Perancangan dan Penerapan Hoshin Kanri di PT. Sinar Biru Cemerlang Probolinggo
Bibliographic record
Abstract
Perkembangan industri powerplant di wilayah Jawa Bali telah mengalami \nkemajuan yang pesat seiring dengan meningkatnya kebutuhan pasokan listrik di pulau \nJawa dan Bali. Perkembangan industri powerplant ini memicu perkembangan perusahaan \nyang menyediakan supply kebutuhan industri powerplant atau perusahaan yang bergerak \ndibidang general contractor dan supplier bagi para industri powerplant. \nSelama ini PT. SBC belum memiliki panduan kerja yang jelas dan manajemen \nperusahaan yang selama ini diterapkan kurang baik. Hal ini yang membuat inisiatif para \ndireksi PT. SBC untuk melakukan perbaikan manajemen dengan menerapkan manajemen \nHoshin Kanri di PT. SBC. Hoshin Kanri adalah sebuah metode perbaikan manajemen \nyang menggabungkan antara policy management (manajemen kebijakan) dengan daily \nmanagement (manajemen harian) dan menggunakan Siklus PDCA sebagai alat \nmonitoring kegiatan pelaksanaan. Tujuan dari manajemen Hoshin Kanri adalah perbaikan \nmanajemen yang berkesinambungan dan terarah dengan mengedepankan keterlibatan \nsemua bagian di perusahaan, mulai dari pihak manajemen hingga pekerja (Cross \nFunctional Management).
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.127 | 0.044 |
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".