Risk and Supply Chain Mitigation Analysis Using House of Risk Method and Analytical Network Process (A Case Study on Palm Oil Company)
Bibliographic record
Abstract
This research studied PT XYZ, a company engaged in the palm oil industry which has eleven subsidiaries spread across five provinces in Indonesia, namely North Kalimantan, West Kalimantan, East Kalimantan, Central Kalimantan, and South Sumatra. The research focused on analyzing supply chain risks in PT A, a subsidiary of PT XYZ.. The objective was to find out and reduce unexpected costs that the company may experience caused by the risks in supply chain. Furthermore, the aim was to determine priority of risk agents and risk mitigation actions. The research method was a mixed methods, which combined both qualitative and quantitative analysis to answer the research questions. Data analysis procedure involved Supply Chain Operations Reference (SCOR), House of Risk (HOR) 1 and Analytic Network Process (ANP). The SCOR method was used for mapping supply chain activities, the HOR 1 was to determine the priority of the risk agent, and the ANP was to determine the priority of mitigation actions. The results show that there are 36 risk events and 35 risk agents. 19 risk agents are categorized as priority risks and 11 preventive actions are proposed to be implemented by PT XYZ. The research suggests that the company implement mitigation actions according to priority in accordance with the research results, for example, by improving the condition of the main garden road.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".