Analysis of Compliance and Supply Chain Security Risks Based on ISO 28001 in a Logistic Service Provider in Indonesia
Bibliographic record
Abstract
Risk of goods and security incidents, such as theft, boycott, smuggling and terrorism are likely to occur in a shipping process, therefore risk controls are needed to reduce the adverse effects. A research on the supply chain security risk management based on ISO 28001 security supply chain is conducted to overcome such problems. The purpose of this research is to analyse compliance & supply chain security risks and propose a mitigation based on ISO 28001 in a logistic service provider in Indonesia. A gap analysis is conducted to assess the compliance of security performance in seven areas, i.e. supply chain security management, security plans, asset security, personnel security, information security, security of goods & conveyance and transportation units closed cargo. The result of the assessment showed that a compliance level of above 75% indicates that the company is ready to implement an ISO 28001. The risk mitigation plan is proposed based on Failure mode effect analysis (FMEA) which calculates the Risk Priority Number (RPN). The RPN value indicates the level of risk where the higher the value, the more critical the risk and become the priority to handle. The mitigation proposed for managing risk are reducing, sharing and avoiding.
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 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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".