Operational supply risks of halal food manufacturer: A mitigation approach to supply chain risks
Bibliographic record
Abstract
This study aims to investigate the strategies employed by halal food manufacturers in Malaysia to mitigate operational supply risks. This study selected a sample of 369 respondents using a simple random sampling method to participate in the main survey. The collected dataset was analysed using covariance-based software (AMOS-SEM)) to test the study hypotheses. The findings of this research highlight that halal food manufacturers in Malaysia proactively adopt measures to manage operational supply risks from suppliers. Notably, they utilize behaviour-based and buffer-based strategies to effectively minimize the impact of these risks. This study focused on supply-related risks. To secure the integrity of halal, the firms must address demand-related risks and governmental and organizational risks to ensure the halalness of halal products. Therefore, it is crucial to consider risk management for all parts of the supply chain to guarantee the halal compliance of food products. The study highlights halal firms' need for behaviour-based and buffer-based risk management strategies to mitigate price, quality, and delivery risks while ensuring brand reputation and consumer trust through collaboration, information sharing, and supplier performance evaluation. This study presents a comprehensive analysis of the operational supply risks faced by halal food manufacturers, offering insights into the unique challenges and vulnerabilities within the halal food supply chain. By specifically focusing on Malaysia, this research contributes to the limited existing literature in this specific context, further enriching our knowledge in this field.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.000 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".