Performance Measurement and Ranking Organization’s Suppliers Based on Risk Factors Using a Hybrid Approach of FMEA and Multi-Criteria Decision Making Techniques: A Case Study
Bibliographic record
Abstract
Risk occurance in the supply chain is unavoidable. Basically, a great portion of these risks comes from suppliers. Timely identification of the risks and using appropriate preventive actions to reduce the probability and impact of their occurrence play a significant role in increasing organizational efficiency, improving product quality and satisfying customers. Developing a systematic and efficient mechanism is a prerequisite for properly identifying and assessing the supply chain risks and making correct decision. Failure Modes and Effects Analysis (FMEA) is a known engineering technique and risk assessment tool to define, identify, and eliminate potential failures and errors in the products, processes, projects, and services. In this paper, FMEA approach is combined with multi-criteria decision-making techniques to make a systematic mechanism for assessing supply chain risks and prioritizing candidate flour suppliers in Sahar bread industrial group. In the proposed model, Analytic Hierarchy Process (AHP) is used to determine the risk’s weights and VIKOR method is applied for assessing and ranking the suppliers. Results shew that among the identified risks, “cost risk group” with the weight "0.43" is the most important. Therefor the company officials have to adopt appropriate policies, carefully, to deal with this risk. Moreover, final evaluation of flour suppliers in the company indicates that according to all criteria, the fourth supplier achieves the highest priority and it is selected as the most qualified flour supplier for the company.
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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 | 0.005 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| 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".