Identifying and Assessing the Risks in the Supply Chain
Bibliographic record
Abstract
Ranking supply chain risks is as an important part of supply chain risk management especially when the numbers of risk factors are increased. In this study, the comprehensive structure of the main risks are prepared in three levels of the supply chain (3 total sets, 7 sub-categories and 34 categories and 300 subcategories) initially and then these risks are evaluated and ranked in the steel supply chain in Esfahan Steel Complex as a case study. For this purpose, group decision-making methods and means of collection and aggregation experts and linear assignment method is used as a method of Multiple Decision Making to determine the ranking of risks. Ranking indexes were divided in two primary and secondary categories. The primary index is based on likelihood and impact of risks on the main objectives (schedule, cost, quality and scope) of supply chain activities with different weights. The second category of indicators includesocio-economic effects, environmental impacts, near the time of risk, exposure to risk, the confidence level of the estimates and the management of risk, risk reduction and risk discovering. Risks are ranked better using linear assignment method and according to various indices and are ranked more realistic. Based on the results, risks related to procurement and supplier are identified and introduced as the most critical risks.
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 | 0.004 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 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".