Identification of supply chain risks in the tobacco products industry in Pasuruan Regency using the supply chain operations reference (SCOR) and house of risk (HoR) model approaches
Bibliographic record
Abstract
Introduction: This research aims to identify types of risks and sources of risks, analyze risk priority levels, and formulate mitigation strategies for the tobacco products industry supply chain in Pasuruan Regency. Methods: This research was conducted at the tobacco products industry PT. XZ in Pasuruan Regency. Respondents focused on company managers (focal firms), namely product development, raw material management, research and development pilot plant and tobacco clue management who knew about supply chain risks. Supply chain network integration uses a snowball method approach. Data collection methods use primary and secondary data. At the interview stage, the Delphi method was used to identify risk events and risk sources. Furthermore, the results of the interview were identified using the Supply Chain Operation Reverence (SCOR) and House of Risk (HoR) models. Results: There are 39 risk events and risk agents in the IHT in Pasuruan Regency. There are eight risk priority levels for tobacco supply chain risk agents using the SCOR approach and the Pareto diagram, namely workers not paying attention, problems with machines, high rainfall/high rainfall, production machines need to be repaired, workers' skills are not good, production machines are old, handling bad goods, and errors in the machine. Conclusion: The results of the IHT supply chain risk mitigation strategy formulation are tightening the use of work SOPs, checking production machines regularly, strengthening information between agents and factories, checking machines every 4 hours, conducting worker training, using Google Maps technology in delivering goods, cleaning machines, maintaining product quality, drying/air-drying tobacco leaves, improving quality control during transactions with farmers, and increasing coordination with the Government.
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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 | 0.004 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
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".