComputer Science
TopicMultimedia Learning Systems
Canadian institutionsEncana (Canada)
Fundersnot available
KeywordsBusiness administrationBusiness

Abstract

fetched live from OpenAlex

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.

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 imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.026
Threshold uncertainty score0.087

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0050.002
Open science0.0010.002
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0260.005

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.

Opus teacher head0.012
GPT teacher head0.256
Teacher spread0.244 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2025
Admission routes1
Has abstractyes

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