Challenges and Strategies in Halal Supply Chain Management for MSEs in West Sumatra: A Participatory Action Research Study
Bibliographic record
Abstract
The study delves into the complexities of managing halal supply chains, specifically focusing on enhancing the quality and safety of halal food produced by Micro and Small Enterprises (MSEs) in West Sumatra.Utilizing the Participatory Action Research (PAR) approach, researchers actively collaborate with MSEs to develop halal processed food.Seventeen MSE communities involved in halal processed food production were engaged in this research.This methodology includes problem identification, planning, data collection, analysis, actions, and reflective evaluation.The primary findings spotlight the challenges and strategies in halal supply chain management that impact the quality and safety of halal food.These challenges span raw material procurement, production and processing processes, warehousing and distribution, product preparation and presentation, and monitoring and reporting.The study suggests that MSE owners can potentially enhance the quality and safety standards of their halal food products.This research offers practical guidance for MSEs engaged in processed food production to improve food quality and safety within their halal supply chain management practices.The recommendations include fostering halal awareness and education, establishing effective supplier relationship management, embracing innovation in halal supply chain technology, and ensuring compliance with halal regulations and certification.
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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 | 0.006 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.003 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.005 |
| 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".